@article {pmid36750362, year = {2023}, author = {Esparza-Iaizzo, M and Vigué-Guix, I and Ruzzoli, M and Torralba, M and Soto-Faraco, S}, title = {Long-range alpha-synchronisation as control signal for BCI: A feasibility study.}, journal = {eNeuro}, volume = {}, number = {}, pages = {}, doi = {10.1523/ENEURO.0203-22.2023}, pmid = {36750362}, issn = {2373-2822}, abstract = {Shifts in spatial attention are associated with variations in alpha-band (α, 8-14 Hz) activity, specifically in inter-hemispheric imbalance. The underlying mechanism is attributed to local α-synchronisation, which regulates local inhibition of neural excitability, and fronto-parietal synchronisation reflecting long-range communication. The direction-specific nature of this neural correlate brings forward its potential as a control signal in brain-computer interfaces (BCI). In the present study, we explored whether long-range α-synchronisation presents lateralised patterns dependent on voluntary attention orienting and whether these neural patterns can be picked up at a single-trial level to provide a control signal for active BCI. We collected electroencephalography (EEG) data from a cohort of healthy adults (n = 10) while performing a covert visuospatial attention (CVSA) task. The data shows a lateralised pattern of α-band phase coupling between frontal and parieto-occipital regions after target presentation, replicating previous findings. This pattern, however, was not evident during the cue-to-target orienting interval, the ideal time window for BCI. Furthermore, decoding the direction of attention trial-by-trial from cue-locked synchronisation with support vector machines (SVM) was at chance-level. The present findings suggest EEG may not be capable of detecting long-range α-synchronisation in attentional orienting on a single-trial basis and, thus, highlight the limitations of this metric as a reliable signal for BCI control.SIGNIFICANCE STATEMENTCognitive neuroscience advances should ideally have a real-world impact, with an obvious avenue for transference being BCI applications. The hope is to faithfully translate user-generated brain endogenous states into control signals to actuate devices. A paramount challenge for transfer is to move from group-level, multi-trial average approaches to single-trial level. Here, we evaluated the feasibility of single-trial estimation of phase synchrony across distant brain regions. Although many studies link attention to long-range synchrony modulation, this metric has never been used to control BCI. We present a first attempt of a synchrony-based BCI that, albeit unsuccessful, should help break new ground to map endogenous attention shifts to real-time control of brain-computer actuated systems.}, }
@article {pmid36750151, year = {2023}, author = {Kikkert, S and Sonar, HA and Freund, P and Paik, J and Wenderoth, N}, title = {Hand and face somatotopy shown using MRI-safe vibrotactile stimulation with a novel Soft Pneumatic Actuator (SPA)-Skin interface.}, journal = {NeuroImage}, volume = {}, number = {}, pages = {119932}, doi = {10.1016/j.neuroimage.2023.119932}, pmid = {36750151}, issn = {1095-9572}, abstract = {The exact somatotopy of the human facial representation in the primary somatosensory cortex (S1) remains debated. One reason that progress has been hampered is due to the methodological challenge of how to apply automated vibrotactile stimuli to face areas in a manner that is: 1) reliable despite differences in the curvatures of face locations; and 2) MR-compatible and free of MR-interference artefacts when applied in the MR head-coil. Here we overcome this challenge by using soft pneumatic actuator (SPA) technology. SPAs are made of a soft silicon material and can be in- or deflated by means of airflow, have a small diameter, and are flexible in structure, enabling good skin contact even on curved body surfaces (as on the face). To validate our approach, we first mapped the well-characterised S1 finger layout using this novel device and confirmed that tactile stimulation of the fingers elicited characteristic somatotopic finger activations in S1. We then used the device to automatically and systematically deliver somatosensory stimulation to different face locations. We found that the forehead representation was least distant from the representation of the hand. Within the face representation, we found that the lip representation is most distant from the forehead representation, with the chin represented in between. Together, our results demonstrate that this novel MR compatible device produces robust and clear somatotopic representational patterns using vibrotactile stimulation through SPA-technology.}, }
@article {pmid36749989, year = {2023}, author = {Massaeli, F and Bagheri, M and Power, SD}, title = {EEG-based detection of modality-specific visual and auditory sensory processing.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acb9be}, pmid = {36749989}, issn = {1741-2552}, abstract = {OBJECTIVE: A passive brain-computer interface (pBCI) is a system that enhances a human-machine interaction by monitoring the mental state of the user and, based on this implicit information, making appropriate modifications to the interaction. Key to the development of such a system is the ability to reliably detect the mental state of interest via neural signals. Many different mental states have been investigated, including fatigue, attention and various emotions, however one of the most commonly studied states is mental workload, i.e., the amount of attentional resources required to perform a task. The emphasis of mental workload studies to date has been almost exclusively on detecting and predicting the "level" of cognitive resources required (e.g., high vs. low), but we argue that having information regarding the specific "type" of resources (e.g., visual or auditory) would allow the pBCI to apply more suitable adaption techniques than would be possible knowing just the overall workload level.<br><br>APPROACH: 15 participants performed carefully designed visual and auditory tasks while EEG data was recorded. The tasks were designed to be as similar as possible to one another except for the type of attentional resources required. The tasks were performed at two different levels of demand. Using traditional machine learning algorithms, we investigated, firstly, if EEG can be used to distinguish between auditory and visual processing tasks and, secondly, what effect level of sensory processing demand has on the ability to distinguish between auditory and visual processing tasks.<br><br>MAIN RESULTS: The results show that at the high level of demand, the auditory vs. visual processing tasks could be distinguished with an accuracy of 77.1% on average. However, in the low demand condition in this experiment, the tasks were not classified with an accuracy exceeding chance.<br><br>SIGNIFICANCE: These results support the feasibility of developing a pBCI for detecting not only the level, but also the type, of attentional resources being required of the user at a given time. Further research is required to determine if there is a threshold of demand under which the type of sensory processing cannot be detected, but even if that is the case, these results are still promising since it is the high end of demand that is of most concern in safety critical scenarios. Such a BCI could help improve safety in high risk occupations by initiating the most effective and efficient possible adaptation strategies when high workload conditions are detected.}, }
@article {pmid36749645, year = {2023}, author = {Ziemba, AM and Woodson, MCC and Funnell, JL and Wich, D and Balouch, B and Rende, D and Amato, DN and Bao, J and Oprea, I and Cao, D and Bajalo, N and Ereifej, ES and Capadona, JR and Palermo, EF and Gilbert, RJ}, title = {Development of a Slow-Degrading Polymerized Curcumin Coating for Intracortical Microelectrodes.}, journal = {ACS applied bio materials}, volume = {}, number = {}, pages = {}, doi = {10.1021/acsabm.2c00969}, pmid = {36749645}, issn = {2576-6422}, abstract = {Intracortical microelectrodes are used with brain-computer interfaces to restore lost limb function following nervous system injury. While promising, recording ability of intracortical microelectrodes diminishes over time due, in part, to neuroinflammation. As curcumin has demonstrated neuroprotection through anti-inflammatory activity, we fabricated a 300 nm-thick intracortical microelectrode coating consisting of a polyurethane copolymer of curcumin and polyethylene glycol (PEG), denoted as poly(curcumin-PEG1000 carbamate) (PCPC). The uniform PCPC coating reduced silicon wafer hardness by two orders of magnitude and readily absorbed water within minutes, demonstrating that the coating is soft and hydrophilic in nature. Using an in vitro release model, curcumin eluted from the PCPC coating into the supernatant over 1 week; the majority of the coating was intact after an 8-week incubation in buffer, demonstrating potential for longer term curcumin release and softness. Assessing the efficacy of PCPC within a rat intracortical microelectrode model in vivo, there were no significant differences in tissue inflammation, scarring, neuron viability, and myelin damage between the uncoated and PCPC-coated probes. As the first study to implant nonfunctional probes with a polymerized curcumin coating, we have demonstrated the biocompatibility of a PCPC coating and presented a starting point in the design of poly(pro-curcumin) polymers as coating materials for intracortical electrodes.}, }
@article {pmid36745927, year = {2023}, author = {Li, B and Zhang, S and Hu, Y and Lin, Y and Gao, X}, title = {Assembling global and local spatial-temporal filters to extract discriminant information of EEG in RSVP task.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acb96f}, pmid = {36745927}, issn = {1741-2552}, abstract = {OBJECTIVE: Brain-computer interface (BCI) system has developed rapidly in the past decade. And rapid serial visual presentation (RSVP) is an important BCI paradigm to detect the targets in high-speed image streams. For decoding EEG in RSVP task, the ensemble-model methods have better performance than the single-model ones.<br><br>APPROACH: This study proposed a method based on ensemble learning to extract discriminant information of EEG. An extreme gradient boosting (XGB) framework was utilized to sequentially generate the sub models, including one global spatial-temporal filter and a group of local ones. EEG was reconstructed in 3-dimensional form (2-D electrode space &#xd7; time series) to learn the spatial-temporal features from real local cortical space.<br><br>MAIN RESULTS: A benchmark RSVP EEG dataset was utilized to evaluate the performance of the proposed method, where EEG data of 63 subjects were analyzed. Compared with several state-of-the-art methods, the spatial-temporal patterns of proposed method were more consistent with P300, and the proposed method can provide significantly better classification performance.<br><br>SIGNIFICANCE: The ensemble model in this study was end-to-end optimized, which can avoid error accumulation. The sub models optimized by gradient boosting theory can extract discriminant information complementarily and non-redundantly.}, }
@article {pmid36745911, year = {2023}, author = {Adhikary, S and Jain, K and Saha, B and Chowdhury, D}, title = {Optimized EEG Based Mood Detection with Signal Processing and Deep Neural Networks for Brain-Computer Interface.}, journal = {Biomedical physics &amp; engineering express}, volume = {}, number = {}, pages = {}, doi = {10.1088/2057-1976/acb942}, pmid = {36745911}, issn = {2057-1976}, abstract = {Electroencephalogram (EEG) is a very promising and widely implemented procedure to study brain signals and activities by amplifying and measuring the post-synaptical potential arising from electrical impulses produced by neurons and detected by specialized electrodes attached to specific points in the scalp. It can be studied for detecting brain abnormalities, headaches, and other conditions. However, there are limited studies performed to establish a smart decision-making model to identify EEG's relation with the mood of the subject. In this experiment, EEG signals of 28 healthy human subjects have been observed with consent and attempts have been made to study and recognise moods. Savitzky-Golay band-pass filtering and Independent Component Analysis have been used for data filtration. Different neural network algorithms have been implemented to analyze and classify the EEG data based on the mood of the subject. The model is further optimised by the usage of Blackman window-based Fourier Transformation and extracting the most significant frequencies for each electrode. Using these techniques, up to 96.01% detection accuracy has been obtained.}, }
@article {pmid36743394, year = {2022}, author = {Song, M and Huang, Y and Visser, HJ and Romme, J and Liu, YH}, title = {An Energy-Efficient and High-Data-Rate IR-UWB Transmitter for Intracortical Neural Sensing Interfaces.}, journal = {IEEE journal of solid-state circuits}, volume = {57}, number = {12}, pages = {3656-3668}, pmid = {36743394}, issn = {0018-9200}, abstract = {This paper presents an implantable impulse-radio ultra-wideband (IR-UWB) wireless telemetry system for intracortical neural sensing interfaces. A 3-dimensional (3-D) hybrid impulse modulation that comprises phase shift keying (PSK), pulse position modulation (PPM) and pulse amplitude modulation (PAM) is proposed to increase modulation order without significantly increasing the demodulation requirement, thus leading to a high data rate of 1.66 Gbps and an increased air-transmission range. Operating in 6 - 9 GHz UWB band, the presented transmitter (TX) supports the proposed hybrid modulation with a high energy efficiency of 5.8 pJ/bit and modulation quality (EVM< -21 dB). A low-noise injection-locked ring oscillator supports 8-PSK with a phase error of 2.6°. A calibration free delay generator realizes a 4-PPM with only 115 μW and avoids potential cross-modulation between PPM and PSK. A switch-cap power amplifier with an asynchronous pulse-shaping performs 4-PAM with high energy efficiency and linearity. The TX is implemented in 28 nm CMOS technology, occupying 0.155mm[2] core area. The wireless module including a printed monopole antenna has a module area of only 1.05 cm[2]. The transmitter consumes in total 9.7 mW when transmitting -41.3 dBm/MHz output power. The wireless telemetry module has been validated ex-vivo with a 15-mm multi-layer porcine tissue, and achieves a communication (air) distance up to 15 cm, leading to at least 16× improvement in distance-moralized energy efficiency of 45 pJ/bit/meter compared to state-of-the-art.}, }
@article {pmid36741783, year = {2022}, author = {Shibu, CJ and Sreedharan, S and Arun, KM and Kesavadas, C and Sitaram, R}, title = {Explainable artificial intelligence model to predict brain states from fNIRS signals.}, journal = {Frontiers in human neuroscience}, volume = {16}, number = {}, pages = {1029784}, pmid = {36741783}, issn = {1662-5161}, abstract = {Objective: Most Deep Learning (DL) methods for the classification of functional Near-Infrared Spectroscopy (fNIRS) signals do so without explaining which features contribute to the classification of a task or imagery. An explainable artificial intelligence (xAI) system that can decompose the Deep Learning mode's output onto the input variables for fNIRS signals is described here. Approach: We propose an xAI-fNIRS system that consists of a classification module and an explanation module. The classification module consists of two separately trained sliding window-based classifiers, namely, (i) 1-D Convolutional Neural Network (CNN); and (ii) Long Short-Term Memory (LSTM). The explanation module uses SHAP (SHapley Additive exPlanations) to explain the CNN model's output in terms of the model's input. Main results: We observed that the classification module was able to classify two types of datasets: (a) Motor task (MT), acquired from three subjects; and (b) Motor imagery (MI), acquired from 29 subjects, with an accuracy of over 96% for both CNN and LSTM models. The explanation module was able to identify the channels contributing the most to the classification of MI or MT and therefore identify the channel locations and whether they correspond to oxy- or deoxy-hemoglobin levels in those locations. Significance: The xAI-fNIRS system can distinguish between the brain states related to overt and covert motor imagery from fNIRS signals with high classification accuracy and is able to explain the signal features that discriminate between the brain states of interest.}, }
@article {pmid36741671, year = {2023}, author = {Almosallam, W and Aljoujou, AA and Ayoubi, HR and Alzoubi, H}, title = {Evaluation of the Effect of Antihypertensive Drugs on the Values of Dental Pulp Oxygen Saturation in Hypertension Patients: A Case-Control Study.}, journal = {Cureus}, volume = {15}, number = {1}, pages = {e33245}, pmid = {36741671}, issn = {2168-8184}, abstract = {Purpose This study aimed to know about the positive or negative effect of antihypertensive drugs of different groups on the values of dental pulp oxygen saturation in hypertension patients. Materials and Methods A case-control study to evaluate the impact of the antihypertensive drugs on the values of dental pulp oxygen saturation in hypertension patients. The studied sample consisted of 40 participants, and they were distributed into two groups: Group I (n=20): Hypertension patients treated with antihypertensive drugs, and Group II (n=20): Healthy participants. A finger pulse oximeter was recorded after a rest period of 15 minutes by BCI® Advisor® vital signs monitor. The patient was then asked to use a chlorhexidine digluconate mouth rinse for five minutes, and the two dental pulp pulse oximeters for the central upper incisors were also recorded for all participants. Data were analyzed using the Mann-Whitney U test. Results The results showed that there was no significant difference between the finger pulse oximeters of the two studied groups (P-value = 0.421). The two dental pulp oxygen saturation was higher than the control group with statistically significant (P-value = 0.043, P-value = 0.002). Conclusions Within the limitation of this study, it can be concluded that antihypertensive drugs increase the dental pulp oxygen saturation in patients with hypertension who are treated with antihypertensive drugs, and thus there is a positive effect of these drugs in stimulating the dental pulp.}, }
@article {pmid36740976, year = {2023}, author = {Yao, S and Shi, S and Zhou, Q and Wang, J and Du, X and Takahata, T and Roe, AW}, title = {Functional topography of pulvinar-visual cortex networks in macaques revealed by INS-fMRI.}, journal = {The Journal of comparative neurology}, volume = {}, number = {}, pages = {}, doi = {10.1002/cne.25456}, pmid = {36740976}, issn = {1096-9861}, abstract = {The pulvinar in the macaque monkey contains three divisions: the medial pulvinar (PM), the lateral pulvinar (PL), and the inferior pulvinar (PI). Anatomical studies have shown that connections of PM are preferentially distributed to higher association areas, those of PL are biased toward the ventral visual pathway, and those of PI are biased with the dorsal visual pathway. To study functional connections of the pulvinar at mesoscale, we used a novel method called INS-fMRI (infrared neural stimulation and functional magnetic resonance imaging). This method permits studies and comparisons of multiple pulvinar networks within single animals. As previously revealed, stimulations of different sites in PL and PI produced topographically organized focal activations in visual areas V1, V2, and V3. In contrast, PM stimulation elicited little or diffuse response. The relative activations of areas V1, V2, V3A, V3d, V3v, V4, MT, and MST revealed that connections of PL are biased to ventral pathway areas, and those of PI are biased to dorsal areas. Different statistical values of activated blood-oxygen-level-dependent responses produced the same center of activation, indicating stability of connectivity; it also suggests possible dynamics of broad to focal responses from single stimulation sites. These results demonstrate that infrared neural stimulation-induced connectivity is largely consistent with previous anatomical connectivity studies, thereby demonstrating validity of our novel method. In addition, it suggests additional interpretations of functional connectivity to complement anatomical studies.}, }
@article {pmid36742108, year = {2021}, author = {Jee, S}, title = {Brain Oscillations and Their Implications for Neurorehabilitation.}, journal = {Brain &amp; NeuroRehabilitation}, volume = {14}, number = {1}, pages = {e7}, pmid = {36742108}, issn = {2383-9910}, abstract = {Neural oscillation is rhythmic or repetitive neural activities, which can be observed at all levels of the central nervous system (CNS). The large-scale oscillations measured by electroencephalography have long been used in clinical practice and may have a potential for the usage in neurorehabilitation for people with various CNS disorders. The recent advancement of computational neuroscience has opened up new opportunities to explore clinical application of the results of neural oscillatory activity analysis to evaluation and diagnosis; monitoring the rehab progress; prognostication; and personalized rehabilitation planning in neurorehabilitation. In addition, neural oscillation is catching more attention to its role as a target of noninvasive neuromodulation in neurological disorders.}, }
@article {pmid36738734, year = {2023}, author = {Cui, Q and Bi, H and Lv, Z and Wu, Q and Hua, J and Gu, B and Huo, C and Tang, M and Chen, Y and Chen, C and Chen, S and Zhang, X and Wu, Z and Lao, Z and Sheng, N and Shen, C and Zhang, Y and Wu, ZY and Jin, Z and Yang, P and Liu, H and Li, J and Bai, G}, title = {Diverse CMT2 neuropathies are linked to aberrant G3BP interactions in stress granules.}, journal = {Cell}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.cell.2022.12.046}, pmid = {36738734}, issn = {1097-4172}, abstract = {Complex diseases often involve the interplay between genetic and environmental factors. Charcot-Marie-Tooth type 2 neuropathies (CMT2) are a group of genetically heterogeneous disorders, in which similar peripheral neuropathology is inexplicably caused by various mutated genes. Their possible molecular links remain elusive. Here, we found that upon environmental stress, many CMT2-causing mutant proteins adopt similar properties by entering stress granules (SGs), where they aberrantly interact with G3BP and integrate into SG pathways. For example, glycyl-tRNA synthetase (GlyRS) is translocated from the cytoplasm into SGs upon stress, where the mutant GlyRS perturbs the G3BP-centric SG network by aberrantly binding to G3BP. This disrupts SG-mediated stress responses, leading to increased stress vulnerability in motoneurons. Disrupting this aberrant interaction rescues SG abnormalities and alleviates motor deficits in CMT2D mice. These findings reveal a stress-dependent molecular link across diverse CMT2 mutants and provide a conceptual framework for understanding genetic heterogeneity in light of environmental stress.}, }
@article {pmid36736668, year = {2023}, author = {Deng, J and Sun, J and Lu, S and Yue, K and Liu, W and Shi, H and Zou, L}, title = {Exploring neural activity in inflammatory bowel diseases using functional connectivity and DKI-fMRI fusion.}, journal = {Behavioural brain research}, volume = {}, number = {}, pages = {114325}, doi = {10.1016/j.bbr.2023.114325}, pmid = {36736668}, issn = {1872-7549}, abstract = {Although MRI has made considerable progress in Inflammatory bowel disease (IBD), most studies have concentrated on data information from a single modality, and a better understanding of the interplay between brain function and structure, as well as appropriate clinical aids to diagnosis, is required. We calculated functional connectivity through fMRI time series using resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion kurtosis imaging (DKI) data from 27 IBD patients and 29 healthy controls. Through the DKI data of each subject, its unique structure map is obtained, and the relevant indicators are projected onto the structure map corresponding to each subject by using the graph Fourier transform in the grasp signal processing (GSP) technology. After the features are optimized, a classical support vector machine is used to classify the features. IBD patients have altered functional connectivity in the default mode network (DMN) and subcortical network (SCN). At the same time, compared with the traditional brain network analysis, in the test of some indicators, the average classification accuracy produced by the framework method is 12.73% higher than that of the traditional analysis method. This paper found that the brain network structure of IBD patients in DMN and SCN has changed. Simultaneously, the application of GSP technology to fuse functional information and structural information is superior to the traditional framework in classification, providing a new perspective for subsequent clinical auxiliary diagnosis.}, }
@article {pmid36736571, year = {2023}, author = {Yan, K and Tao, R and Huang, X and Zhang, E}, title = {Influence of advisees' facial feedback on subsequent advice-giving by advisors: Evidence from the behavioral and neurophysiological approach.}, journal = {Biological psychology}, volume = {}, number = {}, pages = {108506}, doi = {10.1016/j.biopsycho.2023.108506}, pmid = {36736571}, issn = {1873-6246}, abstract = {Previous work has demonstrated the interpersonal implications of advisees' decisions (acceptance or rejection) on advisors' advice-giving behavior in subsequent exchanges. Here, using an ERP technique, we investigated how advisees' facial feedback (smiling, neutral, or frowning) accompanying their decisions (acceptance or rejection) influenced advisors' feedback evaluation from advisees and their advice-giving in subsequent exchanges. Behaviorally, regardless of whether the advice was accepted or rejected, advisors who received smiling-expression feedback would show higher willingness rates in subsequent advice-giving decisions, while advisors who received frowning-expression feedback would show lower willingness rates. On the neural level, in the feedback evaluation stage, the FRN and P3 responses were not sensitive to facial feedback. In contrast, frowning-expression feedback elicited a larger LPC amplitude than neutral- and smiling-expression feedback, regardless of whether the advice was accepted or rejected. In the advice decision stage, advisors who received neutral-expression feedback showed a larger N2 in making decisions than advisors who received frowning-expression feedback only after the advice was rejected. Additionally, Advisors who received smiling- and neutral-expression feedback showed a larger P3 in making decisions than advisors who received frowning-expression feedback only after the advice was accepted. In sum, the current findings extended previous research findings by showing that the effect of advisees' facial expressions on the advisors' advice-giving existed in multiple stages, including both the feedback evaluation stage and the advice decision stage.}, }
@article {pmid36736001, year = {2023}, author = {Mao, J and Qiu, S and Wei, W and He, H}, title = {Cross-modal guiding and reweighting network for multi-modal RSVP-based target detection.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {161}, number = {}, pages = {65-82}, doi = {10.1016/j.neunet.2023.01.009}, pmid = {36736001}, issn = {1879-2782}, abstract = {Rapid Serial Visual Presentation (RSVP) based Brain-Computer Interface (BCI) facilities the high-throughput detection of rare target images by detecting evoked event-related potentials (ERPs). At present, the decoding accuracy of the RSVP-based BCI system limits its practical applications. This study introduces eye movements (gaze and pupil information), referred to as EYE modality, as another useful source of information to combine with EEG-based BCI and forms a novel target detection system to detect target images in RSVP tasks. We performed an RSVP experiment, recorded the EEG signals and eye movements simultaneously during a target detection task, and constructed a multi-modal dataset including 20 subjects. Also, we proposed a cross-modal guiding and fusion network to fully utilize EEG and EYE modalities and fuse them for better RSVP decoding performance. In this network, a two-branch backbone was built to extract features from these two modalities. A Cross-Modal Feature Guiding (CMFG) module was proposed to guide EYE modality features to complement the EEG modality for better feature extraction. A Multi-scale Multi-modal Reweighting (MMR) module was proposed to enhance the multi-modal features by exploring intra- and inter-modal interactions. And, a Dual Activation Fusion (DAF) was proposed to modulate the enhanced multi-modal features for effective fusion. Our proposed network achieved a balanced accuracy of 88.00% (±2.29) on the collected dataset. The ablation studies and visualizations revealed the effectiveness of the proposed modules. This work implies the effectiveness of introducing the EYE modality in RSVP tasks. And, our proposed network is a promising method for RSVP decoding and further improves the performance of RSVP-based target detection systems.}, }
@article {pmid36733372, year = {2023}, author = {Gams, A and Naik, GR}, title = {Editorial: Neurorobotics explores gait movement in the sporting community.}, journal = {Frontiers in neurorobotics}, volume = {17}, number = {}, pages = {1127994}, pmid = {36733372}, issn = {1662-5218}, }
@article {pmid36731812, year = {2023}, author = {Soroush, PZ and Herff, C and Ries, SK and Shih, JJ and Schultz, T and Krusienski, DJ}, title = {The Nested Hierarchy of Overt, Mouthed, and Imagined Speech Activity Evident in Intracranial Recordings.}, journal = {NeuroImage}, volume = {}, number = {}, pages = {119913}, doi = {10.1016/j.neuroimage.2023.119913}, pmid = {36731812}, issn = {1095-9572}, abstract = {Recent studies have demonstrated that it is possible to decode and synthesize various aspects of acoustic speech directly from intracranial measurements of electrophysiological brain activity. In order to continue progressing toward the development of a practical speech neuroprosthesis for the individuals with speech impairments, better understanding and modeling of imagined speech processes are required. The present study uses intracranial brain recordings from participants that performed a speaking task with trials consisting of overt, mouthed, and imagined speech modes, representing various degrees of decreasing behavioral output. Speech activity detection models are constructed using spatial, spectral, and temporal brain activity features, and the features and model performances are characterized and compared across the three degrees of behavioral output. The results indicate the existence of a hierarchy in which the relevant channels for the lower behavioral output modes form nested subsets of the relevant channels from the higher behavioral output modes. This provides important insights for the elusive goal of developing more effective imagined speech decoding models with respect to the better-established overt speech decoding counterparts.}, }
@article {pmid36731770, year = {2023}, author = {Pan, L and Ping, A and Schriver, KE and Roe, AW and Zhu, J and Xu, K}, title = {Infrared neural stimulation in human cerebral cortex.}, journal = {Brain stimulation}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.brs.2023.01.1678}, pmid = {36731770}, issn = {1876-4754}, abstract = {BACKGROUND: Modulation of brain circuits by electrical stimulation has led to exciting and powerful therapies for diseases such as Parkinson's. Because human brain organization is based in mesoscale (millimeter-scale) functional nodes, having a method that can selectively target such nodes could enable more precise, functionally specific stimulation therapies. Infrared Neural Stimulation (INS) is an emerging stimulation technology that stimulates neural tissue via delivery of tiny heat pulses. In nonhuman primates, this optical method provides focal intensity-dependent stimulation of the brain without tissue damage. However, whether INS application to the human central nervous system (CNS) is similarly effective is unknown.<br><br>OBJECTIVE: To examine the effectiveness of INS on human cerebral cortex in intraoperative setting and to evaluate INS damage threshholds.<br><br>METHODS: Five epileptic subjects undergoing standard lobectomy for epilepsy consented to this study. Cortical response to INS was assessed by intrinsic signal optical imaging (OI, a method that detects changes in tissue reflectance due to neuronal activity). A custom integrated INS and OI system was developed specifically for short-duration INS and OI acquisition during surgical procedures. Single pulse trains of INS with intensities from 0.2 to 0.8 J/cm[2] were delivered to the somatosensory cortex and responses were recorded via optical imaging. Following tissue resection, histological analysis was conducted to evaluate damage threshholds.<br><br>RESULTS: As assessed by OI, and similar to results in monkeys, INS induced responses in human cortex were highly focal (millimeter sized) and led to relative suppression of nearby cortical sites. Intensity dependence was observed at both stimulated and functionally connected sites. Histological analysis of INS-stimulated human cortical tissue provided damage threshold estimates.<br><br>CONCLUSION: This is the first study demonstrating application of INS to human CNS and shows feasibility for stimulating single cortical nodes and associated sites and provided INS damage threshold estimates for cortical tissue. Our results suggest that INS is a promising tool for stimulation of functionally selective mesoscale circuits in the human brain, and may lead to advances in the future of precision medicine.}, }
@article {pmid36731636, year = {2023}, author = {Jin, J and Chen, X and Zhang, D and Liang, Z}, title = {Editorial for the Special Issue "Visual Evoked Brain Computer Interface Studies".}, journal = {Journal of neuroscience methods}, volume = {}, number = {}, pages = {109806}, doi = {10.1016/j.jneumeth.2023.109806}, pmid = {36731636}, issn = {1872-678X}, }
@article {pmid36729587, year = {2023}, author = {Rimbert, S and Lelarge, J and Guerci, P and Bidgoli, SJ and Meistelman, C and Cheron, G and Cebolla Alvarez, AM and Schmartz, D}, title = {Detection of Motor Cerebral Activity After Median Nerve Stimulation During General Anesthesia (STIM-MOTANA): Protocol for a Prospective Interventional Study.}, journal = {JMIR research protocols}, volume = {12}, number = {}, pages = {e43870}, doi = {10.2196/43870}, pmid = {36729587}, issn = {1929-0748}, abstract = {BACKGROUND: Accidental awareness during general anesthesia (AAGA) is defined as an unexpected awareness of the patient during general anesthesia. This phenomenon occurs in 1%-2% of high-risk practice patients and can cause physical suffering and psychological after-effects, called posttraumatic stress disorder. In fact, no monitoring techniques are satisfactory enough to effectively prevent AAGA; therefore, new alternatives are needed. Because the first reflex for a patient during an AAGA is to move, but cannot do so because of the neuromuscular blockers, we believe that it is possible to design a brain-computer interface (BCI) based on the detection of movement intention to warn the anesthetist. To do this, we propose to describe and detect the changes in terms of motor cortex oscillations during general anesthesia with propofol, while a median nerve stimulation is performed. We believe that our results could enable the design of a BCI based on median nerve stimulation, which could prevent AAGA.<br><br>OBJECTIVE: To our knowledge, no published studies have investigated the detection of electroencephalographic (EEG) patterns in relation to peripheral nerve stimulation over the sensorimotor cortex during general anesthesia. The main objective of this study is to describe the changes in terms of event-related desynchronization and event-related synchronization modulations, in the EEG signal over the motor cortex during general anesthesia with propofol while a median nerve stimulation is performed.<br><br>METHODS: STIM-MOTANA is an interventional and prospective study conducted with patients scheduled for surgery under general anesthesia, involving EEG measurements and median nerve stimulation at two different times: (1) when the patient is awake before surgery (2) and under general anesthesia. A total of 30 patients will receive surgery under complete intravenous anesthesia with a target-controlled infusion pump of propofol.<br><br>RESULTS: The changes in event-related desynchronization and event-related synchronization during median nerve stimulation according to the various propofol concentrations for 30 patients will be analyzed. In addition, we will apply 4 different offline machine learning algorithms to detect the median nerve stimulation at the cerebral level. Recruitment began in December 2022. Data collection is expected to conclude in June 2024.<br><br>CONCLUSIONS: STIM-MOTANA will be the first protocol to investigate median nerve stimulation cerebral motor effect during general anesthesia for the detection of intraoperative awareness. Based on strong practical and theoretical scientific reasoning from our previous studies, our innovative median nerve stimulation-based BCI would provide a way to detect intraoperative awareness during general anesthesia.<br><br>TRIAL REGISTRATION: Clinicaltrials.gov NCT05272202; https://clinicaltrials.gov/ct2/show/NCT05272202.<br><br>PRR1-10.2196/43870.}, }
@article {pmid36729246, year = {2023}, author = {Knopf, S and Frahm, N and M Pfotenhauer, S}, title = {How Neurotech Start-Ups Envision Ethical Futures: Demarcation, Deferral, Delegation.}, journal = {Science and engineering ethics}, volume = {29}, number = {1}, pages = {4}, pmid = {36729246}, issn = {1471-5546}, abstract = {Like many ethics debates surrounding emerging technologies, neuroethics is increasingly concerned with the private sector. Here, entrepreneurial visions and claims of how neurotechnology innovation will revolutionize society-from brain-computer-interfaces to neural enhancement and cognitive phenotyping-are confronted with public and policy concerns about the risks and ethical challenges related to such innovations. But while neuroethics frameworks have a longer track record in public sector research such as the U.S. BRAIN Initiative, much less is known about how businesses-and especially start-ups-address ethics in tech development. In this paper, we investigate how actors in the field frame and enact ethics as part of their innovative R&amp;D processes and business models. Drawing on an empirical case study on direct-to-consumer (DTC) neurotechnology start-ups, we find that actors engage in careful boundary-work to anticipate and address public critique of their technologies, which allows them to delineate a manageable scope of their ethics integration. In particular, boundaries are drawn around four areas: the technology's actual capability, purpose, safety and evidence-base. By drawing such lines of demarcation, we suggest that start-ups make their visions of ethical neurotechnology in society more acceptable, plausible and desirable, favoring their innovations while at the same time assigning discrete responsibilities for ethics. These visions establish a link from the present into the future, mobilizing the latter as promissory place where a technology's benefits will materialize and to which certain ethical issues can be deferred. In turn, the present is constructed as a moment in which ethical engagement could be delegated to permissive regulatory standards and scientific authority. Our empirical tracing of the construction of 'ethical realities' in and by start-ups offers new inroads for ethics research and governance in tech industries beyond neurotechnology.}, }
@article {pmid36726940, year = {2023}, author = {Liu, Y and Xu, S and Yang, Y and Zhang, K and He, E and Liang, W and Luo, J and Wu, Y and Cai, X}, title = {Nanomaterial-based microelectrode arrays for in vitro bidirectional brain-computer interfaces: a review.}, journal = {Microsystems &amp; nanoengineering}, volume = {9}, number = {}, pages = {13}, pmid = {36726940}, issn = {2055-7434}, abstract = {A bidirectional in vitro brain-computer interface (BCI) directly connects isolated brain cells with the surrounding environment, reads neural signals and inputs modulatory instructions. As a noninvasive BCI, it has clear advantages in understanding and exploiting advanced brain function due to the simplified structure and high controllability of ex vivo neural networks. However, the core of ex vivo BCIs, microelectrode arrays (MEAs), urgently need improvements in the strength of signal detection, precision of neural modulation and biocompatibility. Notably, nanomaterial-based MEAs cater to all the requirements by converging the multilevel neural signals and simultaneously applying stimuli at an excellent spatiotemporal resolution, as well as supporting long-term cultivation of neurons. This is enabled by the advantageous electrochemical characteristics of nanomaterials, such as their active atomic reactivity and outstanding charge conduction efficiency, improving the performance of MEAs. Here, we review the fabrication of nanomaterial-based MEAs applied to bidirectional in vitro BCIs from an interdisciplinary perspective. We also consider the decoding and coding of neural activity through the interface and highlight the various usages of MEAs coupled with the dissociated neural cultures to benefit future developments of BCIs.}, }
@article {pmid36726556, year = {2022}, author = {Hossain, KM and Islam, MA and Hossain, S and Nijholt, A and Ahad, MAR}, title = {Status of deep learning for EEG-based brain-computer interface applications.}, journal = {Frontiers in computational neuroscience}, volume = {16}, number = {}, pages = {1006763}, pmid = {36726556}, issn = {1662-5188}, abstract = {In the previous decade, breakthroughs in the central nervous system bioinformatics and computational innovation have prompted significant developments in brain-computer interface (BCI), elevating it to the forefront of applied science and research. BCI revitalization enables neurorehabilitation strategies for physically disabled patients (e.g., disabled patients and hemiplegia) and patients with brain injury (e.g., patients with stroke). Different methods have been developed for electroencephalogram (EEG)-based BCI applications. Due to the lack of a large set of EEG data, methods using matrix factorization and machine learning were the most popular. However, things have changed recently because a number of large, high-quality EEG datasets are now being made public and used in deep learning-based BCI applications. On the other hand, deep learning is demonstrating great prospects for solving complex relevant tasks such as motor imagery classification, epileptic seizure detection, and driver attention recognition using EEG data. Researchers are doing a lot of work on deep learning-based approaches in the BCI field right now. Moreover, there is a great demand for a study that emphasizes only deep learning models for EEG-based BCI applications. Therefore, we introduce this study to the recent proposed deep learning-based approaches in BCI using EEG data (from 2017 to 2022). The main differences, such as merits, drawbacks, and applications are introduced. Furthermore, we point out current challenges and the directions for future studies. We argue that this review study will help the EEG research community in their future research.}, }
@article {pmid36723288, year = {2023}, author = {Yang, T and Wang, SC and Ye, L and Maimaitiyiming, Y and Naranmandura, H}, title = {Targeting Viral Proteins for Restraining SARS-CoV-2: Focusing Lens on Viral Proteins Beyond Spike for Discovering New Drug Targets.}, journal = {Expert opinion on drug discovery}, volume = {}, number = {}, pages = {}, doi = {10.1080/17460441.2023.2175812}, pmid = {36723288}, issn = {1746-045X}, abstract = {INTRODUCTION: Emergence of highly infectious SARS-CoV-2 variants are reducing protection provided by the current vaccines, requiring constant updates in antiviral approaches. As a member of the Coronaviridae family, SARS-CoV-2 encodes four structural and sixteen nonstructural proteins which participate in various aspects of the viral life cycle including genome replication and transcription, virion assembly, release and entry into cells, as well as compromising host cellular defenses. As alien proteins to host cells, many viral proteins represent potential targets for combating the SARS-CoV-2.<br><br>AREAS COVERED: Based on literature from PubMed and Web of Science databases, the authors summarize the typical characteristics of SARS-CoV-2 from the whole viral particle to the individual viral proteins as well as their corresponding functions in virus life cycle. The authors also discuss the potential and emerging targeted interventions to curb virus replication and spread in detail to provide unique insights into the rapidly spreading SARS-CoV-2 infection and countermeasures against it.<br><br>EXPERT OPINION: Our comprehensive analysis highlights the rationale and need to focus on non-spike viral proteins that are less mutated but has important functions. Examples of this include: structural proteins (e.g., nucleocapsid protein, envelope protein) and extensively-concerned nonstructural proteins (e.g., NSP3, NSP5, NSP12) as well as the ones with relatively less attention (e.g., NSP1, NSP10, NSP14 and NSP16), for developing novel drugs to overcome resistance of SARS-CoV-2 variants to preexisting vaccines and antibody-based treatments.}, }
@article {pmid36721006, year = {2023}, author = {Li, Z and Zheng, Y and Diao, X and Li, R and Sun, N and Xu, Y and Li, X and Duan, S and Gong, W and Si, K}, title = {Robust and adjustable dynamic scattering compensation for high-precision deep tissue optogenetics.}, journal = {Communications biology}, volume = {6}, number = {1}, pages = {128}, doi = {10.1038/s42003-023-04487-w}, pmid = {36721006}, issn = {2399-3642}, abstract = {The development of high-precision optogenetics in deep tissue is limited due to the strong optical scattering induced by biological tissue. Although various wavefront shaping techniques have been developed to compensate the scattering, it is still a challenge to non-invasively characterize the dynamic scattered optical wavefront inside the living tissue. Here, we present a non-invasive scattering compensation system with fast multidither coherent optical adaptive technique (fCOAT), which allows the rapid wavefront correction and stable focusing in dynamic scattering medium. We achieve subcellular-resolution focusing through 500-μm-thickness brain slices, or even three pieces overlapped mouse skulls after just one iteration with a 589 nm CW laser. Further, focusing through dynamic scattering medium such as live rat ear is also successfully achieved. The formed focus can maintain longer than 60 s, which satisfies the requirements of stable optogenetics manipulation. Moreover, the focus size is adjustable from subcellular level to tens of microns to freely match the various manipulation targets. With the specially designed fCOAT system, we successfully achieve single-cellular optogenetic manipulation through the brain tissue, with a stimulation efficiency enhancement up to 300% compared with that of the speckle.}, }
@article {pmid36720854, year = {2023}, author = {Duan, J and Xu, P and Zhang, H and Luan, X and Yang, J and He, X and Mao, C and Shen, DD and Ji, Y and Cheng, X and Jiang, H and Jiang, Y and Zhang, S and Zhang, Y and Xu, HE}, title = {Mechanism of hormone and allosteric agonist mediated activation of follicle stimulating hormone receptor.}, journal = {Nature communications}, volume = {14}, number = {1}, pages = {519}, pmid = {36720854}, issn = {2041-1723}, abstract = {Follicle stimulating hormone (FSH) is an essential glycoprotein hormone for human reproduction, which functions are mediated by a G protein-coupled receptor, FSHR. Aberrant FSH-FSHR signaling causes infertility and ovarian hyperstimulation syndrome. Here we report cryo-EM structures of FSHR in both inactive and active states, with the active structure bound to FSH and an allosteric agonist compound 21 f. The structures of FSHR are similar to other glycoprotein hormone receptors, highlighting a conserved activation mechanism of hormone-induced receptor activation. Compound 21 f formed extensive interactions with the TMD to directly activate FSHR. Importantly, the unique residue H615[7.42] in FSHR plays an essential role in determining FSHR selectivity for various allosteric agonists. Together, our structures provide a molecular basis of FSH and small allosteric agonist-mediated FSHR activation, which could inspire the design of FSHR-targeted drugs for the treatment of infertility and controlled ovarian stimulation for in vitro fertilization.}, }
@article {pmid36720164, year = {2023}, author = {Li, Z and Zhang, G and Wang, L and Wei, J and Dang, J}, title = {Emotion recognition using spatial-temporal EEG features through convolutional graph attention network.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acb79e}, pmid = {36720164}, issn = {1741-2552}, abstract = {OBJECTIVE: Constructing an efficient human emotion recognition model based on electroencephalogram (EEG) signals is of great significance for realizing emotional brain computer interaction and improving machine intelligence.<br><br>APPROACH: In this paper, we present a spatial-temporal feature fused convolutional graph attention network (STFCGAT) model based on multi-channel EEG signals for human emotion recognition. First, we combined the single-channel differential entropy (DE) feature with the cross-channel functional connectivity (FC) feature to extract both the temporal variation and spatial topological information of EEG. After that, a novel convolutional graph attention network was used to fuse the DE and FC features and further extract higher-level graph structural information with sufficient expressive power for emotion recognition. Furthermore, we introduced a multi-headed attention mechanism in graph neural networks to improve the generalization ability of the model.<br><br>MAIN RESULTS: We evaluated the emotion recognition performance of our proposed model on the public SEED and DEAP datasets, which achieved a classification accuracy of 99.11&#xb1;0.83% and 94.83&#xb1;3.41% in subject-dependent and subject-independent experiments on SEED dataset, and achieved an accuracy of 91.19&#xb1;1.24% and 92.03&#xb1;4.57% for discrimination of arousal and valence in subject-independent experiments on DEAP dataset. Notably, our model achieved state-of-the-art (SOTA) performance on cross-subject emotion recognition task for both datasets. In addition, we gained an insight into the proposed frame by both the ablation experiments and the analysis of spatial patterns of FC and DE features.<br><br>SIGNIFICANCE: All these results prove the effectiveness of the STFCGAT architecture for emotion recognition and also indicate that there are significant differences in the spatial-temporal characteristics of the brain under different emotional states.}, }
@article {pmid36720162, year = {2023}, author = {Remakanthakurup Sindhu, K and Ngo, D and Ombao, H and Olaya, JE and Shrey, DW and Lopour, BA}, title = {A novel method for dynamically altering the surface area of intracranial EEG electrodes.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acb79f}, pmid = {36720162}, issn = {1741-2552}, abstract = {Intracranial EEG (iEEG) plays a critical role in the treatment of neurological diseases, such as epilepsy and Parkinson's disease, as well as the development of neural prostheses and brain computer interfaces. While electrode geometries vary widely across these applications, the impact of electrode size on iEEG features and morphology is not well understood. Some insight has been gained from computer simulations, as well as experiments in which signals are recorded using electrodes of different sizes concurrently in different brain regions. Here, we introduce a novel method to record from electrodes of different sizes in the exact same location by changing the size of iEEG electrodes after implantation in the brain. We first present a theoretical model and an in vitro validation of the method. We then report the results of an in vivo implementation in three human subjects with refractory epilepsy. We recorded iEEG data from three different electrode sizes and compared the amplitudes, power spectra, inter-channel correlations, and signal-to-noise ratio (SNR) of interictal epileptiform discharges, i.e., epileptic spikes. We found that iEEG amplitude and power decreased as electrode size increased, while inter-channel correlation did not change significantly with electrode size. The SNR of epileptic spikes was generally highest in the smallest electrodes, but 39% of spikes had maximal SNR in larger electrodes. This likely depends on the precise location and spatial spread of each spike. Overall, this new method enables multi-scale measurements of electrical activity in the human brain that can facilitate our understanding of neurophysiology, treatment of neurological disease, and development of novel technologies.}, }
@article {pmid36719563, year = {2023}, author = {Dong, Y and Wang, L and Li, M}, title = {Applying correlation analysis to electrode optimization in source domain.}, journal = {Medical &amp; biological engineering &amp; computing}, volume = {}, number = {}, pages = {}, pmid = {36719563}, issn = {1741-0444}, abstract = {In brain computer interface-based neurorehabilitation system, a large number of electrodes may increase the difficulty of signal acquisition and the time consumption of decoding algorithm for motor imagery EEG (MI-EEG). The traditional electrode optimization methods were limited by the low spatial resolution of scalp EEG. EEG source imaging (ESI) was further applied to reduce the number of electrodes, in which either the electrodes covering activated cortical areas were selected, or the reconstructed electrodes of EEGs with higher Fisher scores were retained. However, the activated dipoles do not all contribute equally to decoding, and the Fisher score cannot represent the correlations between electrodes and dipoles. In this paper, based on ESI and correlation analysis, a novel electrode optimization method, denoted ECCEO, was developed. The scalp MI-EEG was mapped to cortical regions by ESI, and the dipoles with larger amplitudes were chosen to designate a region of interest (ROI). Then, Pearson correlation coefficients between each dipole of the ROI and the corresponding electrode were calculated, averaged, and ranked to obtain two average correlation coefficient sequences. A small but important group of electrodes for each class were alternately added to the predetermined basic electrode set to form a candidate electrode set. Their features were extracted and evaluated to determine the optimal electrode set. Experiments were conducted on two public datasets, the average decoding accuracies achieved 95.99% and 88.30%, and the reduction of computational cost were 65% and 56%, respectively; statistical significance was examined as well.}, }
@article {pmid36716553, year = {2023}, author = {Fan, C and Zha, R and Liu, Y and Wei, Z and Wang, Y and Song, H and Lv, W and Ren, J and Hong, W and Gou, H and Zhang, P and Chen, Y and Zhou, Y and Pan, Y and Zhang, X}, title = {Altered white matter functional network in nicotine addiction.}, journal = {Psychiatry research}, volume = {321}, number = {}, pages = {115073}, doi = {10.1016/j.psychres.2023.115073}, pmid = {36716553}, issn = {1872-7123}, abstract = {Nicotine addiction is a neuropsychiatric disorder with dysfunction in cortices as well as white matter (WM). The nature of the functional alterations in WM remains unclear. The small-world model can well characterize the structure and function of the human brain. In this study, we utilized the small-world model to compare the WM functional connectivity between 62 nicotine addiction participants (called the discovery sample) and 66 matched healthy controls (called the control sample). We also recruited an independent sample comprising 32 nicotine addicts (called the validation sample) for clinical application. The WM functional network data at the network level showed that the nicotine addiction group revealed decreased small-worldness index (σ) and normalized clustering coefficient (γ) compared with healthy controls. For clinical application, the small-world topology of WM functional connectivity could distinguish nicotine addicts from healthy controls (classification accuracy=0.59323, p = 0.0464). We trained abnormal small-world properties on the discovery sample to identify the severity of nicotine addiction, and the identification was successfully applied to the validation sample (classification accuracy=0.65625, p = 0.0106). Our neuroimaging findings provide direct evidence for WM functional changes in nicotine addiction and suggest that the small-world properties of WM function could be qualified as potential biomarkers in nicotine addiction.}, }
@article {pmid36716494, year = {2023}, author = {Delisle-Rodriguez, D and Silva, L and Bastos Filho, TF}, title = {EEG changes during passive movements improve the motor imagery feature extraction in BCIs-based sensory feedback calibration.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acb73b}, pmid = {36716494}, issn = {1741-2552}, abstract = {OBJECTIVE: This work proposes a method for two calibration schemes based on sensory feedback to extract reliable motor imagery (MI) features, and provide classification outputs more correlated to the user's intention.<br><br>METHOD: After filtering the raw EEG, a two-step method for spatial feature extraction by using the Riemannian Covariance Matrices (RCM) method and Common Spatial Patterns (CSP) is proposed here. It uses electroencephalogram (EEG) data from trials providing feedback, in an intermediate step composed of both kth nearest neighbors and probability analyses, to find periods of time in which the user probably performed well the MI task without feedback. These periods are then used to extract features with better separability, and train a classifier for MI recognition. For evaluation, an in-house dataset with eight healthy volunteers and two post-stroke patients that performed lower-limb MI, and consequently received passive movements as feedback was used. Other popular public EEG datasets (such as BCI Competition IV dataset IIb, among others) from healthy subjects that executed upper-and lower-limbs MI tasks under continuous visual sensory feedback were further used.<br><br>RESULTS: The proposed system based on the Riemannian geometry method in two-steps (RCM-RCM) outperformed significantly baseline methods, reaching average accuracy up to 82.29%. These findings show that EEG data on periods providing passive movement can be used to contribute greatly during MI feature extraction.<br><br>SIGNIFICANCE: Unconscious brain responses elicited over the sensorimotor areas may be avoided or greatly reduced by applying our approach in MI-based brain-computer interfaces (BCIs). Therefore, BCI's outputs more correlated to the user's intention can be obtained.}, }
@article {pmid36711591, year = {2023}, author = {Willett, F and Kunz, E and Fan, C and Avansino, D and Wilson, G and Choi, EY and Kamdar, F and Hochberg, LR and Druckmann, S and Shenoy, KV and Henderson, JM}, title = {A high-performance speech neuroprosthesis.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, doi = {10.1101/2023.01.21.524489}, pmid = {36711591}, abstract = {Speech brain-computer interfaces (BCIs) have the potential to restore rapid communication to people with paralysis by decoding neural activity evoked by attempted speaking movements into text [1,2] or sound [3,4] .Early demonstrations, while promising, have not yet achieved accuracies high enough for communication of unconstrainted sentences from a large vocabulary [1â€"5] . Here, we demonstrate the first speech-to-text BCI that records spiking activity from intracortical microelectrode arrays. Enabled by these high-resolution recordings, our study participant, who can no longer speak intelligibly due amyotrophic lateral sclerosis (ALS), achieved a 9.1% word error rate on a 50 word vocabulary (2.7 times fewer errors than the prior state of the art speech BCI [2]) and a 23.8% word error rate on a 125,000 word vocabulary (the first successful demonstration of large-vocabulary decoding). Our BCI decoded speech at 62 words per minute, which is 3.4 times faster than the prior record for any kind of BCI [6] and begins to approach the speed of natural conversation (160 words per minute [7]). Finally, we highlight two aspects of the neural code for speech that are encouraging for speech BCIs: spatially intermixed tuning to speech articulators that makes accurate decoding possible from only a small region of cortex, and a detailed articulatory representation of phonemes that persists years after paralysis. These results show a feasible path forward for using intracortical speech BCIs to restore rapid communication to people with paralysis who can no longer speak.}, }
@article {pmid36711163, year = {2023}, author = {Cho, YK and Koh, CS and Lee, Y and Park, M and Kim, TJ and Jung, HH and Chang, JW and Jun, SB}, title = {Somatosensory ECoG-based brain-machine interface with electrical stimulation on medial forebrain bundle.}, journal = {Biomedical engineering letters}, volume = {13}, number = {1}, pages = {85-95}, pmid = {36711163}, issn = {2093-985X}, abstract = {Brain-machine interface (BMI) provides an alternative route for controlling an external device with one's intention. For individuals with motor-related disability, the BMI technologies can be used to replace or restore motor functions. Therefore, BMIs for movement restoration generally decode the neural activity from the motor-related brain regions. In this study, however, we designed a BMI system that uses sensory-related neural signals for BMI combined with electrical stimulation for reward. Four-channel electrocorticographic (ECoG) signals were recorded from the whisker-related somatosensory cortex of rats and converted to extract the BMI signals to control the one-dimensional movement of a dot on the screen. At the same time, we used operant conditioning with electrical stimulation on medial forebrain bundle (MFB), which provides a virtual reward to motivate the rat to move the dot towards the desired center region. The BMI task training was performed for 7 days with ECoG recording and MFB stimulation. Animals successfully learned to move the dot location to the desired position using S1BF neural activity. This study successfully demonstrated that it is feasible to utilize the neural signals from the whisker somatosensory cortex for BMI system. In addition, the MFB electrical stimulation is effective for rats to learn the behavioral task for BMI.}, }
@article {pmid36711161, year = {2023}, author = {Valencia, D and Alimohammad, A}, title = {Partially binarized neural networks for efficient spike sorting.}, journal = {Biomedical engineering letters}, volume = {13}, number = {1}, pages = {73-83}, pmid = {36711161}, issn = {2093-985X}, abstract = {While brain-implantable neural spike sorting can be realized using efficient algorithms, the presence of noise may make it difficult to maintain high-peformance sorting using conventional techniques. In this article, we explore the use of partially binarized neural networks (PBNNs), to the best of our knowledge for the first time, for sorting of neural spike feature vectors. It is shown that compared to the waveform template-based methods, PBNNs offer robust spike sorting over various datasets and noise levels. The ASIC implementation of the PBNN-based spike sorting system in a standard 180-nm CMOS process is presented. The post place and route simulations results show that the synthesized PBNN consumes only 0.59 μ W of power from a 1.8 V supply while operating at 24 kHz and occupies 0.15 mm 2 of silicon area. It is shown that the designed PBNN-based spike sorting system not only offers comparable accuracy to the state-of-the-art spike sorting systems over various noise levels and datasets, it also occupies a smaller silicon area and consumes less power and energy. This makes PBNNs a viable alternative towards the implementation of brain-implantable spike sorting systems.}, }
@article {pmid36711153, year = {2022}, author = {Sohn, WJ and Lim, J and Wang, PT and Pu, H and Malekzadeh-Arasteh, O and Shaw, SJ and Armacost, M and Gong, H and Kellis, S and Andersen, RA and Liu, CY and Heydari, P and Nenadic, Z and Do, AH}, title = {Benchtop and bedside validation of a low-cost programmable cortical stimulator in a testbed for bi-directional brain-computer-interface research.}, journal = {Frontiers in neuroscience}, volume = {16}, number = {}, pages = {1075971}, pmid = {36711153}, issn = {1662-4548}, abstract = {INTRODUCTION: Bi-directional brain-computer interfaces (BD-BCI) to restore movement and sensation must achieve concurrent operation of recording and decoding of motor commands from the brain and stimulating the brain with somatosensory feedback.<br><br>METHODS: A custom programmable direct cortical stimulator (DCS) capable of eliciting artificial sensorimotor response was integrated into an embedded BCI system to form a safe, independent, wireless, and battery powered testbed to explore BD-BCI concepts at a low cost. The BD-BCI stimulator output was tested in phantom brain tissue by assessing its ability to deliver electrical stimulation equivalent to an FDA-approved commercial electrical cortical stimulator. Subsequently, the stimulator was tested in an epilepsy patient with subcortical electrocorticographic (ECoG) implants covering the sensorimotor cortex to assess its ability to elicit equivalent responses as the FDA-approved counterpart. Additional safety features (impedance monitoring, artifact mitigation, and passive and active charge balancing mechanisms) were also implemeneted and tested in phantom brain tissue. Finally, concurrent operation with interleaved stimulation and BCI decoding was tested in a phantom brain as a proof-of-concept operation of BD-BCI system.<br><br>RESULTS: The benchtop prototype BD-BCI stimulator's basic output features (current amplitude, pulse frequency, pulse width, train duration) were validated by demonstrating the output-equivalency to an FDA-approved commercial cortical electrical stimulator (R [2] > 0.99). Charge-neutral stimulation was demonstrated with pulse-width modulation-based correction algorithm preventing steady state voltage deviation. Artifact mitigation achieved a 64.5% peak voltage reduction. Highly accurate impedance monitoring was achieved with R [2] > 0.99 between measured and actual impedance, which in-turn enabled accurate charge density monitoring. An online BCI decoding accuracy of 93.2% between instructional cues and decoded states was achieved while delivering interleaved stimulation. The brain stimulation mapping via ECoG grids in an epilepsy patient showed that the two stimulators elicit equivalent responses.<br><br>SIGNIFICANCE: This study demonstrates clinical validation of a fully-programmable electrical stimulator, integrated into an embedded BCI system. This low-cost BD-BCI system is safe and readily applicable as a testbed for BD-BCI research. In particular, it provides an all-inclusive hardware platform that approximates the limitations in a near-future implantable BD-BCI. This successful benchtop/human validation of the programmable electrical stimulator in a BD-BCI system is a critical milestone toward fully-implantable BD-BCI systems.}, }
@article {pmid36711141, year = {2022}, author = {Li, H and Liu, M and Yu, X and Zhu, J and Wang, C and Chen, X and Feng, C and Leng, J and Zhang, Y and Xu, F}, title = {Coherence based graph convolution network for motor imagery-induced EEG after spinal cord injury.}, journal = {Frontiers in neuroscience}, volume = {16}, number = {}, pages = {1097660}, pmid = {36711141}, issn = {1662-4548}, abstract = {BACKGROUND: Spinal cord injury (SCI) may lead to impaired motor function, autonomic nervous system dysfunction, and other dysfunctions. Brain-computer Interface (BCI) system based on motor imagery (MI) can provide more scientific and effective treatment solutions for SCI patients.<br><br>METHODS: According to the interaction between brain regions, a coherence-based graph convolutional network (C-GCN) method is proposed to extract the temporal-frequency-spatial features and functional connectivity information of EEG signals. The proposed algorithm constructs multi-channel EEG features based on coherence networks as graphical signals and then classifies MI tasks. Different from the traditional graphical convolutional neural network (GCN), the C-GCN method uses the coherence network of EEG signals to determine MI-related functional connections, which are used to represent the intrinsic connections between EEG channels in different rhythms and different MI tasks. EEG data of SCI patients and healthy subjects have been analyzed, where healthy subjects served as the control group.<br><br>RESULTS: The experimental results show that the C-GCN method can achieve the best classification performance with certain reliability and stability, the highest classification accuracy is 96.85%.<br><br>CONCLUSION: The proposed framework can provide an effective theoretical basis for the rehabilitation treatment of SCI patients.}, }
@article {pmid36710855, year = {2022}, author = {Sajno, E and Bartolotta, S and Tuena, C and Cipresso, P and Pedroli, E and Riva, G}, title = {Machine learning in biosignals processing for mental health: A narrative review.}, journal = {Frontiers in psychology}, volume = {13}, number = {}, pages = {1066317}, pmid = {36710855}, issn = {1664-1078}, abstract = {Machine Learning (ML) offers unique and powerful tools for mental health practitioners to improve evidence-based psychological interventions and diagnoses. Indeed, by detecting and analyzing different biosignals, it is possible to differentiate between typical and atypical functioning and to achieve a high level of personalization across all phases of mental health care. This narrative review is aimed at presenting a comprehensive overview of how ML algorithms can be used to infer the psychological states from biosignals. After that, key examples of how they can be used in mental health clinical activity and research are illustrated. A description of the biosignals typically used to infer cognitive and emotional correlates (e.g., EEG and ECG), will be provided, alongside their application in Diagnostic Precision Medicine, Affective Computing, and brain-computer Interfaces. The contents will then focus on challenges and research questions related to ML applied to mental health and biosignals analysis, pointing out the advantages and possible drawbacks connected to the widespread application of AI in the medical/mental health fields. The integration of mental health research and ML data science will facilitate the transition to personalized and effective medicine, and, to do so, it is important that researchers from psychological/ medical disciplines/health care professionals and data scientists all share a common background and vision of the current research.}, }
@article {pmid36709613, year = {2023}, author = {Cai, J and Xie, M and Zhao, L and Li, X and Liang, S and Deng, W and Guo, W and Ma, X and Sham, PC and Wang, Q and Li, T}, title = {White matter changes and its relationship with clinical symptom in medication-naive first-episode early onset schizophrenia.}, journal = {Asian journal of psychiatry}, volume = {82}, number = {}, pages = {103482}, doi = {10.1016/j.ajp.2023.103482}, pmid = {36709613}, issn = {1876-2026}, abstract = {Previous studies have highlighted the role of white matter (WM) alterations as biomarkers of the disease state and prognosis of schizophrenia. However, less is known about WM abnormalities in the rarely occurring adolescent early onset schizophrenia (EOS). In this study, T1-weighted and diffusion-weighted images were collected in 56 medication-naive first-episode participants with EOS and 43 healthy controls (HCs). Using Tract-based Spatial Statistics, we calculate case-control differences in scalar diffusion measures, i.e. fractional anisotropy (FA) and mean diffusivity (MD), and investigated their association with clinical feature in participants with EOS. Compared with HCs, decreased MD was found in EOS group most notably in the inferior longitudinal fasciculus, anterior thalamic radiation, inferior fronto-occipital fasciculus and corticospinal tract in the right hemisphere. No significant difference was found in FA between these two groups. The FA values of the forceps minor and the right superior longitudinal fasciculus were suggested to be related to the severity of clinical symptom in participants with EOS. These results provide clues about the neural basis of schizophrenia and a potential biomarker for clinical studies.}, }
@article {pmid36707885, year = {2023}, author = {Angerhöfer, C and Vermehren, M and Colucci, A and Nann, M and Koßmehl, P and Niedeggen, A and Kim, WS and Chang, WK and Paik, NJ and Hömberg, V and Soekadar, SR}, title = {The Berlin Bimanual Test for Tetraplegia (BeBiTT): development, psychometric properties, and sensitivity to change in assistive hand exoskeleton application.}, journal = {Journal of neuroengineering and rehabilitation}, volume = {20}, number = {1}, pages = {17}, pmid = {36707885}, issn = {1743-0003}, support = {759370/ERC_/European Research Council/International ; }, abstract = {BACKGROUND: Assistive hand exoskeletons are promising tools to restore hand function after cervical spinal cord injury (SCI) but assessing their specific impact on bimanual hand and arm function is limited due to lack of reliable and valid clinical tests. Here, we introduce the Berlin Bimanual Test for Tetraplegia (BeBiTT) and demonstrate its psychometric properties and sensitivity to assistive hand exoskeleton-related improvements in bimanual task performance.<br><br>METHODS: Fourteen study participants with subacute cervical SCI performed the BeBiTT unassisted (baseline). Thereafter, participants repeated the BeBiTT while wearing a brain/neural hand exoskeleton (B/NHE) (intervention). Online control of the B/NHE was established via a hybrid sensorimotor rhythm-based brain-computer interface (BCI) translating electroencephalographic (EEG) and electrooculographic (EOG) signals into open/close commands. For reliability assessment, BeBiTT scores were obtained by four independent observers. Besides internal consistency analysis, construct validity was assessed by correlating baseline BeBiTT scores with the Spinal Cord Independence Measure III (SCIM III) and Quadriplegia Index of Function (QIF). Sensitivity to differences in bimanual task performance was assessed with a bootstrapped paired t-test.<br><br>RESULTS: The BeBiTT showed excellent interrater reliability (intraclass correlation coefficients&#x2009;>&#x2009;0.9) and internal consistency (&#x3b1;&#x2009;=&#x2009;0.91). Validity of the BeBiTT was evidenced by strong correlations between BeBiTT scores and SCIM III as well as QIF. Wearing a B/NHE (intervention) improved the BeBiTT score significantly (p&#x2009;<&#x2009;0.05) with high effect size (d&#x2009;=&#x2009;1.063), documenting high sensitivity to intervention-related differences in bimanual task performance.<br><br>CONCLUSION: The BeBiTT is a reliable and valid test for evaluating bimanual task performance in persons with tetraplegia, suitable to assess the impact of assistive hand exoskeletons on bimanual function.}, }
@article {pmid36706879, year = {2023}, author = {Li, H and Shen, S and Yu, K and Wang, H and Fu, J}, title = {Construction of porous structure-based carboxymethyl chitosan/sodium alginate/tea polyphenols for wound dressing.}, journal = {International journal of biological macromolecules}, volume = {}, number = {}, pages = {123404}, doi = {10.1016/j.ijbiomac.2023.123404}, pmid = {36706879}, issn = {1879-0003}, abstract = {Polysaccharide-based materials with porous structure were selected as the basic skeleton to prepare a flexible and biodegradable wound dressing. The carboxymethyl chitosan/sodium alginate/tea polyphenols (CC/SA/TP) with two-layer porous structure exhibits a variety of performances. The specific combined structure with ordered and lamellar porous structure was constructed by high-speed homogenized foaming, Ca[2+] crosslinking and two-step freeze-drying methods. Moreover, the CC/SA/TP porous structure owns a better shape retention and recovery because of the 3D network with "egg-box" structure formed by impregnation. Tea polyphenols are efficiently encapsulated into porous structure and released in a sustained pattern. After storing for 60 days, the CC/SA/TP porous structure still exhibits great suitable water vapor transmittance, efficient antibacterial activity and ultrarapid antioxidant activity. Meanwhile, the relatively low differential blood clotting index (BCI) and cytotoxicity of the CC/SA/TP porous structure indicate that it possesses the possibility for adjusting and controlling wound bleeding. The test results reveal that the CC/SA/TP porous structure might be expected to play a great potential role in biomedical applications of wound dressing.}, }
@article {pmid36705845, year = {2023}, author = {Zhao, ZD and Zhang, L and Xiang, X and Kim, D and Li, H and Cao, P and Shen, WL}, title = {Neurocircuitry of Predatory Hunting.}, journal = {Neuroscience bulletin}, volume = {}, number = {}, pages = {}, doi = {10.1007/s12264-022-01018-1}, pmid = {36705845}, issn = {1995-8218}, abstract = {Predatory hunting is an important type of innate behavior evolutionarily conserved across the animal kingdom. It is typically composed of a set of sequential actions, including prey search, pursuit, attack, and consumption. This behavior is subject to control by the nervous system. Early studies used toads as a model to probe the neuroethology of hunting, which led to the proposal of a sensory-triggered release mechanism for hunting actions. More recent studies have used genetically-trackable zebrafish and rodents and have made breakthrough discoveries in the neuroethology and neurocircuits underlying this behavior. Here, we review the sophisticated neurocircuitry involved in hunting and summarize the detailed mechanism for the circuitry to encode various aspects of hunting neuroethology, including sensory processing, sensorimotor transformation, motivation, and sequential encoding of hunting actions. We also discuss the overlapping brain circuits for hunting and feeding and point out the limitations of current studies. We propose that hunting is an ideal behavioral paradigm in which to study the neuroethology of motivated behaviors, which may shed new light on epidemic disorders, including binge-eating, obesity, and obsessive-compulsive disorders.}, }
@article {pmid36704636, year = {2023}, author = {Lyu, X and Ding, P and Li, S and Dong, Y and Su, L and Zhao, L and Gong, A and Fu, Y}, title = {Human factors engineering of BCI: an evaluation for satisfaction of BCI based on motor imagery.}, journal = {Cognitive neurodynamics}, volume = {17}, number = {1}, pages = {105-118}, pmid = {36704636}, issn = {1871-4080}, abstract = {Existing brain-computer interface (BCI) research has made great progress in improving the accuracy and information transfer rate (ITR) of BCI systems. However, the practicability of BCI is still difficult to achieve. One of the important reasons for this difficulty is that human factors are not fully considered in the research and development of BCI. As a result, BCI systems have not yet reached users' expectations. In this study, we investigate a BCI system of motor imagery for lower limb synchronous rehabilitation as an example. From the perspective of human factors engineering of BCI, a comprehensive evaluation method of BCI system development is proposed based on the concept of human-centered design and evaluation. Subjects' satisfaction ratings for BCI sensors, visual analog scale (VAS), subjects' satisfaction rating of the BCI system, and the mental workload rating for subjects manipulating the BCI system, as well as interview/follow-up comprehensive evaluation of motor imagery of BCI (MI-BCI) system satisfaction were used. The methods and concepts proposed in this study provide useful insights for the design of personalized MI-BCI. We expect that the human factors engineering of BCI could be applied to the design and satisfaction evaluation of MI-BCI, so as to promote the practical application of this kind of BCI.}, }
@article {pmid36704625, year = {2023}, author = {Cui, Z and Lin, J and Fu, X and Zhang, S and Li, P and Wu, X and Wang, X and Chen, W and Zhu, S and Li, Y}, title = {Construction of the dynamic model of SCI rehabilitation using bidirectional stimulation and its application in rehabilitating with BCI.}, journal = {Cognitive neurodynamics}, volume = {17}, number = {1}, pages = {169-181}, pmid = {36704625}, issn = {1871-4080}, abstract = {UNLABELLED: Patients with complete spinal cord injury have a complete loss of motor and sensory functions below the injury plane, leading to a complete loss of function of the nerve pathway in the injured area. Improving the microenvironment in the injured area of patients with spinal cord injury, promoting axon regeneration of the nerve cells is challenging research fields. The brain-computer interface rehabilitation system is different from the other rehabilitation techniques. It can exert bidirectional stimulation on the spinal cord injury area, and can make positively rehabilitation effects of the patient with complete spinal cord injury. A dynamic model was constructed for the patient with spinal cord injury under-stimulation therapy, and the mechanism of the brain-computer interface in rehabilitation training was explored. The effects of the three current rehabilitation treatment methods on the microenvironment in a microscopic nonlinear model were innovatively unified and a complex system mapping relationship from the microscopic axon growth to macroscopic motor functions was constructed. The basic structure of the model was determined by simulating and fitting the data of the open rat experiments. A clinical rehabilitation experiment of spinal cord injury based on brain-computer interface was built, recruiting a patient with complete spinal cord injury, and the rehabilitation training and follow-up were conducted. The changes in the motor function of the patient was simulated and predicted through the constructed model, and the trend in the motor function improvement was successfully predicted over time. This proposed model explores the mechanism of brain-computer interface in rehabilitating patients with complete spinal cord injury, and it is also an application of complex system theory in rehabilitation medicine.<br><br>SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-022-09804-3.}, }
@article {pmid36704007, year = {2022}, author = {de Oliveira, IH and Rodrigues, AC}, title = {Empirical comparison of deep learning methods for EEG decoding.}, journal = {Frontiers in neuroscience}, volume = {16}, number = {}, pages = {1003984}, pmid = {36704007}, issn = {1662-4548}, abstract = {Electroencephalography (EEG) is a technique that can be used in non-invasive brain-machine interface (BMI) systems to register brain electrical activity. The EEG signals are non-linear and non-stationary, making the decoding procedure a complex task. Deep learning techniques have been successfully applied in several research fields, often improving the results compared with traditional approaches. Therefore, it is believed that these techniques can also improve the process of decoding brain signals in BMI systems. In this work, we present the implementation of two deep learning-based decoders and we compared the results with other state of art deep learning methods. The first decoder uses long short-term memory (LSTM) recurrent neural network and the second, entitled EEGNet-LSTM, combines a well-known neural decoder based on convolutional neural networks, called EEGNet, with some LSTM layers. The decoders have been tested using data set 2a from BCI Competition IV, and the results showed that the EEGNet-LSTM decoder has been approximately 23% better than the competition-winning decoder. A Wilcoxon t-test showed a significant difference between the two decoders (Z = 2.524, p = 0.012). The LSTM-based decoder has been approximately 9% higher than the best decoder from the same competition. However, there was no significant difference (Z = 1.540, p = 0.123). In order to verify the replication of the EEGNet-LSTM decoder on another data, we performed a test with PhysioNet's Physiobank EEG Motor Movement/Imagery dataset. The EEGNet-LSTM presented a higher performance (0.85 accuracy) than the EEGNet (0.82 accuracy). The results of this work can be important for the development of new research, as well as EEG-based BMI systems, which can benefit from the high precision of neural decoders.}, }
@article {pmid36699986, year = {2023}, author = {Shang, Q and Ma, H and Wang, C and Gao, L}, title = {Effects of Background Fitting of e-Commerce Live Streaming on Consumers' Purchase Intentions: A Cognitive-Affective Perspective.}, journal = {Psychology research and behavior management}, volume = {16}, number = {}, pages = {149-168}, pmid = {36699986}, issn = {1179-1578}, abstract = {PURPOSE: The purpose of this paper is to explore the effects of the background fitting of e-commerce live streaming on consumers' purchase intentions and the relevant internal psychological mechanism from the cognitive-affective perspective.<br><br>METHODS: In this study, a theoretical framework model of SOR comprising six variables is established. SPSS and SmartPLS are used to test the model and analyze data collected from a comprehensive questionnaire survey of 424 Chinese online consumers.<br><br>RESULTS: Results demonstrate that the impact of background fitting in e-commerce live streaming on consumers' purchase intentions can be divided into three stages. In the first stage, background fitting (comprised of both product-background fit and anchor-background fit) positively affect consumer cognitive process (perceived trust and perceived value). Perceived trust is mainly affected by anchor-background fit, while perceived value is mainly affected by product-background fit. In the second stage, consumers' cognitive process subsequently affects their affective process (perceived pleasure). Perceived value also has a greater positive effect on consumers' perceived pleasure than perceived trust, although perceived trust is a prerequisite for improving perceived value. In the third stage, the affective process further promotes consumers' purchase intentions.<br><br>CONCLUSION: Combining both SOR theory and cognitive-affective perspective, this study reveals that the internal influence mechanism of background fitting in e-commerce live streaming on consumers' purchase intentions is divided into three stages. Theoretically, this study not only expands the application of SOR theory in the research field of e-commerce live streaming from the perspective of external background stimulation, but also importantly contributes to the application of cognitive-emotional perspective in e-commerce live streaming. Practically, the study suggests optimizing background fitting as an effective way to improve consumer purchase intention in e-commerce live streaming, and it is better to optimize background fitting from the perspective of improving perceived trust, perceived value, and perceived pleasure.}, }
@article {pmid36699541, year = {2022}, author = {Hu, J and Wang, Y and Tong, Y and Lin, G and Li, Y and Chen, J and Xu, D and Wang, L and Bai, R}, title = {Thalamic structure and anastomosis in different hemispheres of moyamoya disease.}, journal = {Frontiers in neuroscience}, volume = {16}, number = {}, pages = {1058137}, pmid = {36699541}, issn = {1662-4548}, abstract = {OBJECTIVE: The progression of the asymptomatic hemisphere of moyamoya disease (MMD) is largely unknown. In this study, we investigated the differences in subcortical gray matter structure and angiographic features between asymptomatic and symptomatic hemispheres in patients with MMD.<br><br>METHODS: We retrospectively reviewed patients with MMD in consecutive cases in our center. We compared subcortical gray matter volume and three types of collaterals (lenticulostriate anastomosis, thalamic anastomosis, and choroidal anastomosis) between symptomatic and asymptomatic hemispheres. Symptomatic hemispheres were classified as ischemic hemisphere (i-hemisphere) and hemorrhagic hemisphere (h-hemisphere). Asymptomatic hemispheres were classified as contralateral asymptomatic hemisphere of i-hemisphere (ai-hemisphere), contralateral asymptomatic hemisphere of h-hemisphere (ah-hemisphere), bilateral asymptomatic hemispheres in asymptomatic group (aa-hemisphere).<br><br>RESULTS: A total of 117 MMD patients were reviewed, and 49 of them met the inclusion criteria, with 98 hemispheres being analyzed. The thalamic volume was found to differ significantly between the i- and ai-hemispheres (P = 0.010), between the i- and ah-hemispheres (P = 0.004), as well as between the h- and ai-hemispheres (P = 0.002), between the h- and ah-hemispheres (P < 0.001). There was a higher incidence of thalamic anastomosis in the ai-hemispheres than i-hemispheres (31.3% vs. 6.3%, P = 0.070), and in the ah-hemispheres than h-hemispheres (29.6% vs. 11.1%, P = 0.088). Additionally, the hemispheres with thalamic anastomosis had a significantly greater volume than those without thalamic anastomosis (P = 0.024). Univariate and multivariate logistic regression analysis showed that thalamic volume was closely associated with thalamic anastomosis.<br><br>CONCLUSION: The thalamic volume and the incidence of thalamic anastomosis increase in asymptomatic hemispheres and decrease in symptomatic hemispheres. Combining these two characteristics may be helpful in assessing the risk of stroke in the asymptomatic hemispheres of MMD as well as understanding the pathological evolution of the disease.}, }
@article {pmid36699533, year = {2022}, author = {Li, Y and Zhang, X and Ming, D}, title = {Early-stage fusion of EEG and fNIRS improves classification of motor imagery.}, journal = {Frontiers in neuroscience}, volume = {16}, number = {}, pages = {1062889}, pmid = {36699533}, issn = {1662-4548}, abstract = {INTRODUCTION: Many research papers have reported successful implementation of hybrid brain-computer interfaces by complementarily combining EEG and fNIRS, to improve classification performance. However, modality or feature fusion of EEG and fNIRS was usually designed for specific user cases, which were generally customized and hard to be generalized. How to effectively utilize information from the two modalities was still unclear.<br><br>METHODS: In this paper, we conducted a study to investigate the stage of bi-modal fusion based on EEG and fNIRS. A Y-shaped neural network was proposed and evaluated on an open dataset, which fuses the bimodal information in different stages.<br><br>RESULTS: The results suggests that the early-stage fusion of EEG and fNIRS have significantly higher performance compared to middle-stage and late-stage fusion network configuration (N = 57, P < 0.05). With the proposed framework, the average accuracy of 29 participants reaches 76.21% in the left-or-right hand motor imagery task in leave-one-out cross-validation, using bi-modal data as network inputs respectively, which is in the same level as the state-of-the-art hybrid BCI methods based on EEG and fNIRS data.}, }
@article {pmid36698872, year = {2022}, author = {Zanona, AF and Piscitelli, D and Seixas, VM and Scipioni, KRDDS and Bastos, MSC and de Sá, LCK and Monte-Silva, K and Bolivar, M and Solnik, S and De Souza, RF}, title = {Brain-computer interface combined with mental practice and occupational therapy enhances upper limb motor recovery, activities of daily living, and participation in subacute stroke.}, journal = {Frontiers in neurology}, volume = {13}, number = {}, pages = {1041978}, pmid = {36698872}, issn = {1664-2295}, abstract = {BACKGROUND: We investigated the effects of brain-computer interface (BCI) combined with mental practice (MP) and occupational therapy (OT) on performance in activities of daily living (ADL) in stroke survivors.<br><br>METHODS: Participants were randomized into two groups: experimental (n = 23, BCI controlling a hand exoskeleton combined with MP and OT) and control (n = 21, OT). Subjects were assessed with the functional independence measure (FIM), motor activity log (MAL), amount of use (MAL-AOM), and quality of movement (MAL-QOM). The box and blocks test (BBT) and the Jebsen hand functional test (JHFT) were used for the primary outcome of performance in ADL, while the Fugl-Meyer Assessment was used for the secondary outcome. Exoskeleton activation and the degree of motor imagery (measured as event-related desynchronization) were assessed in the experimental group. For the BCI, the EEG electrodes were placed on the regions of FC3, C3, CP3, FC4, C4, and CP4, according to the international 10-20 EEG system. The exoskeleton was placed on the affected hand. MP was based on functional tasks. OT consisted of ADL training, muscle mobilization, reaching tasks, manipulation and prehension, mirror therapy, and high-frequency therapeutic vibration. The protocol lasted 1 h, five times a week, for 2 weeks.<br><br>RESULTS: There was a difference between baseline and post-intervention analysis for the experimental group in all evaluations: FIM (p = 0.001, d = 0.56), MAL-AOM (p = 0.001, d = 0.83), MAL-QOM (p = 0.006, d = 0.84), BBT (p = 0.004, d = 0.40), and JHFT (p = 0.001, d = 0.45). Within the experimental group, post-intervention improvements were detected in the degree of motor imagery (p < 0.001) and the amount of exoskeleton activations (p < 0.001). For the control group, differences were detected for MAL-AOM (p = 0.001, d = 0.72), MAL-QOM (p = 0.013, d = 0.50), and BBT (p = 0.005, d = 0.23). Notably, the effect sizes were larger for the experimental group. No differences were detected between groups at post-intervention.<br><br>CONCLUSION: BCI combined with MP and OT is a promising tool for promoting sensorimotor recovery of the upper limb and functional independence in subacute post-stroke survivors.}, }
@article {pmid36698168, year = {2023}, author = {Lim, CG and Soh, CP and Lim, SSY and Fung, DSS and Guan, C and Lee, TS}, title = {Home-based brain-computer interface attention training program for attention deficit hyperactivity disorder: a feasibility trial.}, journal = {Child and adolescent psychiatry and mental health}, volume = {17}, number = {1}, pages = {15}, pmid = {36698168}, issn = {1753-2000}, abstract = {BACKGROUND: Attention deficit hyperactivity disorder (ADHD) is a prevalent child neurodevelopmental disorder that is treated in clinics and in schools. Previous trials suggested that our brain-computer interface (BCI)-based attention training program could improve ADHD symptoms. We have since developed a tablet version of the training program which can be paired with wireless EEG headsets. In this trial, we investigated the feasibility of delivering this tablet-based BCI intervention at home.<br><br>METHODS: Twenty children diagnosed with ADHD, who did not receive any medication for the preceding month, were randomised to receive the 8-week tablet-based BCI intervention either in the clinic or at home. Those in the home intervention group received instructions before commencing the program and got reminders if they were lagging on the training sessions. The ADHD Rating Scale was completed by a blinded clinician at baseline and at week 8. Adverse events were monitored during any contact with the child throughout the trial and at week 8.<br><br>RESULTS: Children in both groups could complete the tablet-based intervention easily on their own with minimal support from the clinic therapist or their parents (at home). The intervention was safe with few reported adverse effects. Clinician-rated inattentive symptoms on the ADHD-Rating Scale reduced by 3.2 (SD 6.20) and 3.9 (SD 5.08) for the home-based and clinic-based groups respectively, suggesting that home-based intervention was comparable to clinic-based intervention.<br><br>CONCLUSIONS: This trial demonstrated that the tablet version of our BCI-based attention training program can be safely delivered to children in the comfort of their own home. Trial registration This trial is registered at clinicaltrials.gov as NCT01344044.}, }
@article {pmid36696073, year = {2023}, author = {Öztürk, S and Devecioğlu, &#x130; and Güçlü, B}, title = {Bayesian prediction of psychophysical detection responses from spike activity in the rat sensorimotor cortex.}, journal = {Journal of computational neuroscience}, volume = {}, number = {}, pages = {}, pmid = {36696073}, issn = {1573-6873}, abstract = {Decoding of sensorimotor information is essential for brain-computer interfaces (BCIs) as well as in normal functioning organisms. In this study, Bayesian models were developed for the prediction of binary decisions of 10 awake freely-moving male/female rats based on neural activity in a vibrotactile yes/no detection task. The vibrotactile stimuli were 40-Hz sinusoidal displacements (amplitude: 200 µm, duration: 0.5 s) applied on the glabrous skin. The task was to depress the right lever for stimulus detection and left lever for stimulus-off condition. Spike activity was recorded from 16-channel microwire arrays implanted in the hindlimb representation of primary somatosensory cortex (S1), overlapping also with the associated representation in the primary motor cortex (M1). Single-/multi-unit average spike rate (Rd) within the stimulus analysis window was used as the predictor of the stimulus state and the behavioral response at each trial based on a Bayesian network model. Due to high neural and psychophysical response variability for each rat and also across subjects, mean Rd was not correlated with hit and false alarm rates. Despite the fluctuations in the neural data, the Bayesian model for each rat generated moderately good accuracy (0.60-0.90) and good class prediction scores (recall, precision, F1) and was also tested with subsets of data (e.g. regular vs. fast spike groups). It was generally observed that the models were better for rats with lower psychophysical performance (lower sensitivity index A'). This suggests that Bayesian inference and similar machine learning techniques may be especially helpful during the training phase of BCIs or for rehabilitation with neuroprostheses.}, }
@article {pmid36693374, year = {2023}, author = {Fan, Z and Chang, J and Liang, Y and Zhu, H and Zhang, C and Zheng, D and Wang, J and Xu, Y and Li, QJ and Hu, H}, title = {Neural mechanism underlying depressive-like state associated with social status loss.}, journal = {Cell}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.cell.2022.12.033}, pmid = {36693374}, issn = {1097-4172}, abstract = {Downward social mobility is a well-known mental risk factor for depression, but its neural mechanism remains elusive. Here, by forcing mice to lose against their subordinates in a non-violent social contest, we lower their social ranks stably and induce depressive-like behaviors. These rank-decline-associated depressive-like behaviors can be reversed by regaining social status. In vivo fiber photometry and single-unit electrophysiological recording show that forced loss, but not natural loss, generates negative reward prediction error (RPE). Through the lateral hypothalamus, the RPE strongly activates the brain's anti-reward center, the lateral habenula (LHb). LHb activation inhibits the medial prefrontal cortex (mPFC) that controls social competitiveness and reinforces retreats in contests. These results reveal the core neural mechanisms mutually promoting social status loss and depressive behaviors. The intertwined neuronal signaling controlling mPFC and LHb activities provides a mechanistic foundation for the crosstalk between social mobility and psychological disorder, unveiling a promising target for intervention.}, }
@article {pmid36693292, year = {2023}, author = {Santamaría-Vázquez, E and Martínez-Cagigal, V and Marcos-Martínez, D and Rodríguez-González, V and Pérez-Velasco, S and Moreno-Calderón, S and Hornero, R}, title = {MEDUSA©: A novel Python-based software ecosystem to accelerate brain-computer interface and cognitive neuroscience research.}, journal = {Computer methods and programs in biomedicine}, volume = {230}, number = {}, pages = {107357}, doi = {10.1016/j.cmpb.2023.107357}, pmid = {36693292}, issn = {1872-7565}, abstract = {BACKGROUND AND OBJECTIVE: Neurotechnologies have great potential to transform our society in ways that are yet to be uncovered. The rate of development in this field has increased significantly in recent years, but there are still barriers that need to be overcome before bringing neurotechnologies to the general public. One of these barriers is the difficulty of performing experiments that require complex software, such as brain-computer interfaces (BCI) or cognitive neuroscience experiments. Current platforms have limitations in terms of functionality and flexibility to meet the needs of researchers, who often need to implement new experimentation settings. This work was aimed to propose a novel software ecosystem, called MEDUSA©, to overcome these limitations.<br><br>METHODS: We followed strict development practices to optimize MEDUSA&#xa9; for research in BCI and cognitive neuroscience, making special emphasis in the modularity, flexibility and scalability of our solution. Moreover, it was implemented in Python, an open-source programming language that reduces the development cost by taking advantage from its high-level syntax and large number of community packages.<br><br>RESULTS: MEDUSA&#xa9; provides a complete suite of signal processing functions, including several deep learning architectures or connectivity analysis, and ready-to-use BCI and neuroscience experiments, making it one of the most complete solutions nowadays. We also put special effort in providing tools to facilitate the development of custom experiments, which can be easily shared with the community through an app market available in our website to promote reproducibility.<br><br>CONCLUSIONS: MEDUSA&#xa9; is a novel software ecosystem for modern BCI and neurotechnology experimentation that provides state-of-the-art tools and encourages the participation of the community to make a difference for the progress of these fields. Visit the official website at https://www.medusabci.com/ to know more about this project.}, }
@article {pmid36693278, year = {2023}, author = {Johnston, R and Abbass, M and Corrigan, B and Martinez-Trujillo, J and Sachs, A}, title = {Decoding spatial locations from primate lateral prefrontal cortex neural activity during virtual navigation.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acb5c2}, pmid = {36693278}, issn = {1741-2552}, abstract = {OBJECTIVE: Decoding the intended trajectories from brain signals using a brain-computer interface system could be used to improve the mobility of patients with disabilities.<br><br>APPROACH: Neuronal activity associated with spatial locations was examined while macaques performed a navigation task within a virtual environment.<br><br>MAIN RESULTS: Here, we provide proof of principle that multi-unit spiking activity recorded from the lateral prefrontal cortex of non-human primates can be used to predict the location of a subject in a virtual maze during a navigation task. The spatial positions within the maze that require a choice or are associated with relevant task events can be better predicted than the locations where no relevant events occur. Importantly, within a task epoch of a single trial, multiple locations along the maze can be independently identified using a support vector machine model.<br><br>SIGNIFICANCE: Considering that the lateral prefrontal cortex of macaques and humans share similar properties, our results suggest that this area could be a valuable implant location for an intracortical brain computer interface system used for spatial navigation in patients with disabilities.}, }
@article {pmid36689427, year = {2023}, author = {Pattisapu, S and Ray, S}, title = {Stimulus-induced narrow-band gamma oscillations in humans can be recorded using open-hardware low-cost EEG amplifier.}, journal = {PloS one}, volume = {18}, number = {1}, pages = {e0279881}, doi = {10.1371/journal.pone.0279881}, pmid = {36689427}, issn = {1932-6203}, abstract = {Stimulus-induced narrow-band gamma oscillations (30-70 Hz) in human electro-encephalograph (EEG) have been linked to attentional and memory mechanisms and are abnormal in mental health conditions such as autism, schizophrenia and Alzheimer's Disease. However, since the absolute power in EEG decreases rapidly with increasing frequency following a "1/f" power law, and the gamma band includes line noise frequency, these oscillations are highly susceptible to instrument noise. Previous studies that recorded stimulus-induced gamma oscillations used expensive research-grade EEG amplifiers to address this issue. While low-cost EEG amplifiers have become popular in Brain Computer Interface applications that mainly rely on low-frequency oscillations (< 30 Hz) or steady-state-visually-evoked-potentials, whether they can also be used to measure stimulus-induced gamma oscillations is unknown. We recorded EEG signals using a low-cost, open-source amplifier (OpenBCI) and a traditional, research-grade amplifier (Brain Products GmbH), both connected to the OpenBCI cap, in male (N = 6) and female (N = 5) subjects (22-29 years) while they viewed full-screen static gratings that are known to induce two distinct gamma oscillations: slow and fast gamma, in a subset of subjects. While the EEG signals from OpenBCI were considerably noisier, we found that out of the seven subjects who showed a gamma response in Brain Products recordings, six showed a gamma response in OpenBCI as well. In spite of the noise in the OpenBCI setup, the spectral and temporal profiles of these responses in alpha (8-13 Hz) and gamma bands were highly correlated between OpenBCI and Brain Products recordings. These results suggest that low-cost amplifiers can potentially be used in stimulus-induced gamma response detection.}, }
@article {pmid36683147, year = {2023}, author = {Jin, J and Xu, Z and Zhang, L and Zhang, C and Zhao, X and Mao, Y and Zhang, H and Liang, X and Wu, J and Yang, Y and Zhang, J}, title = {Gut-derived β-amyloid: Likely a centerpiece of the gut-brain axis contributing to Alzheimer's pathogenesis.}, journal = {Gut microbes}, volume = {15}, number = {1}, pages = {2167172}, doi = {10.1080/19490976.2023.2167172}, pmid = {36683147}, issn = {1949-0984}, abstract = {Peripheral β-amyloid (Aβ), including those contained in the gut, may contribute to the formation of Aβ plaques in the brain, and gut microbiota appears to exert an impact on Alzheimer's disease (AD) via the gut-brain axis, although detailed mechanisms are not clearly defined. The current study focused on uncovering the potential interactions among gut-derived Aβ in aging, gut microbiota, and AD pathogenesis. To achieve this goal, the expression levels of Aβ and several key proteins involved in Aβ metabolism were initially assessed in mouse gut, with key results confirmed in human tissue. The results demonstrated that a high level of Aβ was detected throughout the gut in both mice and human, and gut Aβ42 increased with age in wild type and mutant amyloid precursor protein/presenilin 1 (APP/PS1) mice. Next, the gut microbiome of mice was characterized by 16S rRNA sequencing, and we found the gut microbiome altered significantly in aged APP/PS1 mice and fecal microbiota transplantation (FMT) of aged APP/PS1 mice increased gut BACE1 and Aβ42 levels. Intra-intestinal injection of isotope or fluorescence labeled Aβ combined with vagotomy was also performed to investigate the transmission of Aβ from gut to brain. The data showed that, in aged mice, the gut Aβ42 was transported to the brain mainly via blood rather than the vagal nerve. Furthermore, FMT of APP/PS1 mice induced neuroinflammation, a phenotype that mimics early AD pathology. Taken together, this study suggests that the gut is likely a critical source of Aβ in the brain, and gut microbiota can further upregulate gut Aβ production, thereby potentially contributing to AD pathogenesis.}, }
@article {pmid36682180, year = {2023}, author = {Peng, G and Zhao, K and Zhang, H and Xu, D and Kong, X}, title = {Temporal relative transformer encoding cooperating with channel attention for EEG emotion analysis.}, journal = {Computers in biology and medicine}, volume = {154}, number = {}, pages = {106537}, doi = {10.1016/j.compbiomed.2023.106537}, pmid = {36682180}, issn = {1879-0534}, abstract = {Electroencephalogram (EEG)-based emotion computing has become a hot topic of brain-computer fusion. EEG signals have inherent temporal and spatial characteristics. However, existing studies did not fully consider the two properties. In addition, the position encoding mechanism in the vanilla transformer cannot effectively encode the continuous temporal character of the emotion. A temporal relative (TR) encoding mechanism is proposed to encode the temporal EEG signals for constructing the temporality self-attention in the transformer. To explore the contribution of each EEG channel corresponding to the electrode on the cerebral cortex to emotion analysis, a channel-attention (CA) mechanism is presented. The temporality self-attention mechanism cooperates with the channel-attention mechanism to utilize the temporal and spatial information of EEG signals simultaneously by preprocessing. Exhaustive experiments are conducted on the DEAP dataset, including the binary classification on valence, arousal, dominance, and liking. Furthermore, the discrete emotion category classification task is also conducted by mapping the dimensional annotations of DEAP into discrete emotion categories (5-class). Experimental results demonstrate that our model outperforms the advanced methods for all classification tasks.}, }
@article {pmid36682005, year = {2023}, author = {Guo, B and Zheng, H and Jiang, H and Li, X and Guan, N and Zuo, Y and Zhang, Y and Yang, H and Wang, X}, title = {Enhanced compound-protein binding affinity prediction by representing protein multimodal information via a coevolutionary strategy.}, journal = {Briefings in bioinformatics}, volume = {}, number = {}, pages = {}, doi = {10.1093/bib/bbac628}, pmid = {36682005}, issn = {1477-4054}, abstract = {Due to the lack of a method to efficiently represent the multimodal information of a protein, including its structure and sequence information, predicting compound-protein binding affinity (CPA) still suffers from low accuracy when applying machine-learning methods. To overcome this limitation, in a novel end-to-end architecture (named FeatNN), we develop a coevolutionary strategy to jointly represent the structure and sequence features of proteins and ultimately optimize the mathematical models for predicting CPA. Furthermore, from the perspective of data-driven approach, we proposed a rational method that can utilize both high- and low-quality databases to optimize the accuracy and generalization ability of FeatNN in CPA prediction tasks. Notably, we visually interpret the feature interaction process between sequence and structure in the rationally designed architecture. As a result, FeatNN considerably outperforms the state-of-the-art (SOTA) baseline in virtual drug evaluation tasks, indicating the feasibility of this approach for practical use. FeatNN provides an outstanding method for higher CPA prediction accuracy and better generalization ability by efficiently representing multimodal information of proteins via a coevolutionary strategy.}, }
@article {pmid36680589, year = {2023}, author = {Afreen, A and Ahmed, Z and Khalid, N and Ferheen, I and Ahmed, I}, title = {Optimization and cholesterol-lowering activity of exopolysaccharide from Lactiplantibacillus paraplantarum NCCP 962.}, journal = {Applied microbiology and biotechnology}, volume = {}, number = {}, pages = {}, pmid = {36680589}, issn = {1432-0614}, abstract = {Exopolysaccharides (EPSs) are biological polymers with unique structural features have gained particular interest in the fields of food, chemistry and medicine, and food industry. EPS from the food-grade lactic acid bacteria (LAB) can be used as a natural food additives to commercial ones in the processing and development of functional foods and nutraceuticals. The current study was aimed to explore the EPS-producing LAB from the dahi; to optimize the fermentation conditions through Plackett-Burman (PB) and response surface methodology (RSM); and to study its physicochemical, rheological, functional attributes, and cholesterol-lowering activity. Lactiplantibacillus paraplantarum NCCP 962 was isolated among the 08 strains screened at the initial stage. The PB design screened out four independent factors that had a significant positive effect, i.e., lactose, yeast extract, CaCl2, and tryptone, while the remaining seven had a non-significant effect. The RSM exhibited lactose, yeast extract, and CaCl2, significantly contributing to EPS yield. The maximum EPS yield (0.910 g/L) was obtained at 6.57% lactose, 0.047% yeast extract, 0.59% CaCl2, and 1.37% tryptone. The R[2] value above 97% explains the higher variability and depicts the model's validity. The resulted EPS was a heteropolysaccharide in nature with mannose, glucose, and galactose monosaccharides. FTIR spectrum reflected the presence of functional groups, i.e., O-H, C-H, C = O, C-O-H, and CH2. SEM revealed a porous and rough morphology of EPS, also found to be thermally stable and negligible weight loss, i.e., 14.0% at 257 °C and 35.4% at 292.9 °C was observed in the 1st and 2nd phases, respectively. Rheological attributes revealed that strain NCCP 962 had high viscosity by increasing the EPS concentration, low pH, and temperature with respectable water holding, oil capacities, foaming abilities, and stability. NCCP 962 EPS possessed up to 46.4% reduction in cholesterol concentration in the supernatant. Conclusively, these results suggested that strain NCCP 962 can be used in food processing applications and other medical fields. KEY POINTS: • The fermentation conditions affect EPS yield from L. paraplantarum and significantly increased yield to 0.910 g/L. • The EPS was heteropolysaccharide in nature and thermally stable with amorphous morphology. • Good cholesterol-lowering potential with the best rheological, emulsifying, and foaming capacities.}, }
@article {pmid36679557, year = {2023}, author = {Lupenko, S and Butsiy, R and Shakhovska, N}, title = {Advanced Modeling and Signal Processing Methods in Brain-Computer Interfaces Based on a Vector of Cyclic Rhythmically Connected Random Processes.}, journal = {Sensors (Basel, Switzerland)}, volume = {23}, number = {2}, pages = {}, doi = {10.3390/s23020760}, pmid = {36679557}, issn = {1424-8220}, abstract = {In this study is substantiated the new mathematical model of vector of electroencephalographic signals, registered under the conditions of multiple repetitions of the mental control influences of brain-computer interface operator, in the form of a vector of cyclic rhythmically connected random processes, which, due to taking into account the stochasticity and cyclicity, the variability and commonality of the rhythm of the investigated signals have a number of advantages over the known models. This new model opens the way for the study of multidimensional distribution functions; initial, central, and mixed moment functions of higher order such as for each electroencephalographic signal separately; as well as for their respective compatible probabilistic characteristics, among which the most informative characteristics can be selected. This provides an increase in accuracy in the detection (classification) of mental control influences of the brain-computer interface operators. Based on the developed mathematical model, the statistical processing methods of vector of electroencephalographic signals are substantiated, which consist of statistical evaluation of its probabilistic characteristics and make it possible to conduct an effective joint statistical estimation of the probability characteristics of electroencephalographic signals. This provides the basis for coordinated integration of information from different sensors. The use of moment functions of higher order and their spectral images in the frequency domain, as informative characteristics in brain-computer interface systems, are substantiated. Their significant sensitivity to the mental controlling influence of the brain-computer interface operator is experimentally established. The application of Bessel's inequality to the problems of reducing the dimensions (from 500 to 20 numbers) of the vectors of informative features makes it possible to significantly reduce the computational complexity of the algorithms for the functioning of brain-computer interface systems. Namely, we experimentally established that only the first 20 values of the Fourier transform of the estimation of moment functions of higher-order electroencephalographic signals are sufficient to form the vector of informative features in brain-computer interface systems, because these spectral components make up at least 95% of the total energy of the corresponding statistical estimate of the moment functions of higher-order electroencephalographic signals.}, }
@article {pmid36679501, year = {2023}, author = {Milanés-Hermosilla, D and Trujillo-Codorniú, R and Lamar-Carbonell, S and Sagaró-Zamora, R and Tamayo-Pacheco, JJ and Villarejo-Mayor, JJ and Delisle-Rodriguez, D}, title = {Robust Motor Imagery Tasks Classification Approach Using Bayesian Neural Network.}, journal = {Sensors (Basel, Switzerland)}, volume = {23}, number = {2}, pages = {}, doi = {10.3390/s23020703}, pmid = {36679501}, issn = {1424-8220}, abstract = {The development of Brain-Computer Interfaces based on Motor Imagery (MI) tasks is a relevant research topic worldwide. The design of accurate and reliable BCI systems remains a challenge, mainly in terms of increasing performance and usability. Classifiers based on Bayesian Neural Networks are proposed in this work by using the variational inference, aiming to analyze the uncertainty during the MI prediction. An adaptive threshold scheme is proposed here for MI classification with a reject option, and its performance on both datasets 2a and 2b from BCI Competition IV is compared with other approaches based on thresholds. The results using subject-specific and non-subject-specific training strategies are encouraging. From the uncertainty analysis, considerations for reducing computational cost are proposed for future work.}, }
@article {pmid36676348, year = {2023}, author = {Wen, J and Tang, L and Zhang, S and Zhan, Q and Wang, Y}, title = {Qualitative and Quantitative Investigations on the Failure Effect of Critical Fissures in Rock Specimens under Plane Strain Compression.}, journal = {Materials (Basel, Switzerland)}, volume = {16}, number = {2}, pages = {}, doi = {10.3390/ma16020611}, pmid = {36676348}, issn = {1996-1944}, abstract = {To investigate the failure effects of critical fissures in rock specimens subjected to plane strain compression (PSC), five types of internal fissures in rock specimens were designed and twelve PSC tests were conducted for two lithologies based on the discrete element method (DEM). The results were analyzed in terms of the fracture mode, data characteristics, and crack evolution. The results indicated the following. (1) The rock samples with a critical fissure under PSC showed a weak face shear fracture mode, which was influenced by lithology, fissure angle, and fissure surface direction. (2) There were four critical expansion points (CEPs) of axial stress of the rocks under PSC, which were the stage signs of rock materials from local damage to complete fracture. The rock-bearing capacity index (RockBCI) was further proposed. (3) The bearing capacity of rock samples with horizontal fissures, fissures whose angles coincided with the fracture surface, and fissures whose surface was perpendicular to the lateral confine direction was the worst; their BCI[2] values were found to be 80.6%, 70.8%, and 56.9% of the rock samples without any fissures, respectively. The delayed fracture situation under PSC was identified and analyzed. (4) The crack evolution followed the unified law of localization, and the fissures in the rocks changed the mode of crack development and the path of the deepening and connecting of crack clusters, as well as affecting the time process from damage to collapse. This research innovatively investigated the behavior characteristics of rock samples with a fissure under PSC, and it qualitatively and quantitatively analyzed the bearing capacity of rock mass from local damage to fracture.}, }
@article {pmid36675707, year = {2022}, author = {Ma, Y and Gong, A and Nan, W and Ding, P and Wang, F and Fu, Y}, title = {Personalized Brain-Computer Interface and Its Applications.}, journal = {Journal of personalized medicine}, volume = {13}, number = {1}, pages = {}, doi = {10.3390/jpm13010046}, pmid = {36675707}, issn = {2075-4426}, abstract = {Brain-computer interfaces (BCIs) are a new technology that subverts traditional human-computer interaction, where the control signal source comes directly from the user's brain. When a general BCI is used for practical applications, it is difficult for it to meet the needs of different individuals because of the differences among individual users in physiological and mental states, sensations, perceptions, imageries, cognitive thinking activities, and brain structures and functions. For this reason, it is necessary to customize personalized BCIs for specific users. So far, few studies have elaborated on the key scientific and technical issues involved in personalized BCIs. In this study, we will focus on personalized BCIs, give the definition of personalized BCIs, and detail their design, development, evaluation methods and applications. Finally, the challenges and future directions of personalized BCIs are discussed. It is expected that this study will provide some useful ideas for innovative studies and practical applications of personalized BCIs.}, }
@article {pmid36675486, year = {2023}, author = {Morone, G and Pichiorri, F}, title = {Post-Stroke Rehabilitation: Challenges and New Perspectives.}, journal = {Journal of clinical medicine}, volume = {12}, number = {2}, pages = {}, doi = {10.3390/jcm12020550}, pmid = {36675486}, issn = {2077-0383}, abstract = {A stroke is determined by insufficient blood supply to the brain due to vessel occlusion (ischemic stroke) or rupture (hemorrhagic stroke), resulting in immediate neurological impairment to differing degrees [...].}, }
@article {pmid36672726, year = {2023}, author = {Coelho, HRS and Neves, SCD and Menezes, JNDS and Antoniolli-Silva, ACMB and Oliveira, RJ}, title = {Mesenchymal Stromal Cell Therapy Reverses Detrusor Hypoactivity in a Chronic Kidney Patient.}, journal = {Biomedicines}, volume = {11}, number = {1}, pages = {}, doi = {10.3390/biomedicines11010218}, pmid = {36672726}, issn = {2227-9059}, abstract = {Detrusor hypoactivity (DH) is characterized by low detrusor pressure or a short contraction associated with low urinary flow. This condition can progress to chronic renal failure (CRF) and result in the need for dialysis. The present case report demonstrates that a patient diagnosed with DH and CRF who received two transplants with 2 × 10[6] autologous mesenchymal stromal cells at an interval of 30 days recovered the contractile strength of the bladder and normalized his renal function. The patient had a score of 19 on the ICIQ-SF before cell therapy, and that score was reduced to 1 after transplantation. These results demonstrate that there was an improvement in his voiding function, urinary stream and urine volume as evaluated by urofluxometry. In addition, a urodynamic study carried out after treatment showed an increase in the maximum flow from 2 mL/s to 23 mL/s, the detrusor pressure in the maximum flow from 21 cm H2O to 46 cm H2O and a BCI that went from 31 to 161, characterizing good detrusor contraction. Thus, in the present case, the transplantation of autologous mesenchymal stromal cells proved to be a viable therapeutic option to allow the patient to recover the contractile strength of the bladder, and reversed the CRF.}, }
@article {pmid36672115, year = {2023}, author = {Zhao, ZP and Nie, C and Jiang, CT and Cao, SH and Tian, KX and Yu, S and Gu, JW}, title = {Modulating Brain Activity with Invasive Brain-Computer Interface: A Narrative Review.}, journal = {Brain sciences}, volume = {13}, number = {1}, pages = {}, doi = {10.3390/brainsci13010134}, pmid = {36672115}, issn = {2076-3425}, abstract = {Brain-computer interface (BCI) can be used as a real-time bidirectional information gateway between the brain and machines. In particular, rapid progress in invasive BCI, propelled by recent developments in electrode materials, miniature and power-efficient electronics, and neural signal decoding technologies has attracted wide attention. In this review, we first introduce the concepts of neuronal signal decoding and encoding that are fundamental for information exchanges in BCI. Then, we review the history and recent advances in invasive BCI, particularly through studies using neural signals for controlling external devices on one hand, and modulating brain activity on the other hand. Specifically, regarding modulating brain activity, we focus on two types of techniques, applying electrical stimulation to cortical and deep brain tissues, respectively. Finally, we discuss the related ethical issues concerning the clinical application of this emerging technology.}, }
@article {pmid36672052, year = {2022}, author = {Pepi, C and Mercier, M and Carfì Pavia, G and de Benedictis, A and Vigevano, F and Rossi-Espagnet, MC and Falcicchio, G and Marras, CE and Specchio, N and de Palma, L}, title = {Can Presurgical Interhemispheric EEG Connectivity Predict Outcome in Hemispheric Surgery? A Brain Machine Learning Approach.}, journal = {Brain sciences}, volume = {13}, number = {1}, pages = {}, doi = {10.3390/brainsci13010071}, pmid = {36672052}, issn = {2076-3425}, abstract = {OBJECTIVES: Hemispherotomy (HT) is a surgical option for treatment of drug-resistant seizures due to hemispheric structural lesions. Factors affecting seizure outcome have not been fully clarified. In our study, we used a brain Machine Learning (ML) approach to evaluate the possible role of Inter-hemispheric EEG Connectivity (IC) in predicting post-surgical seizure outcome.<br><br>METHODS: We collected 21 pediatric patients with drug-resistant epilepsy; who underwent HT in our center from 2009 to 2020; with a follow-up of at least two years. We selected 5-s windows of wakefulness and sleep pre-surgical EEG and we trained Artificial Neuronal Network (ANN) to estimate epilepsy outcome. We extracted EEG features as input data and selected the ANN with best accuracy.<br><br>RESULTS: Among 21 patients, 15 (71%) were seizure and drug-free at last follow-up. ANN showed 73.3% of accuracy, with 85% of seizure free and 40% of non-seizure free patients appropriately classified.<br><br>CONCLUSIONS: The accuracy level that we reached supports the hypothesis that pre-surgical EEG features may have the potential to predict epilepsy outcome after HT.<br><br>SIGNIFICANCE: The role of pre-surgical EEG data in influencing seizure outcome after HT is still debated. We proposed a computational predictive model, with an ML approach, with a high accuracy level.}, }
@article {pmid36672050, year = {2022}, author = {Gao, T and Hu, Y and Zhuang, J and Bai, Y and Lu, R}, title = {Repetitive Transcranial Magnetic Stimulation of the Brain Region Activated by Motor Imagery Involving a Paretic Wrist and Hand for Upper-Extremity Motor Improvement in Severe Stroke: A Preliminary Study.}, journal = {Brain sciences}, volume = {13}, number = {1}, pages = {}, doi = {10.3390/brainsci13010069}, pmid = {36672050}, issn = {2076-3425}, abstract = {Approximately two-thirds of stroke survivors experience chronic upper-limb paresis; however, treatment options are limited. Repetitive transcranial magnetic stimulation (rTMS) can enhance motor function recovery in stroke survivors, but its efficacy is controversial. We compared the efficacy of stimulating different targets in 10 chronic stroke patients with severe upper-limb motor impairment. Motor imagery-based brain-computer interface training augmented with virtual reality was used to induce neural activity in the brain region during an imagery task. Participants were then randomly assigned to two groups: an experimental group (received high-frequency rTMS delivered to the brain region activated earlier) and a comparison group (received low-frequency rTMS delivered to the contralesional primary motor cortex). Behavioural metrics and diffusion tensor imaging were compared pre- and post rTMS. After the intervention, participants in both groups improved somewhat. This preliminary study indicates that in chronic stroke patients with severe upper-limb motor impairment, inducing activation in specific brain regions during motor imagery tasks and selecting these regions as a target is feasible. Further studies are needed to explore the efficacy of this intervention.}, }
@article {pmid36672046, year = {2022}, author = {Adama, S and Bogdan, M}, title = {Application of Soft-Clustering to Assess Consciousness in a CLIS Patient.}, journal = {Brain sciences}, volume = {13}, number = {1}, pages = {}, doi = {10.3390/brainsci13010065}, pmid = {36672046}, issn = {2076-3425}, abstract = {Completely locked-in (CLIS) patients are characterized by sufficiently intact cognitive functions, but a complete paralysis that prevents them to interact with their surroundings. On one hand, studies have shown that the ability to communicate plays an important part in these patients' quality of life and prognosis. On the other hand, brain-computer interfaces (BCIs) provide a means for them to communicate using their brain signals. However, one major problem for such patients is the difficulty to determine if they are conscious or not at a specific time. This work aims to combine different sets of features consisting of spectral, complexity and connectivity measures, to increase the probability of correctly estimating CLIS patients' consciousness levels. The proposed approach was tested on data from one CLIS patient, which is particular in the sense that the experimenter was able to point out one time frame Δt during which he was undoubtedly conscious. Results showed that the method presented in this paper was able to detect increases and decreases of the patient's consciousness levels. More specifically, increases were observed during this Δt, corroborating the assertion of the experimenter reporting that the patient was definitely conscious then. Assessing the patients' consciousness is intended as a step prior attempting to communicate with them, in order to maximize the efficiency of BCI-based communication systems.}, }
@article {pmid36672038, year = {2022}, author = {Fu, J and Chen, S and Jia, J}, title = {Sensorimotor Rhythm-Based Brain-Computer Interfaces for Motor Tasks Used in Hand Upper Extremity Rehabilitation after Stroke: A Systematic Review.}, journal = {Brain sciences}, volume = {13}, number = {1}, pages = {}, doi = {10.3390/brainsci13010056}, pmid = {36672038}, issn = {2076-3425}, abstract = {Brain-computer interfaces (BCIs) are becoming more popular in the neurological rehabilitation field, and sensorimotor rhythm (SMR) is a type of brain oscillation rhythm that can be captured and analyzed in BCIs. Previous reviews have testified to the efficacy of the BCIs, but seldom have they discussed the motor task adopted in BCIs experiments in detail, as well as whether the feedback is suitable for them. We focused on the motor tasks adopted in SMR-based BCIs, as well as the corresponding feedback, and searched articles in PubMed, Embase, Cochrane library, Web of Science, and Scopus and found 442 articles. After a series of screenings, 15 randomized controlled studies were eligible for analysis. We found motor imagery (MI) or motor attempt (MA) are common experimental paradigms in EEG-based BCIs trials. Imagining/attempting to grasp and extend the fingers is the most common, and there were multi-joint movements, including wrist, elbow, and shoulder. There were various types of feedback in MI or MA tasks for hand grasping and extension. Proprioception was used more frequently in a variety of forms. Orthosis, robot, exoskeleton, and functional electrical stimulation can assist the paretic limb movement, and visual feedback can be used as primary feedback or combined forms. However, during the recovery process, there are many bottleneck problems for hand recovery, such as flaccid paralysis or opening the fingers. In practice, we should mainly focus on patients' difficulties, and design one or more motor tasks for patients, with the assistance of the robot, FES, or other combined feedback, to help them to complete a grasp, finger extension, thumb opposition, or other motion. Future research should focus on neurophysiological changes and functional improvements and further elaboration on the changes in neurophysiology during the recovery of motor function.}, }
@article {pmid36672034, year = {2022}, author = {Gao, X and Yang, Y and Zhang, F and Zhou, F and Zhu, J and Sun, J and Xu, K and Chen, Y}, title = {A Feature Extraction Method for Seizure Detection Based on Multi-Site Synchronous Changes and Edge Detection Algorithm.}, journal = {Brain sciences}, volume = {13}, number = {1}, pages = {}, doi = {10.3390/brainsci13010052}, pmid = {36672034}, issn = {2076-3425}, abstract = {Automatic detection of epileptic seizures is important in epilepsy control and treatment, and specific feature extraction assists in accurate detection. We developed a feature extraction method for seizure detection based on multi-site synchronous changes and an edge detection algorithm. We investigated five chronic temporal lobe epilepsy rats with 8- and 12-channel detection sites in the hippocampus and limbic system. Multi-site synchronous changes were selected as a specific feature and implemented as a seizure detection method. For preprocessing, we used magnitude-squared coherence maps and Canny edge detection algorithm to find the frequency band with the most significant change in synchronization and the important channel pairs. In detection, we used the maximal cross-correlation coefficient as an indicator of synchronization and the correlation coefficient curves' average value and standard deviation as two detection features. The method achieved high performance, with an average 96.60% detection rate, 2.63/h false alarm rate, and 1.25 s detection delay. The experimental results show that synchronization is an appropriate feature for seizure detection. The magnitude-squared coherence map can assist in selecting a specific frequency band and channel pairs to enhance the detection result. We found that individuals have a specific frequency band that reflects the most significant synchronization changes, and our method can individually adjust parameters and has good detection performance.}, }
@article {pmid36671894, year = {2022}, author = {Xu, M and Zhao, Y and Xu, G and Zhang, Y and Sun, S and Sun, Y and Wang, J and Pei, R}, title = {Recent Development of Neural Microelectrodes with Dual-Mode Detection.}, journal = {Biosensors}, volume = {13}, number = {1}, pages = {}, doi = {10.3390/bios13010059}, pmid = {36671894}, issn = {2079-6374}, abstract = {Neurons communicate through complex chemical and electrophysiological signal patterns to develop a tight information network. A physiological or pathological event cannot be explained by signal communication mode. Therefore, dual-mode electrodes can simultaneously monitor the chemical and electrophysiological signals in the brain. They have been invented as an essential tool for brain science research and brain-computer interface (BCI) to obtain more important information and capture the characteristics of the neural network. Electrochemical sensors are the most popular methods for monitoring neurochemical levels in vivo. They are combined with neural microelectrodes to record neural electrical activity. They simultaneously detect the neurochemical and electrical activity of neurons in vivo using high spatial and temporal resolutions. This paper systematically reviews the latest development of neural microelectrodes depending on electrode materials for simultaneous in vivo electrochemical sensing and electrophysiological signal recording. This includes carbon-based microelectrodes, silicon-based microelectrode arrays (MEAs), and ceramic-based MEAs, focusing on the latest progress since 2018. In addition, the structure and interface design of various types of neural microelectrodes have been comprehensively described and compared. This could be the key to simultaneously detecting electrochemical and electrophysiological signals.}, }
@article {pmid36669202, year = {2023}, author = {Ming, G and Pei, W and Gao, X and Wang, Y}, title = {A high-performance SSVEP-based BCI using imperceptible flickers.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acb50e}, pmid = {36669202}, issn = {1741-2552}, abstract = {Objective.Existing steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) struggle to balance user experience and system performance. This study proposed an individualized space and phase modulation method to code imperceptible flickers at 60 Hz towards a user-friendly SSVEP-based BCI with high performance.Approach.The individualized customization of visual stimulation took the subject-to-subject variability in cortex geometry into account. An annulus global-stimulation was divided into local-stimulations of eight annular sectors and presented to subjects separately. The local-stimulation SSVEPs were superimposed to simulate global-stimulation SSVEPs with 4[7]space and phase coding combinations. A four-class phase-coded BCI diagram was used to evaluate the simulated classification performance. The performance ranking of all simulated global-stimulation SSVEPs were obtained and three performance levels (optimal, medium, worst) of individualized modulation groups were searched for each subject. The standard-modulation group conforming to the V1 'cruciform' geometry and the non-modulation group were involved as controls. A four-target phase-coded BCI system with SSVEPs at 60 Hz was implemented with the five modulation groups and questionnaires were used to evaluate user experience.Main results.The proposed individualized space and phase modulation method effectively modulated the SSVEP intensity without affecting the user experience. The online BCI system using the 60 Hz stimuli achieved mean information transfer rates of 52.8 ± 1.9 bits min[-1], 16.8 ± 2.4 bits min[-1], and 42.4 ± 3.0 bits min[-1]with individualized optimal-modulation, individualized worst-modulation, and non-modulation groups, respectively.Significance.Structural and functional characteristics of the human visual cortex were exploited to enhance the response intensity of SSVEPs at 60 Hz, resulting in a high-performance BCI system with good user experience. This study has important theoretical significance and application value for promoting the development of the visual BCI technology.}, }
@article {pmid36662378, year = {2023}, author = {Li, J and Wang, J and Wang, T and Kong, W and Xi, X}, title = {Quantification of body ownership awareness induced by the visual movement illusion of the lower limbs: a study of electroencephalogram and surface electromyography.}, journal = {Medical &amp; biological engineering &amp; computing}, volume = {}, number = {}, pages = {}, pmid = {36662378}, issn = {1741-0444}, abstract = {The visual movement illusion (VMI) is a subjective experience. This illusion is produced by watching the subject's motion video. At the same time, VMI evokes awareness of body ownership. We applied the power spectral density (PSD) matrix and the partial directed correlation (PDC) matrix to build the PPDC matrix for the γ2 band (34-98.5 Hz), combining cerebral cortical and musculomotor cortical complexity and PPDC to quantify the degree of body ownership. Thirty-five healthy subjects were recruited to participate in this experiment. The subjects' electroencephalography (EEG) and surface electromyography (sEMG) data were recorded under resting conditions, observation conditions, illusion conditions, and actual seated front-kick movements. The results show the following: (1) VMI activates the cerebral cortex to some extent; (2) VMI enhances cortical muscle excitability in the rectus femoris and medial vastus muscles; (3) VMI induces a sense of body ownership; (4) the use of PPDC values, fuzzy entropy values of muscles, and fuzzy entropy values of the cerebral cortex can quantify whether VMI induces awareness of body ownership. These results illustrate that PPDC can be used as a biomarker to show that VMI affects changes in the cerebral cortex and as a quantitative tool to show whether body ownership awareness arises.}, }
@article {pmid36662082, year = {2023}, author = {Karbalaei Akbari, M and Siraj Lopa, N and Shahriari, M and Najafzadehkhoee, A and Galusek, D and Zhuiykov, S}, title = {Functional Two-Dimensional Materials for Bioelectronic Neural Interfacing.}, journal = {Journal of functional biomaterials}, volume = {14}, number = {1}, pages = {}, doi = {10.3390/jfb14010035}, pmid = {36662082}, issn = {2079-4983}, abstract = {Realizing the neurological information processing by analyzing the complex data transferring behavior of populations and individual neurons is one of the fast-growing fields of neuroscience and bioelectronic technologies. This field is anticipated to cover a wide range of advanced applications, including neural dynamic monitoring, understanding the neurological disorders, human brain-machine communications and even ambitious mind-controlled prosthetic implant systems. To fulfill the requirements of high spatial and temporal resolution recording of neural activities, electrical, optical and biosensing technologies are combined to develop multifunctional bioelectronic and neuro-signal probes. Advanced two-dimensional (2D) layered materials such as graphene, graphene oxide, transition metal dichalcogenides and MXenes with their atomic-layer thickness and multifunctional capabilities show bio-stimulation and multiple sensing properties. These characteristics are beneficial factors for development of ultrathin-film electrodes for flexible neural interfacing with minimum invasive chronic interfaces to the brain cells and cortex. The combination of incredible properties of 2D nanostructure places them in a unique position, as the main materials of choice, for multifunctional reception of neural activities. The current review highlights the recent achievements in 2D-based bioelectronic systems for monitoring of biophysiological indicators and biosignals at neural interfaces.}, }
@article {pmid36658415, year = {2023}, author = {Gu, J and Jiang, J and Ge, S and Wang, H}, title = {Capped L21-norm-based common spatial patterns for EEG signals classification applicable to BCI systems.}, journal = {Medical &amp; biological engineering &amp; computing}, volume = {}, number = {}, pages = {}, pmid = {36658415}, issn = {1741-0444}, abstract = {The common spatial patterns (CSP) technique is an effective strategy for the classification of multichannel electroencephalogram (EEG) signals. However, the objective function expression of the conventional CSP algorithm is based on the L2-norm, which makes the performance of the method easily affected by outliers and noise. In this paper, we consider a new extension to CSP, which is termed capped L21-norm-based common spatial patterns (CCSP-L21), by using the capped L21-norm rather than the L2-norm for robust modeling. L21-norm considers the L1-norm sum which largely alleviates the influence of outliers and noise for the sake of robustness. The capped norm is further used to mitigate the effects of extreme outliers whose signal amplitude is much higher than that of the normal signal. Moreover, a non-greedy iterative procedure is derived to solve the proposed objective function. The experimental results show that the proposed method achieves the highest average recognition rates on the three real data sets of BCI competitions, which are 91.67%, 85.07%, and 82.04%, respectively. Capped L21-norm-based common spatial patterns-a robust model for EEG signals classification.}, }
@article {pmid36657633, year = {2023}, author = {Perez-Garcia, G and Bicak, M and Haure-Mirande, JV and Perez, GM and Otero-Pagan, A and Gama Sosa, MA and De Gasperi, R and Sano, M and Barlow, C and Gage, FH and Readhead, B and Ehrlich, ME and Gandy, S and Elder, GA}, title = {BCI-838, an orally active mGluR2/3 receptor antagonist pro-drug, rescues learning behavior deficits in the PS19 MAPT[P301S] mouse model of tauopathy.}, journal = {Neuroscience letters}, volume = {}, number = {}, pages = {137080}, doi = {10.1016/j.neulet.2023.137080}, pmid = {36657633}, issn = {1872-7972}, abstract = {Tauopathies are a heterogeneous group of neurodegenerative disorders that are clinically and pathologically distinct from Alzheimer's disease (AD) having tau inclusions in neurons and/or glia as their most prominent neuropathological feature. BCI-838 (MGS00210) is a group II metabotropic glutamate receptor (mGluR2/3) antagonist pro-drug. Previously, we reported that orally administered BCI-838 improved learning behavior and reduced anxiety in Dutch (APP[E693Q]) transgenic mice, a model of the pathological accumulation of Aβ oligomers found in AD. Herein, we investigated effects of BCI-838 on PS19 male mice that express the tauopathy mutation MAPT[P301S] associated with human frontotemporal lobar degeneration (FTLD). These mice develop an aging-related tauopathy without amyloid accumulation. Mice were divided into three experimental groups: (1) non-transgenic wild type mice treated with vehicle, (2) PS19 mice treated with vehicle and (3) PS19 mice treated with 5 mg/kg BCI-838. Groups of 10-13 mice were utilized. Vehicle or BCI-838 was administered by oral gavage for 4 weeks. Behavioral testing consisting of a novel object recognition task was conducted after drug administration. Two studies were performed beginning treatment of mice at 3 or 7 months of age. One month of BCI-838 treatment rescued deficits in recognition memory in PS19 mice whether treatment was begun at 3 or 7 months of age. These studies extend the potential utility of BCI-838 to neurodegenerative conditions that have tauopathy as their underlying basis. They also suggest an mGluR2/3 dependent mechanism as a basis for the behavioral deficits in PS19 mice.}, }
@article {pmid36657242, year = {2023}, author = {Proverbio, AM and Tacchini, M and Jiang, K}, title = {What do you have in mind? ERP markers of visual and auditory imagery.}, journal = {Brain and cognition}, volume = {166}, number = {}, pages = {105954}, doi = {10.1016/j.bandc.2023.105954}, pmid = {36657242}, issn = {1090-2147}, abstract = {This study aimed to investigate the psychophysiological markers of imagery processes through EEG/ERP recordings. Visual and auditory stimuli representing 10 different semantic categories were shown to 30 healthy participants. After a given interval and prompted by a light signal, participants were asked to activate a mental image corresponding to the semantic category for recording synchronized electrical potentials. Unprecedented electrophysiological markers of imagination were recorded in the absence of sensory stimulation. The following peaks were identified at specific scalp sites and latencies, during imagination of infants (centroparietal positivity, CPP, and late CPP), human faces (anterior negativity, AN), animals (anterior positivity, AP), music (P300-like), speech (N400-like), affective vocalizations (P2-like) and sensory (visual vs auditory) modality (PN300). Overall, perception and imagery conditions shared some common electro/cortical markers, but during imagery the category-dependent modulation of ERPs was long latency and more anterior, with respect to the perceptual condition. These ERP markers might be precious tools for BCI systems (pattern recognition, classification, or A.I. algorithms) applied to patients affected by consciousness disorders (e.g., in a vegetative or comatose state) or locked-in-patients (e.g., spinal or SLA patients).}, }
@article {pmid36656873, year = {2023}, author = {Pang, J and Peng, S and Hou, C and Zhao, H and Fan, Y and Ye, C and Zhang, N and Wang, T and Cao, Y and Zhou, W and Sun, D and Wang, K and Rümmeli, MH and Liu, H and Cuniberti, G}, title = {Applications of Graphene in Five Senses, Nervous System, and Artificial Muscles.}, journal = {ACS sensors}, volume = {}, number = {}, pages = {}, doi = {10.1021/acssensors.2c02790}, pmid = {36656873}, issn = {2379-3694}, abstract = {Graphene remains of great interest in biomedical applications because of biocompatibility. Diseases relating to human senses interfere with life satisfaction and happiness. Therefore, the restoration by artificial organs or sensory devices may bring a bright future by the recovery of senses in patients. In this review, we update the most recent progress in graphene based sensors for mimicking human senses such as artificial retina for image sensors, artificial eardrums, gas sensors, chemical sensors, and tactile sensors. The brain-like processors are discussed based on conventional transistors as well as memristor related neuromorphic computing. The brain-machine interface is introduced for providing a single pathway. Besides, the artificial muscles based on graphene are summarized in the means of actuators in order to react to the physical world. Future opportunities remain for elevating the performances of human-like sensors and their clinical applications.}, }
@article {pmid36655886, year = {2022}, author = {Breen, JR and Pensini, P}, title = {Grounded by Mother Nature's Revenge.}, journal = {Experimental psychology}, volume = {69}, number = {5}, pages = {284-294}, doi = {10.1027/1618-3169/a000566}, pmid = {36655886}, issn = {2190-5142}, abstract = {Leisure air travel is a popular form of tourism, but its emissions are a major contributor to anthropogenic climate change. Restrictions to leisure air travel have previously received little support; however, the same restrictions to mitigate the spread of COVID-19 have been popular. This support is unlikely to persist in a postpandemic world, highlighting the need for alternative ways to improve support for reducing leisure air travel. Anthropomorphism of nature has consistently predicted proenvironmental behavior, which has been mediated by guilt felt for harm to the environment. This research is the first empirical study to explore this relationship in the context of COVID-19, where it examined support for restricting leisure air travel to help mitigate (1) COVID-19 and (2) climate change. In an experimental online study, Australian residents (N = 325, Mage = 54.48, SDage = 14.63, 62% women) were recruited through social media. Anthropomorphism of nature in the context of COVID-19 (AMP-19) was manipulated through exposure to a news article. Participants then completed measures of environmental guilt and support for restricting leisure air travel to mitigate COVID-19 (LAT-19) and to mitigate climate change (LAT-CC). A significant indirect effect was observed in both models, such that AMP-19 predicted environmental guilt which in turn predicted LAT-19 (f[2] = .26; BCI [0.66, 3.87]) and LAT-CC (f[2] = .45; BCI [0.84, 5.06]). The results imply that anthropomorphism of nature in the context of COVID-19 can improve attitudes toward this proenvironmental behavior, with greater support when this was to mitigate climate change. Implications are discussed.}, }
@article {pmid36654858, year = {2023}, author = {Jin, S and Chen, X and Zheng, H and Cai, W and Lin, X and Kong, X and Ni, Y and Ye, J and Li, X and Shen, L and Guo, B and Abdelrahman, Z and Zhou, S and Mao, S and Wang, Y and Yao, C and Gu, X and Yu, B and Wang, Z and Wang, X}, title = {Downregulation of UBE4B promotes CNS axon regrowth and functional recovery after stroke.}, journal = {iScience}, volume = {26}, number = {1}, pages = {105885}, pmid = {36654858}, issn = {2589-0042}, abstract = {The limited intrinsic regrowth capacity of corticospinal axons impedes functional recovery after cortical stroke. Although the mammalian target of rapamycin (mTOR) and p53 pathways have been identified as the key intrinsic pathways regulating CNS axon regrowth, little is known about the key upstream regulatory mechanism by which these two major pathways control CNS axon regrowth. By screening genes that regulate ubiquitin-mediated degradation of the p53 proteins in mice, we found that ubiquitination factor E4B (UBE4B) represses axonal regrowth in retinal ganglion cells and corticospinal neurons. We found that axonal regrowth induced by UBE4B depletion depended on the cooperative activation of p53 and mTOR. Importantly, overexpression of UbV.E4B, a competitive inhibitor of UBE4B, in corticospinal neurons promoted corticospinal axon sprouting and facilitated the recovery of corticospinal axon-dependent function in a cortical stroke model. Thus, our findings provide a translatable strategy for restoring corticospinal tract-dependent functions after cortical stroke.}, }
@article {pmid36654371, year = {2021}, author = {Zhu, J and Chen, F and Luo, L and Wu, W and Dai, J and Zhong, J and Lin, X and Chai, C and Ding, P and Liang, L and Wang, S and Ding, X and Chen, Y and Wang, H and Qiu, J and Wang, F and Sun, C and Zeng, Y and Fang, J and Jiang, X and Liu, P and Tang, G and Qiu, X and Zhang, X and Ruan, Y and Jiang, S and Li, J and Zhu, S and Xu, X and Li, F and Liu, Z and Cao, G and Chen, D}, title = {Single-cell atlas of domestic pig cerebral cortex and hypothalamus.}, journal = {Science bulletin}, volume = {66}, number = {14}, pages = {1448-1461}, doi = {10.1016/j.scib.2021.04.002}, pmid = {36654371}, issn = {2095-9281}, abstract = {The brain of the domestic pig (Sus scrofa domesticus) has drawn considerable attention due to its high similarities to that of humans. However, the cellular compositions of the pig brain (PB) remain elusive. Here we investigated the single-nucleus transcriptomic profiles of five regions of the PB (frontal lobe, parietal lobe, temporal lobe, occipital lobe, and hypothalamus) and identified 21 cell subpopulations. The cross-species comparison of mouse and pig hypothalamus revealed the shared and specific gene expression patterns at the single-cell resolution. Furthermore, we identified cell types and molecular pathways closely associated with neurological disorders, bridging the gap between gene mutations and pathogenesis. We reported, to our knowledge, the first single-cell atlas of domestic pig cerebral cortex and hypothalamus combined with a comprehensive analysis across species, providing extensive resources for future research regarding neural science, evolutionary developmental biology, and regenerative medicine.}, }
@article {pmid36652620, year = {2023}, author = {Abbasi, J and Suran, M}, title = {From Thought to Text: How an Endovascular Brain-Computer Interface Could Help Patients With Severe Paralysis Communicate.}, journal = {JAMA}, volume = {}, number = {}, pages = {}, doi = {10.1001/jama.2022.24343}, pmid = {36652620}, issn = {1538-3598}, }
@article {pmid36652475, year = {2023}, author = {Liang, W and Balasubramanian, K and Papadourakis, V and Hatsopoulos, NG}, title = {Propagating spatiotemporal activity patterns across macaque motor cortex carry kinematic information.}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, volume = {120}, number = {4}, pages = {e2212227120}, doi = {10.1073/pnas.2212227120}, pmid = {36652475}, issn = {1091-6490}, support = {NIH R01 NS111982/GF/NIH HHS/United States ; }, abstract = {Propagating spatiotemporal neural patterns are widely evident across sensory, motor, and association cortical areas. However, it remains unclear whether any characteristics of neural propagation carry information about specific behavioral details. Here, we provide the first evidence for a link between the direction of cortical propagation and specific behavioral features of an upcoming movement on a trial-by-trial basis. We recorded local field potentials (LFPs) from multielectrode arrays implanted in the primary motor cortex of two rhesus macaque monkeys while they performed a 2D reach task. Propagating patterns were extracted from the information-rich high-gamma band (200 to 400 Hz) envelopes in the LFP amplitude. We found that the exact direction of propagating patterns varied systematically according to initial movement direction, enabling kinematic predictions. Furthermore, characteristics of these propagation patterns provided additional predictive capability beyond the LFP amplitude themselves, which suggests the value of including mesoscopic spatiotemporal characteristics in refining brain-machine interfaces.}, }
@article {pmid36650644, year = {2023}, author = {Jiang, J and Fu, Y and Tang, A and Gao, X and Zhang, D and Shen, Y and Mou, T and Hu, S and Gao, J and Lai, J}, title = {Sex difference in prebiotics on gut and blood-brain barrier dysfunction underlying stress-induced anxiety and depression.}, journal = {CNS neuroscience &amp; therapeutics}, volume = {}, number = {}, pages = {}, doi = {10.1111/cns.14091}, pmid = {36650644}, issn = {1755-5949}, abstract = {BACKGROUND: Most of the previous studies have demonstrated the potential antidepressive and anxiolytic role of prebiotic supplement in male subjects, yet few have females enrolled. Herein, we explored whether prebiotics administration during chronic stress prevented depression-like and anxiety-like behavior in a sex-specific manner and the mechanism of behavioral differences caused by sex.<br><br>METHODS: Female and male C57 BL/J mice on normal diet were supplemented with or without a combination of fructo-oligosaccharides (FOS) and galacto-oligosaccharides (GOS) during 3- and 4-week chronic restraint stress (CRS) treatment, respectively. C57 BL/J mice on normal diet without CRS were used as controls. Behavior consequences, gut microbiota, dysfunction of gut and brain-blood barriers, and inflammatory profiles were measured.<br><br>RESULTS: In the 3rd week, FOS&#x2009;+&#x2009;GOS administration attenuated stress-induced anxiety-like behavior in female, but not in male mice, and the anxiolytic effects in males were observed until the 4th week. However, protective effects of prebiotics on CRS-induced depression were not observed. Changes in the gene expression of tight junction proteins in the distal colon and hippocampus, and decreased number of colon goblet cells following CRS were restored by prebiotics only in females. In both female and male mice, prebiotics alleviated stress-induced BBB dysfunction and elevation in pro-inflammatory cytokines levels, and modulated gut microbiota caused by stress. Furthermore, correlation analysis revealed that anxiety-like behaviors were significantly correlated with levels of pro-inflammatory cytokines and gene expression of tight junction proteins in the hippocampus of female mice, and the abundance of specific gut microbes was also correlated with anxiety-like behaviors, pro-inflammatory cytokines, and gene expression of tight junction proteins in the hippocampus of female mice.<br><br>CONCLUSION: Female mice were more vulnerable to stress and prebiotics than males. The gut microbiota, gut and blood-brain barrier, and inflammatory response may mediate the protective effects of prebiotics on anxiety-like behaviors in female mice.}, }
@article {pmid36607323, year = {2023}, author = {Zhang, Y and Schriver, KE and Hu, JM and Roe, AW}, title = {Spatial frequency representation in V2 and V4 of macaque monkey.}, journal = {eLife}, volume = {12}, number = {}, pages = {}, doi = {10.7554/eLife.81794}, pmid = {36607323}, issn = {2050-084X}, abstract = {Spatial frequency (SF) is an important attribute in the visual scene and is a defining feature of visual processing channels. However, there remain many unsolved questions about how extrastriate areas in primate visual cortex code this fundamental information. Here, using intrinsic signal optical imaging in visual areas of V2 and V4 of macaque monkeys, we quantify the relationship between SF maps and (1) visual topography and (2) color and orientation maps. We find that in orientation regions, low to high SF is mapped orthogonally to orientation; in color regions, which are reported to contain orthogonal axes of color and lightness, low SFs tend to be represented more frequently than high SFs. This supports a population-based SF fluctuation related to the 'color/orientation' organizations. We propose a generalized hypercolumn model across cortical areas, comprised of two orthogonal parameters with additional parameters.}, }
@article {pmid36650410, year = {2023}, author = {Zhang, J and Wang, X and Xu, B and Wu, Y and Lou, X and Shen, X}, title = {An overview of methods of left and right foot motor imagery based on Tikhonov regularisation common spatial pattern.}, journal = {Medical &amp; biological engineering &amp; computing}, volume = {}, number = {}, pages = {}, pmid = {36650410}, issn = {1741-0444}, abstract = {The motor imagery brain-computer interface (MI-BCI) provides an interactive control channel for spinal cord injury patients. However, the limitations of feature extraction algorithms may lead to low accuracy and instability in decoding electroencephalogram (EEG) signals. In this study, we examined the classification performance of an MI-BCI system by focusing on the distinction of the left and right foot kinaesthetic motor imagery tasks in five subjects. Feature extraction was performed using the common space pattern (CSP) and the Tikhonov regularisation CSP (TRCSP) spatial filters. TRCSP overcomes the CSP problems of noise sensitivity and overfitting. Moreover, support vector machine (SVM) and linear discriminant analysis (LDA) were used for classification and recognition. We constructed four combined classification methods (TRCSP-SVM, TRCSP-LDA, CSP-SVM, and CSP-LDA) and evaluated them by comparing their accuracies, kappa coefficients, and receiver operating characteristic (ROC) curves. The results showed that the TRCSP-SVM method performed significantly better than others (average accuracy 97%, average kappa coefficient 0.91, and average area under ROC curve (AUC) 0.98). Using TRCSP instead of standard CSP improved accuracy by up to 10%. This study provides insights into the classification of EEG signals. The results of this study can aid lower limb MI-BCI systems in rehabilitation training.}, }
@article {pmid36645915, year = {2023}, author = {Li, M and Zuo, H and Zhou, H and Xu, G and Qi, E}, title = {A study of action difference on motor imagery based on delayed matching posture task.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acb386}, pmid = {36645915}, issn = {1741-2552}, abstract = {OBJECTIVE: Motor imagery (MI)-based brain-computer interfaces (BCI) provide an additional control pathway for people by decoding the intention of action imagination. The way people imagine greatly affects MI-BCI performance. Action itself is one of the factors that influence the way people imagine. Whether the different actions cause a difference in the MI performance is unknown. What is more important is how to manifest this action difference in the process of imagery, which has the potential to guide people to use their individualized actions to imagine more effectively.<br><br>APPROACH: To explore action differences, this study proposes a novel paradigm named as Action Observation based Delayed Matching Posture Task (AO-DMPT). Ten subjects are required to observe, memorize, match, and imagine three types of actions (cutting, grasping and writing) given by visual images or videos, to accomplish the phases of encoding, retrieval and reinforcement of MI. Event-related potential (ERP), MI features, and classification accuracy of the left or the right hand are used to evaluate the effect of the action difference on the MI difference.<br><br>MAIN RESULTS: Action differences cause different feature distributions, resulting in that the accuracy with high event-related (de)synchronization (ERD/ERS) is 27.75% higher than the ones with low ERD/ERS (p<0.05), which indicates that the action difference has impact on the MI difference and the BCI performance. In addition, significant differences in the ERP amplitudes exists among the three actions: the amplitude of P300-N200 potential reaches 9.28&#x3bc;V of grasping, 5.64&#x3bc;V and 5.25&#x3bc;V higher than the cutting and the writing, respectively (p<0.05).<br><br>SIGNIFICANCE: The ERP amplitudes derived from the supplementary motor area shows positive correlation to the MI classification accuracy, implying that the ERP might be an index of the MI performance when the people is faced with action selection. This study demonstrates that the MI difference is related to the action difference, and can be manifested by the ERP, which is important for improving MI training by selecting suitable action; the relationship between the ERP and the MI provides a novel index to find the suitable action to set up an individualized BCI and improve the performance further.}, }
@article {pmid36645913, year = {2023}, author = {Valencia, D and Leone, G and Keller, N and Mercier, PP and Alimohammad, A}, title = {Power-efficient in vivo brain-machine interfaces via brain-state estimation.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acb385}, pmid = {36645913}, issn = {1741-2552}, abstract = {OBJECTIVE: Advances in brain-machine interfaces (BMIs) can potentially improve the quality of life of millions of users with spinal cord injury or other neurological disorders by allowing them to interact with the physical environment at their will.<br><br>APPROACH: To reduce the power consumption of the brain-implanted interface, this article presents the first hardware realization of an in vivo intention-aware interface via brain-state estimation.<br><br>MAIN RESULTS: It is shown that incorporating brain-state estimation reduces the in vivo power consumption and reduces total energy dissipation by over 1.8x compared to those of the current systems, enabling longer batter life for implanted circuits. The synthesized application-specific integrated circuit (ASIC) of the designed intention-aware multi-unit spike detection system in a standard 180-nm CMOS process occupies 0.03 mm[2]of silicon area and consumes 0.63 &#x3bc;W of power per channel, which is the least power consumption among the current in vivo ASIC realizations.<br><br>SIGNIFICANCE: The proposed interface is the first practical approach towards realizing asynchronous BMIs while reducing the power consumption of the BMI interface and enhancing neural decoding performance compared to those of the conventional synchronous BMIs.}, }
@article {pmid36644311, year = {2022}, author = {Sui, Y and Yu, H and Zhang, C and Chen, Y and Jiang, C and Li, L}, title = {Deep brain-machine interfaces: sensing and modulating the human deep brain.}, journal = {National science review}, volume = {9}, number = {10}, pages = {nwac212}, pmid = {36644311}, issn = {2053-714X}, abstract = {Different from conventional brain-machine interfaces that focus more on decoding the cerebral cortex, deep brain-machine interfaces enable interactions between external machines and deep brain structures. They sense and modulate deep brain neural activities, aiming at function restoration, device control and therapeutic improvements. In this article, we provide an overview of multiple deep brain recording and stimulation techniques that can serve as deep brain-machine interfaces. We highlight two widely used interface technologies, namely deep brain stimulation and stereotactic electroencephalography, for technical trends, clinical applications and brain connectivity research. We discuss the potential to develop closed-loop deep brain-machine interfaces and achieve more effective and applicable systems for the treatment of neurological and psychiatric disorders.}, }
@article {pmid36643889, year = {2023}, author = {Alharbi, H}, title = {Identifying Thematics in a Brain-Computer Interface Research.}, journal = {Computational intelligence and neuroscience}, volume = {2023}, number = {}, pages = {2793211}, pmid = {36643889}, issn = {1687-5273}, abstract = {This umbrella review is motivated to understand the shift in research themes on brain-computer interfacing (BCI) and it determined that a shift away from themes that focus on medical advancement and system development to applications that included education, marketing, gaming, safety, and security has occurred. The background of this review examined aspects of BCI categorisation, neuroimaging methods, brain control signal classification, applications, and ethics. The specific area of BCI software and hardware development was not examined. A search using One Search was undertaken and 92 BCI reviews were selected for inclusion. Publication demographics indicate the average number of authors on review papers considered was 4.2 ± 1.8. The results also indicate a rapid increase in the number of BCI reviews from 2003, with only three reviews before that period, two in 1972, and one in 1996. While BCI authors were predominantly Euro-American in early reviews, this shifted to a more global authorship, which China dominated by 2020-2022. The review revealed six disciplines associated with BCI systems: life sciences and biomedicine (n = 42), neurosciences and neurology (n = 35), and rehabilitation (n = 20); (2) the second domain centred on the theme of functionality: computer science (n = 20), engineering (n = 28) and technology (n = 38). There was a thematic shift from understanding brain function and modes of interfacing BCI systems to more applied research novel areas of research-identified surround artificial intelligence, including machine learning, pre-processing, and deep learning. As BCI systems become more invasive in the lives of "normal" individuals, it is expected that there will be a refocus and thematic shift towards increased research into ethical issues and the need for legal oversight in BCI application.}, }
@article {pmid36534700, year = {2022}, author = {Zhang, L and Liu, C and Zhou, X and Zhou, H and Luo, S and Wang, Q and Yao, Z and Chen, JF}, title = {Neural representation and modulation of volitional motivation in response to escalating efforts.}, journal = {The Journal of physiology}, volume = {}, number = {}, pages = {}, doi = {10.1113/JP283915}, pmid = {36534700}, issn = {1469-7793}, abstract = {Task-dependent volitional control of the selected neural activity in the cortex is critical to neuroprosthetic learning to achieve reliable and robust control of the external device. The volitional control of neural activity is driven by a motivational factor (volitional motivation), which directly reinforces the target neurons via real-time biofeedback. However, in the absence of motor behaviour, how do we evaluate volitional motivation? Here, we defined the criterion (ΔF/F) of the calcium fluorescence signal in a volitionally controlled neural task, then escalated the efforts by progressively increasing the number of reaching the criterion or holding time after reaching the criterion. We devised calcium-based progressive threshold-crossing events (termed 'Calcium PTE') and calcium-based progressive threshold-crossing holding-time (termed 'Calcium PTH') for quantitative assessment of volitional motivation in response to progressively escalating efforts. Furthermore, we used this novel neural representation of volitional motivation to explore the neural circuit and neuromodulator bases for volitional motivation. As with behavioural motivation, chemogenetic activation and pharmacological blockade of the striatopallidal pathway decreased and increased, respectively, the breakpoints of the 'Calcium PTE' and 'Calcium PTH' in response to escalating efforts. Furthermore, volitional and behavioural motivation shared similar dopamine dynamics in the nucleus accumbens in response to trial-by-trial escalating efforts. In general, the development of a neural representation of volitional motivation may open a new avenue for smooth and effective control of brain-machine interface tasks. KEY POINTS: Volitional motivation is quantitatively evaluated by M1 neural activity in response to progressively escalating volitional efforts. The striatopallidal pathway and adenosine A2A receptor modulate volitional motivation in response to escalating efforts. Dopamine dynamics encode prediction signal for reward in response to repeated escalating efforts during motor and volitional conditioning. Mice learn to modulate neural activity to compensate for repeated escalating efforts in volitional control.}, }
@article {pmid36639665, year = {2023}, author = {Pichiorri, F and Toppi, J and de Seta, V and Colamarino, E and Masciullo, M and Tamburella, F and Lorusso, M and Cincotti, F and Mattia, D}, title = {Exploring high-density corticomuscular networks after stroke to enable a hybrid Brain-Computer Interface for hand motor rehabilitation.}, journal = {Journal of neuroengineering and rehabilitation}, volume = {20}, number = {1}, pages = {5}, pmid = {36639665}, issn = {1743-0003}, abstract = {BACKGROUND: Brain-Computer Interfaces (BCI) promote upper limb recovery in stroke patients reinforcing motor related brain activity (from electroencephalogaphy, EEG). Hybrid BCIs which include peripheral signals (electromyography, EMG) as control features could be employed to monitor post-stroke motor abnormalities. To ground the use of corticomuscular coherence (CMC) as a hybrid feature for a rehabilitative BCI, we analyzed high-density CMC networks (derived from multiple EEG and EMG channels) and their relation with upper limb motor deficit by comparing data from stroke patients with healthy participants during simple hand tasks.<br><br>METHODS: EEG (61 sensors) and EMG (8 muscles per arm) were simultaneously recorded from 12 stroke (EXP) and 12 healthy participants (CTRL) during simple hand movements performed with right/left (CTRL) and unaffected/affected hand (EXP, UH/AH). CMC networks were estimated for each movement and their properties were analyzed by means of indices derived ad-hoc from graph theory and compared among groups.<br><br>RESULTS: Between-group analysis showed that CMC weight of the whole brain network was significantly reduced in patients during AH movements. The network density was increased especially for those connections entailing bilateral non-target muscles. Such reduced muscle-specificity observed in patients was confirmed by muscle degree index (connections per muscle) which indicated a connections' distribution among non-target and contralateral muscles and revealed a higher involvement of proximal muscles in patients. CMC network properties correlated with upper-limb motor impairment as assessed by Fugl-Meyer Assessment and Manual Muscle Test in patients.<br><br>CONCLUSIONS: High-density CMC networks can capture motor abnormalities in stroke patients during simple hand movements. Correlations with upper limb motor impairment support their use in a BCI-based rehabilitative approach.}, }
@article {pmid36639237, year = {2023}, author = {Rubin, DB and Ajiboye, AB and Barefoot, L and Bowker, M and Cash, SS and Chen, D and Donoghue, JP and Eskandar, EN and Friehs, G and Grant, C and Henderson, JM and Kirsch, RF and Marujo, R and Masood, M and Mernoff, ST and Miller, JP and Mukand, JA and Penn, RD and Shefner, J and Shenoy, KV and Simeral, JD and Sweet, JA and Walter, BL and Williams, ZM and Hochberg, LR}, title = {Interim Safety Profile From the Feasibility Study of the BrainGate Neural Interface System.}, journal = {Neurology}, volume = {}, number = {}, pages = {}, doi = {10.1212/WNL.0000000000201707}, pmid = {36639237}, issn = {1526-632X}, abstract = {BACKGROUND AND OBJECTIVES: Brain computer interfaces (BCIs) are being developed to restore mobility, communication, and functional independence to people with paralysis. Though supported by decades of preclinical data, the safety of chronically implanted microelectrode array BCIs in humans is unknown. We report safety results from the prospective, open-label, non-randomized BrainGate feasibility study (NCT00912041), the largest and longest-running clinical trial of an implanted BCI.<br><br>METHODS: Adults aged 18-75 with quadriparesis from spinal cord injury, brainstem stroke, or motor neuron disease were enrolled through seven clinical sites in the United States. Participants underwent surgical implantation of one or two microelectrode arrays in the motor cortex of the dominant cerebral hemisphere. The primary safety outcome was device-related serious adverse events requiring device explanation or resulting in death or permanently increased disability during the one-year post-implant evaluation period. Secondary outcomes include the type and frequency of other adverse events as well as the feasibility of the BrainGate system for controlling a computer or other assistive technologies.<br><br>RESULTS: From 2004 - 2021, fourteen adults enrolled in the BrainGate trial had devices surgically implanted. The average duration of device implantation was 872 days, yielding 12,203 days of safety experience. There were 68 device-related adverse events, including 6 device-related serious adverse events. The most common device-related adverse event was skin irritation around the percutaneous pedestal. There were no safety events that required device explantation, no unanticipated adverse device events, no intracranial infections, and no participant deaths or adverse events resulting in permanently increased disability related to the investigational device.<br><br>DISCUSSION: The BrainGate Neural Interface system has a safety record comparable to other chronically implanted medical devices. Given rapid recent advances in this technology and continued performance gains, these data suggest a favorable risk/benefit ratio in appropriately selected individuals to support ongoing research and development.<br><br>ClinicalTrials.gov Identifier: NCT00912041.<br><br>CLASSIFICATION OF EVIDENCE: This study provides Class IV evidence that the neurosurgically placed BrainGate Neural Interface system is associated with a low rate of SAEs defined as those requiring device explanation, resulting in death, or resulting in permanently increased disability during the one-year post implant period.}, }
@article {pmid36638268, year = {2023}, author = {Wu, J and Chen, C and Qin, C and Li, Y and Jiang, N and Yuan, Q and Duan, Y and Liu, M and Wei, X and Yu, Y and Zhuang, L and Wang, P}, title = {Mimicking the Biological Sense of Taste In Vitro Using a Taste Organoids-on-a-Chip System.}, journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)}, volume = {}, number = {}, pages = {e2206101}, doi = {10.1002/advs.202206101}, pmid = {36638268}, issn = {2198-3844}, abstract = {Thanks to the gustatory system, humans can experience the flavors in foods and drinks while avoiding the intake of some harmful substances. Although great advances in the fields of biotechnology, microfluidics, and nanotechnologies have been made in recent years, this astonishing recognition system can hardly be replaced by any artificial sensors designed so far. Here, taste organoids are coupled with an extracellular potential sensor array to form a novel bioelectronic organoid and developed a taste organoids-on-a-chip system (TOS) for highly mimicking the biological sense of taste ex vivo with high stability and repeatability. The taste organoids maintain key taste receptors expression after the third passage and high cell viability during 7 days of on-chip culture. Most importantly, the TOS not only distinguishs sour, sweet, bitter, and salt stimuli with great specificity, but also recognizes varying concentrations of the stimuli through an analytical method based on the extraction of signal features and principal component analysis. It is hoped that this bioelectronic tongue can facilitate studies in food quality controls, disease modelling, and drug screening.}, }
@article {pmid36637269, year = {2023}, author = {Hu, J and Wang, Y and Zhu, Y and Li, Y and Chen, J and Zhang, Y and Xu, D and Bai, R and Wang, L}, title = {Preoperative Brain Functional Connectivity Improve Predictive Accuracy of Outcomes After Revascularization in Moyamoya Disease.}, journal = {Neurosurgery}, volume = {92}, number = {2}, pages = {344-352}, doi = {10.1227/neu.0000000000002205}, pmid = {36637269}, issn = {1524-4040}, abstract = {BACKGROUND: In patients with moyamoya disease (MMD), focal impairments in cerebral hemodynamics are often inconsistent with patients' clinical prognoses. Evaluation of entire brain functional networks may enable predicting MMD outcomes after revascularization.<br><br>OBJECTIVE: To investigate whether preoperative brain functional connectivity could predict outcomes after revascularization in MMD.<br><br>METHODS: We included 34 patients with MMD who underwent preoperative MRI scanning and combined revascularization surgery. We used region of interest analyses to explore the differences in functional connectivity for 90 paired brain regions between patients who had favorable outcomes 1 year after surgery (no recurrent stroke, with improved preoperative symptoms, or modified Rankin Scale [mRS]) and those who had unimproved outcomes (recurrent stroke, persistent symptoms, or declined mRS). Variables, including age, body mass index, mRS at admission, Suzuki stage, posterior cerebral artery involvement, and functional connectivity with significant differences between the groups, were included in the discriminant function analysis to predict patient outcomes.<br><br>RESULTS: Functional connectivity between posterior cingulate cortex and paracentral lobule within the right hemisphere, and interhemispheric connection between superior parietal gyrus and middle frontal gyrus, precuneus and middle cingulate cortex, cuneus and precuneus, differed significantly between the groups (P < .001, false discovery rate corrected) and had the greatest discriminant function in the prediction model. Although clinical characteristics of patients with MMD showed great accuracy in predicting outcomes (64.7%), adding information on functional connections improved accuracy to 91.2%.<br><br>CONCLUSION: Preoperative functional connectivity derived from rs-fMRI may be an early hallmark for predicting patients' prognosis after revascularization surgery for MMD.}, }
@article {pmid36636754, year = {2023}, author = {Hudson, HM and Guggenmos, DJ and Azin, M and Vitale, N and McKenzie, KA and Mahnken, JD and Mohseni, P and Nudo, RJ}, title = {Broad Therapeutic Time Window for Driving Motor Recovery After TBI Using Activity-Dependent Stimulation.}, journal = {Neurorehabilitation and neural repair}, volume = {}, number = {}, pages = {15459683221145144}, doi = {10.1177/15459683221145144}, pmid = {36636754}, issn = {1552-6844}, abstract = {BACKGROUND: After an acquired injury to the motor cortex, the ability to generate skilled movements is impaired, leading to long-term motor impairment and disability. While rehabilitative therapy can improve outcomes in some individuals, there are no treatments currently available that are able to fully restore lost function.<br><br>OBJECTIVE: We previously used activity-dependent stimulation (ADS), initiated immediately after an injury, to drive motor recovery. The objective of this study was to determine if delayed application of ADS would still lead to recovery and if the recovery would persist after treatment was stopped.<br><br>METHODS: Rats received a controlled cortical impact over primary motor cortex, microelectrode arrays were implanted in ipsilesional premotor and somatosensory areas, and a custom brain-machine interface was attached to perform the ADS. Stimulation was initiated either 1, 2, or 3&#x2009;weeks after injury and delivered constantly over a 4-week period. An additional group was monitored for 8&#x2009;weeks after terminating ADS to assess persistence of effect. Results were compared to rats receiving no stimulation.<br><br>RESULTS: ADS was delayed up to 3&#x2009;weeks from injury onset and still resulted in significant motor recovery, with maximal recovery occurring in the 1-week delay group. The improvements in motor performance persisted for at least 8&#x2009;weeks following the end of treatment.<br><br>CONCLUSIONS: ADS is an effective method to treat motor impairments following acquired brain injury in rats. This study demonstrates the clinical relevance of this technique as it could be initiated in the post-acute period and could be explanted/ceased once recovery has occurred.}, }
@article {pmid36636584, year = {2022}, author = {Truong, MT and Liu, YC and Kohn, J and Chinnadurai, S and Zopf, DA and Tribble, M and Tanner, PB and Sie, K and Chang, KW}, title = {Integrated microtia and aural atresia management.}, journal = {Frontiers in surgery}, volume = {9}, number = {}, pages = {944223}, pmid = {36636584}, issn = {2296-875X}, abstract = {OBJECTIVES: To present recommendations for the coordinated evaluation and management of the hearing and reconstructive needs of patients with microtia and aural atresia.<br><br>METHODS: A national working group of 9 experts on microtia and atresia evaluated a working document on the evaluation and treatment of patients. Treatment options for auricular reconstruction and hearing habilitation were reviewed and integrated into a coordinated care timeline.<br><br>RESULTS: Recommendations were created for children with microtia and atresia, including diagnostic considerations, surgical and non-surgical options for hearing management and auricular reconstruction, and the treatment timeline for each option. These recommendations are based on the collective opinion of the group and are intended for otolaryngologists, audiologists, plastic surgeons, anaplastologists, and any provider caring for a patient with microtia and ear canal atresia. Close communication between atresia/hearing reconstruction surgeon and microtia repair surgeon is strongly recommended.}, }
@article {pmid36635340, year = {2023}, author = {Daly, I}, title = {Neural decoding of music from the EEG.}, journal = {Scientific reports}, volume = {13}, number = {1}, pages = {624}, pmid = {36635340}, issn = {2045-2322}, abstract = {Neural decoding models can be used to decode neural representations of visual, acoustic, or semantic information. Recent studies have demonstrated neural decoders that are able to decode accoustic information from a variety of neural signal types including electrocortiography (ECoG) and the electroencephalogram (EEG). In this study we explore how functional magnetic resonance imaging (fMRI) can be combined with EEG to develop an accoustic decoder. Specifically, we first used a joint EEG-fMRI paradigm to record brain activity while participants listened to music. We then used fMRI-informed EEG source localisation and a bi-directional long-term short term deep learning network to first extract neural information from the EEG related to music listening and then to decode and reconstruct the individual pieces of music an individual was listening to. We further validated our decoding model by evaluating its performance on a separate dataset of EEG-only recordings. We were able to reconstruct music, via our fMRI-informed EEG source analysis approach, with a mean rank accuracy of 71.8% ([Formula: see text], [Formula: see text]). Using only EEG data, without participant specific fMRI-informed source analysis, we were able to identify the music a participant was listening to with a mean rank accuracy of 59.2% ([Formula: see text], [Formula: see text]). This demonstrates that our decoding model may use fMRI-informed source analysis to aid EEG based decoding and reconstruction of acoustic information from brain activity and makes a step towards building EEG-based neural decoders for other complex information domains such as other acoustic, visual, or semantic information.}, }
@article {pmid36634598, year = {2022}, author = {Zhu, S and Hosni, SI and Huang, X and Wan, M and Borgheai, SB and McLinden, J and Shahriari, Y and Ostadabbas, S}, title = {A dynamical graph-based feature extraction approach to enhance mental task classification in brain-computer interfaces.}, journal = {Computers in biology and medicine}, volume = {153}, number = {}, pages = {106498}, doi = {10.1016/j.compbiomed.2022.106498}, pmid = {36634598}, issn = {1879-0534}, abstract = {Graph theoretic approaches in analyzing spatiotemporal dynamics of brain activities are under-studied but could be very promising directions in developing effective brain-computer interfaces (BCIs). Many existing BCI systems use electroencephalogram (EEG) signals to record and decode human neural activities noninvasively. Often, however, the features extracted from the EEG signals ignore the topological information hidden in the EEG temporal dynamics. Moreover, existing graph theoretic approaches are mostly used to reveal the topological patterns of brain functional networks based on synchronization between signals from distinctive spatial regions, instead of interdependence between states at different timestamps. In this study, we present a robust fold-wise hyperparameter optimization framework utilizing a series of conventional graph-based measurements combined with spectral graph features and investigate its discriminative performance on classification of a designed mental task in 6 participants with amyotrophic lateral sclerosis (ALS). Across all of our participants, we reached an average accuracy of 71.1%±4.5% for mental task classification by combining the global graph-based measurements and the spectral graph features, higher than the conventional non-graph based feature performance (67.1%±7.5%). Compared to using either one of the graphic features (66.3%±6.5% for the eigenvalues and 65.9%±5.2% for the global graph features), our feature combination strategy shows considerable improvement in both accuracy and robustness performance. Our results indicate the feasibility and advantage of the presented fold-wise optimization framework utilizing graph-based features in BCI systems targeted at end-users.}, }
@article {pmid36633302, year = {2023}, author = {Sample, M and Sattler, S and Boehlen, W and Racine, E}, title = {Brain-computer interfaces, disability, and the stigma of refusal: A factorial vignette study.}, journal = {Public understanding of science (Bristol, England)}, volume = {}, number = {}, pages = {9636625221141663}, doi = {10.1177/09636625221141663}, pmid = {36633302}, issn = {1361-6609}, abstract = {As brain-computer interfaces are promoted as assistive devices, some researchers worry that this promise to "restore" individuals worsens stigma toward disabled people and fosters unrealistic expectations. In three web-based survey experiments with vignettes, we tested how refusing a brain-computer interface in the context of disability affects cognitive (blame), emotional (anger), and behavioral (coercion) stigmatizing attitudes (Experiment 1, N = 222) and whether the effect of a refusal is affected by the level of brain-computer interface functioning (Experiment 2, N = 620) or the risk of malfunctioning (Experiment 3, N = 620). We found that refusing a brain-computer interface increased blame and anger, while brain-computer interface functioning did change the effect of a refusal. Higher risks of device malfunctioning partially reduced stigmatizing attitudes and moderated the effect of refusal. This suggests that information about disabled people who refuse a technology can increase stigma toward them. This finding has serious implications for brain-computer interface regulation, media coverage, and the prevention of ableism.}, }
@article {pmid36630716, year = {2023}, author = {Abrego, AM and Khan, W and Wright, CE and Islam, MR and Ghajar, MH and Bai, X and Tandon, N and Seymour, JP}, title = {Sensing local field potentials with a directional and scalable depth electrode array.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acb230}, pmid = {36630716}, issn = {1741-2552}, abstract = {A variety of electrophysiology tools are available to the neurosurgeon for diagnosis, functional therapy, and neural prosthetics. However, no tool can currently address these three critical needs: (i) access to all cortical regions in a minimally invasive manner; (ii) recordings with microscale, mesoscale, and macroscale resolutions simultaneously; and (iii) access to spatially distant multiple brain regions that constitute distributed cognitive networks. We present a novel device for recording local field potentials (LFPs) with the form factor of a stereo-electroencephalographic electrode but combined with radially positioned microelectrodes and using the lead body to shield LFP sources, enabling directional sensitivity and scalability, referred to as the DISC array. As predicted by our electro-quasistatic models, DISC demonstrated significantly improved signal-to-noise ratio, directional sensitivity, and decoding accuracy from rat barrel cortex recordings during whisker stimulation. Critical for future translation, DISC demonstrated a higher SNR than virtual ring electrodes and a noise floor approaching that of large ring electrodes in an unshielded environment after common average referencing. DISC also revealed independent, stereoscopic current source density measures whose direction was verified after histology. Directional sensitivity of LFPs may significantly improve brain-computer interfaces and many diagnostic procedures, including epilepsy foci detection and deep brain targeting.}, }
@article {pmid36630714, year = {2023}, author = {Guo, Z and Chen, F}, title = {Impacts of simplifying articulation movements imagery to speech imagery BCI performance.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acb232}, pmid = {36630714}, issn = {1741-2552}, abstract = {OBJECTIVE: Speech imagery can be used as a reliable, natural, and user-friendly activation task for the development of brain-computer interface (BCI), which empowers individuals with severe disabilities to interact with their environment. The functional near-infrared spectroscopy (fNIRS) is advanced as one of the most suitable brain imaging methods for developing BCI systems owing to its advantages of being non-invasive, portable, insensitive to motion artifacts, and having relatively high spatial resolution.<br><br>APPROACH: To improve the classification performance of speech imagery BCI based on fNIRS, a novel paradigm was developed in this work by simplifying the articulation movements in speech imagery to make the articulation movement differences clearer between different words imagery tasks. A speech imagery BCI was proposed to directly answer questions by covertly rehearsing the word '' or '' ('yes' or 'no' in English), and an unconstrained rest task also was contained in this BCI. The articulation movements of speech imagery were simplified by retaining only the movements of the jaw and lips of vowels in Chinese Pinyin for words '' and ''.<br><br>MAIN RESULTS: Compared with conventional speech imagery, simplifying the articulation movements in speech imagery could generate more different brain activities among different tasks, which led to more differentiable temporal features and significantly higher classification performance. The average 3-class classification accuracies of the proposed paradigm across all 20 participants reached 69.6 % and 60.2% which were about 10.8% and 5.6% significantly higher than those of the conventional speech imagery paradigm operated in the 0-10 s and 0-2.5 s time windows, respectively.<br><br>SIGNIFICANCE: These results suggested that simplifying the articulation movements in speech imagery is promising for improving the classification performance of intuitive BCIs based on speech imagery.}, }
@article {pmid36628907, year = {2022}, author = {Lai, JB and Kong, LZ and Chen, J and Hu, SH}, title = {From strict quarantine to an optimized policy: Are we psychologically prepared?.}, journal = {Asian journal of psychiatry}, volume = {81}, number = {}, pages = {103435}, doi = {10.1016/j.ajp.2022.103435}, pmid = {36628907}, issn = {1876-2026}, }
@article {pmid36626112, year = {2023}, author = {Dos Santos, EM and San-Martin, R and Fraga, FJ}, title = {Comparison of subject-independent and subject-specific EEG-based BCI using LDA and SVM classifiers.}, journal = {Medical &amp; biological engineering &amp; computing}, volume = {}, number = {}, pages = {}, pmid = {36626112}, issn = {1741-0444}, abstract = {Motor imagery brain-computer interface (MI-BCI) is one of the most used paradigms in EEG-based brain-computer interface (BCI). The current state-of-the-art in BCI involves tuning classifiers to subject-specific training data, acquired over several sessions, in order to perform calibration prior to actual use of the so-called subject-specific BCI system (SS-BCI). Herein, the goal is to provide a ready-to-use system requiring minimal effort for setup. Thus, our challenge was to design a subject-independent BCI (SI-BCI) to be used by any new user without the constraint of individual calibration. Outcomes from other studies with the same purpose were used to undertake comparisons and validate our findings. For the EEG signal processing, we used a combination of the delta (0.5-4 Hz), alpha (8-13 Hz), and beta+gamma (13-40 Hz) bands at a stage prior to feature extraction. Next, we extracted features from the 27-channel EEG using common spatial pattern (CSP) and performed binary classification (MI of right- and left-hand) with linear discriminant analysis (LDA) and support vector machine (SVM) classifiers. These analyses were done for both the SS-BCI and SI-BCI models. We employed "leave-one-subject-out" (LOSO) arrangement and 10-fold cross-validation to evaluate our SI-BCI and SS-BCI systems, respectively. Compared with other two studies, our work was the only one that showed higher accuracy for the LDA classifier in SI-BCI as compared to SS-BCI. On the other hand, LDA accuracy was lower than accuracy achieved with SVM in both conditions (SI-BCI and SS-BCI). Our SS-BCI accuracy reached 76.85% using LDA and 94.20% using SVM and for SI-BCI we got 80.30% with LDA and 83.23% with SVM. We conclude that SI-BCI may be a feasible and relevant option, which can be used in scenarios where subjects are not able to submit themselves to long training sessions or to fast evaluation of the so called "BCI illiteracy." Comparatively, our strategy proved to be more efficient, giving us the best result for SI-BCI when faced against the classification performances of other three studies, even considering the caveat that different datasets were used in the comparison of the four studies.}, }
@article {pmid36625869, year = {2023}, author = {Sprinzl, G and Toner, J and Koitschev, A and Berger, N and Keintzel, T and Rasse, T and Baumgartner, WD and Honeder, C and Magele, A and Plontke, S and Götze, G and Schmutzhard, J and Zelger, P and Corkill, S and Lenarz, T and Salcher, R}, title = {Multicentric study on surgical information and early safety and performance results with the Bonebridge BCI 602: an active transcutaneous bone conduction hearing implant.}, journal = {European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery}, volume = {}, number = {}, pages = {}, pmid = {36625869}, issn = {1434-4726}, abstract = {AIM: This European multicentric study aimed to prove safety and performance of the Bonebridge BCI 602 in children and adults suffering from either conductive hearing loss (CHL), mixed hearing loss (MHL), or single-sided sensorineural deafness (SSD).<br><br>METHODS: 33 patients (13 adults and 10 children with either CHL or MHL and 10 patients with SSD) in three study groups were included. Patients were their own controls (single-subject repeated measures), comparing the unaided or pre-operative to the 3-month post-operative outcomes. Performance was evaluated by sound field thresholds (SF), word recognition scores (WRS) and/or speech reception thresholds in quiet (SRT) and in noise (SNR). Safety was demonstrated with a device-specific surgical questionnaire, adverse event reporting and stable pure-tone measurements.<br><br>RESULTS: The Bonebridge BCI 602 significantly improved SF thresholds (+&#x2009;25.5&#xa0;dB CHL/MHL/SSD), speech intelligibility in WRS (+&#x2009;68.0% CHL/MHL) and SRT in quiet (-&#x2009;16.5&#xa0;dB C/MHL) and in noise (-&#xa0;3.51&#xa0;dB SNR SSD). Air conduction (AC) and bone conduction (BC) thresholds remained stable over time. All adverse events were resolved, with none unanticipated. Mean audio processor wearing times in hours [h] per day for the CHL/MHL group were&#x2009;~&#x2009;13&#xa0;h for adults,&#x2009;~&#x2009;11&#xa0;h for paediatrics and&#x2009;~&#x2009;6&#xa0;h for the SSD group. The average surgical length was 57&#xa0;min for the CHL/MHL group and 42&#xa0;min for the SSD group. The versatility of the BCI 602 (reduced drilling depth and ability to bend the transition for optimal placement) allows for treatment of normal, pre-operated and malformed anatomies. All audiological endpoints were reached.<br><br>CONCLUSIONS: The Bonebridge BCI 602 significantly improved hearing thresholds and speech understanding. Since implant placement follows the patient's anatomy instead of the shape of the device and the duration of surgery is shorter than with its predecessor, implantation is easier with the BCI 602. Performance and safety were proven for adults and children as well as for the CHL/MHL and SSD indications 3 months post-operatively.}, }
@article {pmid36624409, year = {2023}, author = {Rusé, J and Clenet, A and Vaiva, G and Debien, C and Arbus, C and Salles, J}, title = {The association between reattempted suicide and incoming calls to the brief contact intervention service, VigilanS: a study of the clinical profile of callers.}, journal = {BMC psychiatry}, volume = {23}, number = {1}, pages = {21}, pmid = {36624409}, issn = {1471-244X}, abstract = {BACKGROUND: Suicide is a major health problem globally. As attempted suicide is a major risk factor for suicide, specific prevention strategies have been designed for use thereafter. An example is the brief contact intervention (BCI). In this regard, France employs a composite BCI, VigilanS, which utilizes three types of contact: phone calls, postcards and a 'who to contact in a crisis' card. Previous studies have found that this system is effective at preventing suicide. Nevertheless, VigilanS was not effective in the same way for all the patients included. This observation raises the question of specific adaptation during follow-up for populations that were less receptive to the service. In consideration of this issue, we identified one study which found that incoming calls to the service were linked with a higher risk of suicide reattempts. However, this study did not document the profiles of the patients who made these calls. Better understanding of why this population is more at risk is important in terms of identifying factors that could be targeted to improve follow-up. This research therefore aims to bring together such data.<br><br>METHODS: We performed a retrospective analysis of 579 patients referred to VigilanS by Toulouse University Hospital (France). We examined the sociodemographics, clinical characteristics, and follow-ups in place and compared the patients who made incoming calls to the service versus those who did not. Subsequently, we conducted a regression analysis using the significantly associated element of patients calling VigilanS. Then, in order to better understand this association, we analyzed the factors, including such calls, that were linked to the risk of suicide reattempts.<br><br>RESULTS: We found that 22% of the patients in our sample called the VigilanS service. These individuals: were older, at 41.4&#x2009;years versus 37.9&#x2009;years for the non-callers; were more likely to have a borderline personality disorder (BPD) diagnosis (28.9% versus 19.3%); and had a history of suicide attempts (71.9% versus 54.6%). Our analysis confirmed that incoming calls to VigilanS (OR&#x2009;=&#x2009;2.9) were associated with reattempted suicide, as were BPD (OR&#x2009;=&#x2009;1.8) and a history of suicide attempts (OR&#x2009;=&#x2009;1.7).<br><br>CONCLUSION: There was a high risk that the patients calling VigilanS would make another suicide attempt. However, this association was present regardless of the clinical profile. We postulate that this link between incoming calls and reattempted suicide may arise because this form of contact is, in fact, a way in which patients signal that a further attempt will be made.}, }
@article {pmid36622685, year = {2023}, author = {Mitchell, P and Lee, SCM and Yoo, PE and Morokoff, A and Sharma, RP and Williams, DL and MacIsaac, C and Howard, ME and Irving, L and Vrljic, I and Williams, C and Bush, S and Balabanski, AH and Drummond, KJ and Desmond, P and Weber, D and Denison, T and Mathers, S and O'Brien, TJ and Mocco, J and Grayden, DB and Liebeskind, DS and Opie, NL and Oxley, TJ and Campbell, BCV}, title = {Assessment of Safety of a Fully Implanted Endovascular Brain-Computer Interface for Severe Paralysis in 4 Patients: The Stentrode With Thought-Controlled Digital Switch (SWITCH) Study.}, journal = {JAMA neurology}, volume = {}, number = {}, pages = {}, doi = {10.1001/jamaneurol.2022.4847}, pmid = {36622685}, issn = {2168-6157}, abstract = {IMPORTANCE: Brain-computer interface (BCI) implants have previously required craniotomy to deliver penetrating or surface electrodes to the brain. Whether a minimally invasive endovascular technique to deliver recording electrodes through the jugular vein to superior sagittal sinus is safe and feasible is unknown.<br><br>OBJECTIVE: To assess the safety of an endovascular BCI and feasibility of using the system to control a computer by thought.<br><br>The Stentrode With Thought-Controlled Digital Switch (SWITCH) study, a single-center, prospective, first in-human study, evaluated 5 patients with severe bilateral upper-limb paralysis, with a follow-up of 12 months. From a referred sample, 4 patients with amyotrophic lateral sclerosis and 1 with primary lateral sclerosis met inclusion criteria and were enrolled in the study. Surgical procedures and follow-up visits were performed at the Royal Melbourne Hospital, Parkville, Australia. Training sessions were performed at patients' homes and at a university clinic. The study start date was May 27, 2019, and final follow-up was completed January 9, 2022.<br><br>INTERVENTIONS: Recording devices were delivered via catheter and connected to subcutaneous electronic units. Devices communicated wirelessly to an external device for personal computer control.<br><br>MAIN OUTCOMES AND MEASURES: The primary safety end point was device-related serious adverse events resulting in death or permanent increased disability. Secondary end points were blood vessel occlusion and device migration. Exploratory end points were signal fidelity and stability over 12 months, number of distinct commands created by neuronal activity, and use of system for digital device control.<br><br>RESULTS: Of 4 patients included in analyses, all were male, and the mean (SD) age was 61 (17) years. Patients with preserved motor cortex activity and suitable venous anatomy were implanted. Each completed 12-month follow-up with no serious adverse events and no vessel occlusion or device migration. Mean (SD) signal bandwidth was 233 (16) Hz and was stable throughout study in all 4 patients (SD range across all sessions, 7-32 Hz). At least 5 attempted movement types were decoded offline, and each patient successfully controlled a computer with the BCI.<br><br>CONCLUSIONS AND RELEVANCE: Endovascular access to the sensorimotor cortex is an alternative to placing BCI electrodes in or on the dura by open-brain surgery. These final safety and feasibility data from the first in-human SWITCH study indicate that it is possible to record neural signals from a blood vessel. The favorable safety profile could promote wider and more rapid translation of BCI to people with paralysis.<br><br>TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT03834857.}, }
@article {pmid36620442, year = {2022}, author = {Mu, J and Grayden, DB and Tan, Y and Oetomo, D}, title = {Frequency set selection for multi-frequency steady-state visual evoked potential-based brain-computer interfaces.}, journal = {Frontiers in neuroscience}, volume = {16}, number = {}, pages = {1057010}, pmid = {36620442}, issn = {1662-4548}, abstract = {OBJECTIVE: Multi-frequency steady-state visual evoked potential (SSVEP) stimulation and decoding methods enable the representation of a large number of visual targets in brain-computer interfaces (BCIs). However, unlike traditional single-frequency SSVEP, multi-frequency SSVEP is not yet widely used. One of the key reasons is that the redundancy in the input options requires an additional selection process to define an effective set of frequencies for the interface. This study investigates systematic frequency set selection methods.<br><br>METHODS: An optimization strategy based on the analysis of the frequency components in the resulting multi-frequency SSVEP is proposed, investigated and compared to existing methods, which are constructed based on the analysis of the stimulation (input) signals. We hypothesized that minimizing the occurrence of common sums in the multi-frequency SSVEP improves the performance of the interface, and that selection by pairs further increases the accuracy compared to selection by frequencies. An experiment with 12 participants was conducted to validate the hypotheses.<br><br>RESULTS: Our results demonstrated a statistically significant improvement in decoding accuracy with the proposed optimization strategy based on multi-frequency SSVEP features compared to conventional techniques. Both hypotheses were validated by the experiments.<br><br>CONCLUSION: Performing selection by pairs and minimizing the number of common sums in selection by pairs are effective ways to select suitable frequency sets that improve multi-frequency SSVEP-based BCI accuracies.<br><br>SIGNIFICANCE: This study provides guidance on frequency set selection in multi-frequency SSVEP. The proposed method in this study shows significant improvement in BCI performance (decoding accuracy) compared to existing methods in the literature.}, }
@article {pmid36619242, year = {2022}, author = {Wang, L and Lan, Z and Wang, Q and Bai, X and Ma, F}, title = {An Adaptive EEG Classification Algorithm Based on CSSD and ELM_Kernel for Small Training Samples.}, journal = {Journal of healthcare engineering}, volume = {2022}, number = {}, pages = {4509612}, pmid = {36619242}, issn = {2040-2309}, abstract = {Rehabilitation technologies based on brain-computer interface (BCI) have become a promising approach for patients with dyskinesia to regain movement. In BCI experiment, there is often a necessary stage of calibration measurement before the feedback applications. To reduce the time required for initial training, it is of great importance to have a method which can learn to classify electroencephalogram (EEG) signals with a little amount of training data. In this paper, the novel combination of feature extraction and classification algorithm is proposed for classification of EEG signals with a small number of training samples. For feature extraction, the motor imagery EEG signals are pre-processed, and a relative distance criterion is defined to select the optimal combination of channels. Subsequently, common spatial subspace decomposition (CSSD) algorithm and extreme learning machine with kernel (ELM_Kernel) algorithm are used to perform the types of tasks classification of motor imagery EEG signals. Simulation results demonstrate that the proposed method produces a high average classification accuracy of 99.1% on BCI Competition III dataset IVa and 76.92% on BCI Competition IV dataset IIa outperforming state-of-the-art algorithms and obtains a good classification accuracy.}, }
@article {pmid36619090, year = {2022}, author = {Chen, J and Zhao, Z and Shu, Q and Cai, G}, title = {Feature extraction based on microstate sequences for EEG-based emotion recognition.}, journal = {Frontiers in psychology}, volume = {13}, number = {}, pages = {1065196}, pmid = {36619090}, issn = {1664-1078}, abstract = {Recognizing emotion from Electroencephalography (EEG) is a promising and valuable research issue in the field of affective brain-computer interfaces (aBCI). To improve the accuracy of emotion recognition, an emotional feature extraction method is proposed based on the temporal information in the EEG signal. This study adopts microstate analysis as a spatio-temporal analysis for EEG signals. Microstates are defined as a series of momentary quasi-stable scalp electric potential topographies. Brain electrical activity could be modeled as being composed of a time sequence of microstates. Microstate sequences provide an ideal macroscopic window for observing the temporal dynamics of spontaneous brain activity. To further analyze the fine structure of the microstate sequence, we propose a feature extraction method based on k-mer. K-mer is a k-length substring of a given sequence. It has been widely used in computational genomics and sequence analysis. We extract features that are based on the D 2 ∗ statistic of k-mer. In addition, we also extract four parameters (duration, occurrence, time coverage, GEV) of each microstate class as features at the coarse level. We conducted experiments on the DEAP dataset to evaluate the performance of the proposed features. The experimental results demonstrate that the fusion of features in fine and coarse levels can effectively improve classification accuracy.}, }
@article {pmid36618996, year = {2022}, author = {Pan, J and Chen, X and Ban, N and He, J and Chen, J and Huang, H}, title = {Advances in P300 brain-computer interface spellers: toward paradigm design and performance evaluation.}, journal = {Frontiers in human neuroscience}, volume = {16}, number = {}, pages = {1077717}, pmid = {36618996}, issn = {1662-5161}, abstract = {A brain-computer interface (BCI) is a non-muscular communication technology that provides an information exchange channel for our brains and external devices. During the decades, BCI has made noticeable progress and has been applied in many fields. One of the most traditional BCI applications is the BCI speller. This article primarily discusses the progress of research into P300 BCI spellers and reviews four types of P300 spellers: single-modal P300 spellers, P300 spellers based on multiple brain patterns, P300 spellers with multisensory stimuli, and P300 spellers with multiple intelligent techniques. For each type of P300 speller, we further review several representative P300 spellers, including their design principles, paradigms, algorithms, experimental performance, and corresponding advantages. We particularly emphasized the paradigm design ideas, including the overall layout, individual symbol shapes and stimulus forms. Furthermore, several important issues and research guidance for the P300 speller were identified. We hope that this review can assist researchers in learning the new ideas of these novel P300 spellers and enhance their practical application capability.}, }
@article {pmid36618992, year = {2022}, author = {Feng, J and Li, Y and Jiang, C and Liu, Y and Li, M and Hu, Q}, title = {Classification of motor imagery electroencephalogram signals by using adaptive cross-subject transfer learning.}, journal = {Frontiers in human neuroscience}, volume = {16}, number = {}, pages = {1068165}, pmid = {36618992}, issn = {1662-5161}, abstract = {INTRODUCTION: Electroencephalogram (EEG)-based motor imagery (MI) classification is an important aspect in brain-computer interfaces (BCIs), which bridges between neural system and computer devices decoding brain signals into recognizable machine commands. However, due to the small number of training samples of MI electroencephalogram (MI-EEG) for a single subject and the great individual differences of MI-EEG among different subjects, the generalization and accuracy of the model on the specific MI task may be poor.<br><br>METHODS: To solve these problems, an adaptive cross-subject transfer learning algorithm is proposed, which is based on kernel mean matching (KMM) and transfer learning adaptive boosting (TrAdaBoost) method. First, the common spatial pattern (CSP) is used to extract the spatial features. Then, in order to make the feature distribution more similar among different subjects, the KMM algorithm is used to compute a sample weight matrix for aligning the mean between source and target domains and reducing distribution differences among different subjects. Finally, the sample weight matrix from KMM is used as the initialization weight of TrAdaBoost, and then TrAdaBoost is used to adaptively select source domain samples that are closer to the target task distribution to assist in building a classification model.<br><br>RESULTS: In order to verify the effectiveness and feasibility of the proposed method, the algorithm is applied to BCI Competition IV datasets and in-house datasets. The results show that the average classification accuracy of the proposed method on the public datasets is 89.1%, and the average classification accuracy on the in-house datasets is 80.4%.<br><br>DISCUSSION: Compared with the existing methods, the proposed method effectively improves the classification accuracy of MI-EEG signals. At the same time, this paper also applies the proposed algorithm to the in-house dataset, the results verify the effectiveness of the algorithm again, and the results of this study have certain clinical guiding significance for brain rehabilitation.}, }
@article {pmid36617977, year = {2023}, author = {Khan, NN and Ganai, NA and Ahmad, T and Shanaz, S and Majid, R and Mir, MA and Ahmad, SF}, title = {Morphometric indices of native sheep breeds of the Himalayan region of India using multivariate principal component analysis.}, journal = {Zygote (Cambridge, England)}, volume = {}, number = {}, pages = {1-6}, doi = {10.1017/S0967199422000636}, pmid = {36617977}, issn = {1469-8730}, abstract = {This study was performed to analyze the morphometric traits and indices in 3000 animals of five registered sheep breeds in the Himalayan region under a multivariate approach. Data were recorded under field conditions with equal coverage of the five breeds, viz., Karnah, Gurez, Poonchi, Bakerwal and Changthangi on body length (BL), height at withers (HW), chest girth (CG), ear length (EL), and tail length (TL). Furthermore, four derived traits (indices) were studied, which included an index of body frame (IBF), an index of thorax development (ITD), a Baron-Crevat index (BCI), and an index of body weight (IBW). Multivariate principal component analysis (PCA) was undertaken on nine morphometric traits. Kaiser's criterion was used to reduce the number of principal components for further analysis and interpretation. The adequacy of sampling was evaluated using Kaiser-Meyer-Olkin (KMO) test and Bartlett's test of sphericity. The mean BL ranged from 52.15 (Changthangi) to 71.13 (Gurez). The estimates of HW, CG, EL and TL were highest in Gurez (63.49), Bakerwal (84.82), Bakerwal (7.26), and Karnah (8.18) breeds, respectively. Among the derived traits, the highest IBF was observed in the Gurez breed with an estimate of 112.22. Upon multivariate PCA on the dataset, the first four principal components were able to explain 92.117% of the total variance. The KMO test, Bartlett's test of sphericity and estimated communalities showed the appropriateness of PCA on the evaluated traits. Four eigenvalues were greater than one and were extracted for further analysis. Morphometric traits were highly correlated, except for EL and TL that showed lower correlation estimates with other traits. The Changthangi population showed the lowest estimates of BL, HW, CG and rectangular body frame. The present study ascertained important morphometric traits/indices that can help in developing selection criteria and formulating sustainable breeding and conservation plans vis-à-vis the unique sheep breeds of the temperate Himalayas.}, }
@article {pmid36617798, year = {2022}, author = {Lee, YJ and Lee, HJ and Tae, KS}, title = {Classification of EEG signals related to real and imagery knee movements using deep learning for brain computer interfaces.}, journal = {Technology and health care : official journal of the European Society for Engineering and Medicine}, volume = {}, number = {}, pages = {}, doi = {10.3233/THC-220363}, pmid = {36617798}, issn = {1878-7401}, abstract = {BACKGROUND: Non-invasive Brain-Computer Interface (BCI) uses an electroencephalogram (EEG) to obtain information on brain neural activity. Because EEG can be contaminated by various artifacts during the collection process, it has primarily evolved into motor imagery (MI) with a low risk of contamination. However, MI has a disadvantage in that accurate data is difficult to obtain.<br><br>OBJECTIVE: The goal of this study was to determine which motor imagery and movement execution (ME) of the knee has the best classification performance.<br><br>METHODS: Ten subjects were selected to provide MI and ME data for four different types of knee exercise. The experiment was conducted to keep the left, right, and both knees extend or bend for five seconds, and there was a five seconds break between each movement. Each motion was performed 20 times and the MI was carried out in the same protocol. Motions were classified through a modified model of the Lenet-5 of CNN (Convolution Neural Network).<br><br>RESULTS: The deep learning data was classified, and a study discovered that ME (98.91%) could be classified significantly more accurately than MI (98.37%) (p< 0.001).<br><br>CONCLUSION: If future studies on other body movements are conducted, we anticipate that BCI can be further developed to be more accurate. And such advancements in BCI can be used to facilitate the patient's communication by analyzing the user's movement intention. These results can also be used for various controls such as robots using a combination of MI and ME.}, }
@article {pmid35950925, year = {2022}, author = {Miskowiak, KW and Yalin, N and Seeberg, I and Burdick, KE and Balanzá-Martínez, V and Bonnin, CDM and Bowie, CR and Carvalho, AF and Dols, A and Douglas, K and Gallagher, P and Hasler, G and Kessing, LV and Lafer, B and Lewandowski, KE and López-Jaramillo, C and Martinez-Aran, A and McIntyre, RS and Porter, RJ and Purdon, SE and Schaffer, A and Sumiyoshi, T and Torres, IJ and Van Rheenen, TE and Yatham, LN and Young, AH and Vieta, E and Stokes, PRA}, title = {Can magnetic resonance imaging enhance the assessment of potential new treatments for cognitive impairment in mood disorders? A systematic review and position paper by the International Society for Bipolar Disorders Targeting Cognition Task Force.}, journal = {Bipolar disorders}, volume = {24}, number = {6}, pages = {615-636}, pmid = {35950925}, issn = {1399-5618}, mesh = {*Bipolar Disorder/diagnostic imaging/drug therapy ; Cognition ; *Cognitive Dysfunction/diagnostic imaging ; Humans ; Magnetic Resonance Imaging ; Mood Disorders/diagnostic imaging/drug therapy ; }, abstract = {BACKGROUND: Developing treatments for cognitive impairment is key to improving the functioning of people with mood disorders. Neuroimaging may assist in identifying brain-based efficacy markers. This systematic review and position paper by the International Society for Bipolar Disorders Targeting Cognition Task Force examines the evidence from neuroimaging studies of pro-cognitive interventions.<br><br>METHODS: We included magnetic resonance imaging (MRI) studies of candidate interventions in people with mood disorders or healthy individuals, following the procedures of the Preferred Reporting Items for Systematic reviews and Meta-Analysis 2020 statement. Searches were conducted on PubMed/MEDLINE, PsycInfo, EMBASE, Cochrane Library, and Clinicaltrials.gov from inception to 30th April 2021. Two independent authors reviewed the studies using the National Heart, Lung, Blood Institutes of Health Quality Assessment Tool for Controlled Intervention Studies and the quality of neuroimaging methodology assessment checklist.<br><br>RESULTS: We identified 26 studies (N&#xa0;=&#xa0;702). Six investigated cognitive remediation or pharmacological treatments in mood disorders (N&#xa0;=&#xa0;190). In healthy individuals, 14 studies investigated pharmacological interventions (N&#xa0;=&#xa0;319), 2 cognitive training (N&#xa0;=&#xa0;73) and 4 neuromodulatory treatments (N&#xa0;=&#xa0;120). Methodologies were mostly rated as 'fair'. 77% of studies investigated effects with task-based fMRI. Findings varied but most consistently involved treatment-associated cognitive control network (CCN) activity increases with cognitive improvements, or CCN activity decreases with no cognitive change, and increased functional connectivity. In mood disorders, treatment-related default mode network suppression occurred.<br><br>CONCLUSIONS: Modulation of CCN and DMN activity is a putative efficacy biomarker. Methodological recommendations are to pre-declare intended analyses and use task-based fMRI, paradigms probing the CCN, longitudinal assessments, mock scanning, and out-of-scanner tests.}, }
@article {pmid36617098, year = {2023}, author = {Alotaibi, FM and Fawad, }, title = {An AI-Inspired Spatio-Temporal Neural Network for EEG-Based Emotional Status.}, journal = {Sensors (Basel, Switzerland)}, volume = {23}, number = {1}, pages = {}, doi = {10.3390/s23010498}, pmid = {36617098}, issn = {1424-8220}, abstract = {The accurate identification of the human emotional status is crucial for an efficient human-robot interaction (HRI). As such, we have witnessed extensive research efforts made in developing robust and accurate brain-computer interfacing models based on diverse biosignals. In particular, previous research has shown that an Electroencephalogram (EEG) can provide deep insight into the state of emotion. Recently, various handcrafted and deep neural network (DNN) models were proposed by researchers for extracting emotion-relevant features, which offer limited robustness to noise that leads to reduced precision and increased computational complexity. The DNN models developed to date were shown to be efficient in extracting robust features relevant to emotion classification; however, their massive feature dimensionality problem leads to a high computational load. In this paper, we propose a bag-of-hybrid-deep-features (BoHDF) extraction model for classifying EEG signals into their respective emotion class. The invariance and robustness of the BoHDF is further enhanced by transforming EEG signals into 2D spectrograms before the feature extraction stage. Such a time-frequency representation fits well with the time-varying behavior of EEG patterns. Here, we propose to combine the deep features from the GoogLeNet fully connected layer (one of the simplest DNN models) together with the OMTLBP_SMC texture-based features, which we recently developed, followed by a K-nearest neighbor (KNN) clustering algorithm. The proposed model, when evaluated on the DEAP and SEED databases, achieves a 93.83 and 96.95% recognition accuracy, respectively. The experimental results using the proposed BoHDF-based algorithm show an improved performance in comparison to previously reported works with similar setups.}, }
@article {pmid36610247, year = {2022}, author = {Robinette, K and Sims, J and Pang, B and Babu, S}, title = {Transcutaneous versus percutaneous bone-anchored hearing aids: A quality of life comparison.}, journal = {American journal of otolaryngology}, volume = {44}, number = {2}, pages = {103758}, doi = {10.1016/j.amjoto.2022.103758}, pmid = {36610247}, issn = {1532-818X}, abstract = {PURPOSE: To determine whether patients have improved quality of life outcomes with percutaneous bone conduction implant (p-BCI) versus transcutaneous bone conduction implant (t-BCI).<br><br>MATERIALS &amp; METHODS: Retrospective chart review of patients who have undergone placement of a BCI in the Ascension St John Providence Health System from 2013 to 2018. Patient satisfaction of t-BCI and p-BCI was measured using a questionnaire that incorporated the Glasgow Benefit Inventory (GBI) and BAHA, aesthetic, hygiene &amp; use (BAHU) survey. Key outcome variables were separated into 2 categories: (1) evaluation of wound healing and implant-associated complications, and (2) quality of life improvements.<br><br>RESULTS: Comparative analysis of the 27 p-BCI patients and 10 t-BCI patients showed overall positive benefit with no statistically significant difference on quality of life improvement between the two groups. Total complication rates for p-BCI (48.1&#xa0;%) vs t-BCI (10&#xa0;%) was marginally significant (p&#xa0;=&#xa0;0.056). Rate of revision for p-BCI versus t-BCI was 14.8&#xa0;% vs 0&#xa0;%, respectively.<br><br>CONCLUSION: This study provides a much-needed comparative insight in patient's experience with these two devices. Understanding which device is preferable in the patient's view will offer helpful information for guiding proper implant selection.}, }
@article {pmid36610205, year = {2022}, author = {Tang, W and Shen, T and Huang, Y and Zhu, W and You, S and Zhu, C and Zhang, L and Ma, J and Wang, Y and Zhao, J and Li, T and Lai, HY}, title = {Exploring structural and functional alterations in drug-naïve obsessive-compulsive disorder patients: An ultrahigh field multimodal MRI study.}, journal = {Asian journal of psychiatry}, volume = {81}, number = {}, pages = {103431}, doi = {10.1016/j.ajp.2022.103431}, pmid = {36610205}, issn = {1876-2026}, abstract = {BACKGROUND: Brain structural and functional alterations have been reported in obsessive-compulsive disorder (OCD) patients; however, these findings were inconsistent across studies due to several limitations, including small sample sizes, different inclusion/exclusion criteria, varied demographic characteristics and symptom dimensions, comorbidity, and medication status. Prominent and replicable neuroimaging biomarkers remain to be discovered.<br><br>METHODS: This study explored the gray matter structure, neural activity, and white matter microstructure differences in 40 drug-na&#xef;ve OCD patients and 57 matched healthy controls using ultrahigh field 7.0&#xa0;T multimodal magnetic resonance imaging, which increased the spatial resolution and detection power. We also evaluated correlations among different modalities, imaging features and clinical symptoms.<br><br>RESULTS: Drug-na&#xef;ve OCD patients exhibited significantly increased gray matter volume in the frontal cortex, especially in the orbitofrontal cortex, as well as volumetric reduction in the temporal lobe, occipital lobe and cerebellum. Increased neural activities were observed in the cingulate gyri and precuneus. Increased temporal-middle cingulate and posterior cingulate-precuneus functional connectivities and decreased frontal-middle cingulate connectivity were further detected. Decreased fractional anisotropy values were found in the cingulum-hippocampus gyrus and inferior fronto-occipital fascicle in OCD patients. Moreover, significantly altered imaging features were related to OCD symptom severity. Altered functional and structural neural connectivity might influence compulsive and obsessive features, respectively.<br><br>CONCLUSIONS: Altered structure and function of the classical cortico-striato-thalamo-cortical circuit, limbic system, default mode network, visual, language and sensorimotor networks play important roles in the neurophysiology of OCD.}, }
@article {pmid36609445, year = {2023}, author = {Williams, JB and Cao, Q and Wang, W and Lee, YH and Qin, L and Zhong, P and Ren, Y and Ma, K and Yan, Z}, title = {Inhibition of histone methyltransferase Smyd3 rescues NMDAR and cognitive deficits in a tauopathy mouse model.}, journal = {Nature communications}, volume = {14}, number = {1}, pages = {91}, pmid = {36609445}, issn = {2041-1723}, abstract = {Pleiotropic mechanisms have been implicated in Alzheimer's disease (AD), including transcriptional dysregulation, protein misprocessing and synaptic dysfunction, but how they are mechanistically linked to induce cognitive deficits in AD is unclear. Here we find that the histone methyltransferase Smyd3, which catalyzes histone H3 lysine 4 trimethylation (H3K4me3) to activate gene transcription, is significantly elevated in prefrontal cortex (PFC) of AD patients and P301S Tau mice, a model of tauopathies. A short treatment with the Smyd3 inhibitor, BCI-121, rescues cognitive behavioral deficits, and restores synaptic NMDAR function and expression in PFC pyramidal neurons of P301S Tau mice. Fbxo2, which encodes an E3 ubiquitin ligase controlling the degradation of NMDAR subunits, is identified as a downstream target of Smyd3. Smyd3-induced upregulation of Fbxo2 in P301S Tau mice is linked to the increased NR1 ubiquitination. Fbxo2 knockdown in PFC leads to the recovery of NMDAR function and cognitive behaviors in P301S Tau mice. These data suggest an integrated mechanism and potential therapeutic strategy for AD.}, }
@article {pmid36608342, year = {2023}, author = {Han, J and Xu, M and Xiao, X and Yi, W and Jung, TP and Ming, D}, title = {A high-speed hybrid brain-computer interface with more than 200 targets.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acb105}, pmid = {36608342}, issn = {1741-2552}, abstract = {OBJECTIVE: Brain-computer interfaces (BCIs) have recently made significant strides in expanding their instruction set, which has attracted wide attention from researchers. The number of targets and commands is a key indicator of how well BCIs can decode the brain's intentions. No studies have reported a BCI system with over 200 targets.<br><br>APPROACH: This study developed the first high-speed BCI system with up to 216 targets that were encoded by a combination of electroencephalography (EEG) features, including P300, motion visual evoked potential (mVEP), and steady-state visual evoked potential (SSVEP). Specifically, the hybrid BCI paradigm used the time-frequency division multiple access strategy to elaborately tag targets with P300 and mVEP of different time windows, along with SSVEP of different frequencies. The hybrid features were then decoded by task-discriminant component analysis and linear discriminant analysis. Ten subjects participated in the offline and online cued-guided spelling experiments. Other ten subjects took part in online free-spelling experiments.<br><br>MAIN RESULTS: The offline results showed that the mVEP and P300 components were prominent in the central, parietal, and occipital regions, while the most distinct SSVEP feature was in the occipital region. The online cued-guided spelling and free-spelling results showed that the proposed BCI system achieved an average accuracy of 85.37%&#xb1;7.49% and 86.00%&#xb1;5.98% for the 216-target classification, resulting in an average information transfer rate (ITR) of 302.83&#xb1;39.20 bits/min and 204.47&#xb1;37.56 bits/min, respectively. Notably, the peak ITR could reach up to 367.83 bits/min.<br><br>SIGNIFICANCE: This study developed the first high-speed BCI system with more than 200 targets, which holds promise for extending BCI's application scenarios.}, }
@article {pmid36608339, year = {2023}, author = {Tao, T and Jia, Y and Xu, G and Liang, R and Zhang, Q and Chen, L and Gao, Y and Chen, R and Zheng, X and Yu, Y}, title = {Enhancement of motor imagery training efficiency by an online adaptive training paradigm integrated with error related potential.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acb102}, pmid = {36608339}, issn = {1741-2552}, abstract = {OBJECTIVE: Motor Imagery (MI) is a process of autonomously modulating the motor area to rehearse action mentally without actual execution. Based on the neuroplasticity of the cerebral cortex, MI can promote the functional rehabilitation of the injured cerebral cortex motor area. However, it usually takes several days to a few months to train individuals to acquire the necessary MI ability to control rehabilitation equipment in current studies, which greatly limits the clinical application of rehabilitation training systems based on the MI brain-computer interface (BCI).<br><br>APPROACH: A novel MI training paradigm combined with the error related potential (ErrP) is proposed, and online adaptive training of the MI classifier was performed using ErrP. ErrP is used to correct the output of the MI classification to obtain a higher accuracy of kinesthetic feedback based on the imagination intention of subjects while generating simulated labels for MI online adaptive training. In this way, we improved the MI training efficiency. Thirteen subjects were randomly divided into an experimental group using the proposed paradigm and a control group using the traditional MI training paradigm to participate in six MI training experiments.<br><br>MAIN RESULTS: The proposed paradigm enabled the experimental group to obtain a higher event-related desynchronization (ERD) modulation level in the contralateral brain region compared with the control group and 69.76% online classification accuracy of MI after 3 MI training experiments. The online classification accuracy reached 72.76% and the whole system recognized the MI intention of the subjects with an online accuracy of 82.61% after 6 experiments.<br><br>SIGNIFICANCE: Compared with the conventional unimodal MI training strategy, the proposed approach enables subjects to use the MI-BCI based system directly and achieve a better performance after only 3 training experiments with training left and right hands simultaneously. This greatly improves the usability of the MI-BCI-based rehabilitation system and makes it more convenient for clinical use.}, }
@article {pmid36607454, year = {2023}, author = {LaMarca, K and Gevirtz, R and Lincoln, AJ and Pineda, JA}, title = {Brain-Computer Interface Training of mu EEG Rhythms in Intellectually Impaired Children with Autism: A Feasibility Case Series.}, journal = {Applied psychophysiology and biofeedback}, volume = {}, number = {}, pages = {}, pmid = {36607454}, issn = {1573-3270}, abstract = {Prior studies show that neurofeedback training (NFT) of mu rhythms improves behavior and EEG mu rhythm suppression during action observation in children with autism spectrum disorder (ASD). However, intellectually impaired persons were excluded because of their behavioral challenges. We aimed to determine if intellectually impaired children with ASD, who were behaviorally prepared to take part in a mu-NFT study using conditioned auditory reinforcers, would show improvements in symptoms and mu suppression following mu-NFT. Seven children with ASD (ages 6-8; mean IQ 70.6 ± 7.5) successfully took part in mu-NFT. Four cases demonstrated positive learning trends (hit rates) during mu-NFT (learners), and three cases did not (non-learners). Artifact-creating behaviors were present during tests of mu suppression for all cases, but were more frequent in non-learners. Following NFT, learners showed behavioral improvements and were more likely to show evidence of a short-term increase in mu suppression relative to non-learners who showed little to no EEG or behavior improvements. Results support mu-NFT's application in some children who otherwise may not have been able to take part without enhanced behavioral preparations. Children who have more limitations in demonstrating learning during NFT, or in providing data with relatively low artifact during task-dependent EEG tests, may have less chance of benefiting from mu-NFT. Improving the identification of ideal mu-NFT candidates, mu-NFT learning rates, source analyses, EEG outcome task performance, population-specific artifact-rejection methods, and the theoretical bases of NFT protocols, could aid future BCI-based, neurorehabilitation efforts.}, }
@article {pmid36606248, year = {2022}, author = {Kim, J and Jiang, X and Forenzo, D and Liu, Y and Anderson, N and Greco, CM and He, B}, title = {Immediate effects of short-term meditation on sensorimotor rhythm-based brain-computer interface performance.}, journal = {Frontiers in human neuroscience}, volume = {16}, number = {}, pages = {1019279}, pmid = {36606248}, issn = {1662-5161}, abstract = {INTRODUCTION: Meditation has been shown to enhance a user's ability to control a sensorimotor rhythm (SMR)-based brain-computer interface (BCI). For example, prior work have demonstrated that long-term meditation practices and an 8-week mindfulness-based stress reduction (MBSR) training have positive behavioral and neurophysiological effects on SMR-based BCI. However, the effects of short-term meditation practice on SMR-based BCI control are still unknown.<br><br>METHODS: In this study, we investigated the immediate effects of a short, 20-minute meditation on SMR-based BCI control. Thirty-seven subjects performed several runs of one-dimensional cursor control tasks before and after two types of 20-minute interventions: a guided mindfulness meditation exercise and a recording of a narrator reading a journal article.<br><br>RESULTS: We found that there is no significant change in BCI performance and Electroencephalography (EEG) BCI control signal following either 20-minute intervention. Moreover, the change in BCI performance between the meditation group and the control group was found to be not significant.<br><br>DISCUSSION: The present results suggest that a longer period of meditation is needed to improve SMR-based BCI control.}, }
@article {pmid36604821, year = {2023}, author = {Echtioui, A and Zouch, W and Ghorbel, M and Mhiri, C and Hamam, H}, title = {Classification of BCI Multiclass Motor Imagery Task Based on Artificial Neural Network.}, journal = {Clinical EEG and neuroscience}, volume = {}, number = {}, pages = {15500594221148285}, doi = {10.1177/15500594221148285}, pmid = {36604821}, issn = {2169-5202}, abstract = {Motor imagery (MI) signals recorded by electroencephalography provide the most practical basis for conceiving brain-computer interfaces (BCI). These interfaces offer a high degree of freedom. This helps people with motor disabilities communicate with the device by tackling a sequence of motor imagery tasks. However, the extracting user-specific features and increasing the accuracy of the classifier remain as difficult tasks in MI-based BCI. In this work, we propose a new method using artificial neural network (ANN) enhancing the performance of the motor imagery classification. Feature extraction techniques, like time domain parameters, band power features, signal power features, and wavelet packet decomposition (WPD), are studied and compared. Four classification algorithms are implemented which are Quadratic Discriminant Analysis, k-Nearest Neighbors, Linear Discriminant Analysis, and proposed ANN architecture. We added Batch Normalization layers to the proposed ANN architecture to improve the learning time and accuracy of the neural network. These layers also alleviate the effect of weight initialization and the addition of a regularization effect on the network. Our proposed method using ANN architecture achieves 0.5545 of kappa and 58.42% of accuracy on the BCI Competition IV-2a dataset. Our results show that the modified ANN method, with frequency and spatial features extracted by WPD and Common Spatial Pattern, respectively, offers a better classification compared to other current methods.}, }
@article {pmid36604739, year = {2023}, author = {Sun, G and McCartin, M and Liu, W and Zhang, Q and Kenefati, G and Chen, ZS and Wang, J}, title = {Temporal pain processing in the primary somatosensory cortex and anterior cingulate cortex.}, journal = {Molecular brain}, volume = {16}, number = {1}, pages = {3}, pmid = {36604739}, issn = {1756-6606}, support = {GM115384/GM/NIGMS NIH HHS/United States ; NS121776/NS/NINDS NIH HHS/United States ; }, abstract = {Pain is known to have sensory and affective components. The sensory pain component is encoded by neurons in the primary somatosensory cortex (S1), whereas the emotional or affective pain experience is in large part processed by neural activities in the anterior cingulate cortex (ACC). The timing of how a mechanical or thermal noxious stimulus triggers activation of peripheral pain fibers is well-known. However, the temporal processing of nociceptive inputs in the cortex remains little studied. Here, we took two approaches to examine how nociceptive inputs are processed by the S1 and ACC. We simultaneously recorded local field potentials in both regions, during the application of a brain-computer interface (BCI). First, we compared event related potentials in the S1 and ACC. Next, we used an algorithmic pain decoder enabled by machine-learning to detect the onset of pain which was used during the implementation of the BCI to automatically treat pain. We found that whereas mechanical pain triggered neural activity changes first in the S1, the S1 and ACC processed thermal pain with a reasonably similar time course. These results indicate that the temporal processing of nociceptive information in different regions of the cortex is likely important for the overall pain experience.}, }
@article {pmid36604186, year = {2023}, author = {Zhang, P and Zhang, D and Lai, J and Fu, Y and Wu, L and Huang, H and Pan, Y and Jiang, J and Xi, C and Che, Z and Song, X and Hu, S}, title = {Characteristics of the gut microbiota in bipolar depressive disorder patients with distinct weight.}, journal = {CNS neuroscience &amp; therapeutics}, volume = {}, number = {}, pages = {}, doi = {10.1111/cns.14078}, pmid = {36604186}, issn = {1755-5949}, abstract = {BACKGROUND: Preliminary studies have indicated metabolic dysfunction and gut dysbiosis in patients with bipolar disorder (BD). In this study, we aimed to clarify the impact of the gut microbial composition and function on metabolic dysfunction in BD patients with an acute depressive episode.<br><br>METHODS: Fresh fecal samples were provided from 58 patients with BD depression, including 29 with normal weight (NW) and 29 with overweight/obesity (OW), and 31 healthy controls (HCs). The hypervariable region of 16&#x2009;S rRNA gene (V3-V4) sequencing was performed using IonS5TMXL platform to evaluate the bacterial communities. Differences of microbial community and correlation to clinical parameters across different groups were analyzed.<br><br>RESULTS: Compared to NW and HCs, the OW group showed a decreased tendency in alpha diversity index. Beta diversity was markedly different among these groups (PERMANOVA: R[2] &#xa0;=&#xa0;0.034, p&#xa0;=&#xa0;0.01) and was higher in patients versus HCs. A total number of 24 taxa displayed significantly different abundance among OW, NW, and HCs. At the family level, the abundance of three taxa was remarkably increased in NW, one in OW, and one in HCs. At the genus level, five taxa were enriched in OW, eight in NW, and two in HCs. The relative abundance of the genera Megamonas was positively associated with BMI, while Eggerthella was negatively correlated with BMI. Functional prediction analysis revealed the metabolism of cofactors and vitamins and amino acid were highly enriched in OW compared to HCs. In addition, microbial functions involved in "lipid metabolism" were depleted while the "fructose and mannose metabolism" was enriched in OW compared to NW group.<br><br>CONCLUSIONS: Specific bacterial taxa involved in pathways regulating the lipid, energy, and amino acid metabolisms may underlie the weight concerns in depressed BD patients. Potential targeting gut microbial therapy is provided for overweight/obesity patients with BD, which still need further studies in the future.}, }
@article {pmid36603232, year = {2022}, author = {Yu, W and Zhao, F and Ren, Z and Jin, D and Yang, X and Zhang, X}, title = {Mining attention distribution paradigm: Discover gaze patterns and their association rules behind the visual image.}, journal = {Computer methods and programs in biomedicine}, volume = {230}, number = {}, pages = {107330}, doi = {10.1016/j.cmpb.2022.107330}, pmid = {36603232}, issn = {1872-7565}, abstract = {BACKGROUND AND OBJECTIVE: Attention allocation reflects the way of humans filtering and organizing the information. On one hand, different task scenarios seriously affect human's rule of attention distribution, on the other hand, visual attention reflecting the cognitive and psychological process. Most of the previous studies on visual attention allocation are based on cognitive models, predicted models, or statistical analysis of eye movement data or visual images, however, these methods are inadequate to provide an inside view of gaze behavior to reveal the attention distribution pattern within scenario context. Moreover, they seldom study the association rules of these patterns. Therefore, we adopted the big data mining approach to discover the paradigm of visual attention distribution.<br><br>METHODS: We applied the data mining method to extract the gaze patterns to discover the regularities of attention distribution behavior within the scenario context. The proposed method consists of three components, tasks scenario segmented and clustered, gaze pattern mining, and association rule of frequent pattern mining.<br><br>RESULTS: The proposed approach is tested on the operation platform. The complex operation task is simultaneously segmented and clustered with the TICC-based method and evaluated by the BCI index. The operator's eye movement frequent patterns and their association rule are discovered. The results demonstrate that our method can associate the eye-tracking data with the task-oriented scene data.<br><br>DISCUSSION: The proposed method provides the benefits of being able to explicitly express and quantitatively&#xa0;analyze people's visual attention patterns. The proposed method can not only be applied in the field of aerospace medicine and aviation psychology, but also can likely be applied to computer-aided diagnosis and follow-up tool for neurological disease and cognitive impairment related disease, such as ADHD (Attention Deficit Hyperactivity Disorder), neglect syndrome, social attention differences in ASD (Autism spectrum disorder).}, }
@article {pmid36601593, year = {2022}, author = {Schalk, G and Worrell, S and Mivalt, F and Belsten, A and Kim, I and Morris, JM and Hermes, D and Klassen, BT and Staff, NP and Messina, S and Kaufmann, T and Rickert, J and Brunner, P and Worrell, GA and Miller, KJ}, title = {Toward a fully implantable ecosystem for adaptive neuromodulation in humans: Preliminary experience with the CorTec BrainInterchange device in a canine model.}, journal = {Frontiers in neuroscience}, volume = {16}, number = {}, pages = {932782}, pmid = {36601593}, issn = {1662-4548}, abstract = {This article describes initial work toward an ecosystem for adaptive neuromodulation in humans by documenting the experience of implanting CorTec's BrainInterchange (BIC) device in a beagle canine and using the BCI2000 environment to interact with the BIC device. It begins with laying out the substantial opportunity presented by a useful, easy-to-use, and widely available hardware/software ecosystem in the current landscape of the field of adaptive neuromodulation, and then describes experience with implantation, software integration, and post-surgical validation of recording of brain signals and implant parameters. Initial experience suggests that the hardware capabilities of the BIC device are fully supported by BCI2000, and that the BIC/BCI2000 device can record and process brain signals during free behavior. With further development and validation, the BIC/BCI2000 ecosystem could become an important tool for research into new adaptive neuromodulation protocols in humans.}, }
@article {pmid36601085, year = {2022}, author = {Yan, L and Hou, Z and Fu, W and Yu, Y and Cui, R and Miao, Z and Lou, X and Ma, N}, title = {Association of periprocedural perfusion non-improvement with recurrent stroke after endovascular treatment for Intracranial Atherosclerotic Stenosis.}, journal = {Therapeutic advances in neurological disorders}, volume = {15}, number = {}, pages = {17562864221143178}, pmid = {36601085}, issn = {1756-2856}, abstract = {BACKGROUND: Predictors of recurrent stroke after endovascular treatment for symptomatic intracranial atherosclerotic stenosis (ICAS) remain uncertain.<br><br>OBJECTIVES: Among baseline characteristics, lesion features, and cerebral perfusion changes, we try to explore which factors are associated with the risk of recurrent stroke in symptomatic ICAS after endovascular treatment.<br><br>DESIGN: Consecutive patients with symptomatic ICAS of 70-99% receiving endovascular treatment were enrolled. All patients underwent whole-brain computer tomography perfusion (CTP) within 3&#x2009;days before and 3&#x2009;days after the endovascular treatment. Baseline characteristics, lesion features, and cerebral perfusion changes were collected.<br><br>METHODS: Cerebral perfusion changes were evaluated with RAPID software and calculated as preprocedural cerebral blood flow (CBF)&#x2009;<&#x2009;30%, time to maximum of the residue function (Tmax)&#x2009;>&#x2009;6&#x2009;s, and Tmax&#x2009;>&#x2009;4&#x2009;s volumes minus postprocedural. Cerebral perfusion changes were divided into periprocedural perfusion improvement (>0&#x2009;ml) and non-improvement (&#x2a7d;&#x2009;0&#x2009;ml). Recurrent stroke within 180&#x2009;days was collected. The Cox proportional hazards analysis analyses were performed to evaluate factors associated with recurrent stroke.<br><br>RESULTS: From March 2021 to December 2021, 107 patients with symptomatic ICAS were enrolled. Of the 107 enrolled patients, 30 (28.0%) patients underwent balloon angioplasty alone and 77 patients (72.0%) underwent stenting. The perioperative complications occurred in three patients. Among CBF&#x2009;<&#x2009;30%, Tmax&#x2009;>&#x2009;6&#x2009;s, and Tmax&#x2009;>&#x2009;4&#x2009;s volumes, Tmax&#x2009;>&#x2009;4&#x2009;s volume was available to evaluate cerebral perfusion changes. Periprocedural perfusion improvement was found in 77 patients (72.0%) and non-improvement in 30 patients (28.0%). Nine patients (8.4%) suffered from recurrent stroke in 180-day follow-up. In Cox proportional hazards analysis adjusted for age and sex, perfusion non-improvement was associated with recurrent stroke [hazards ratio (HR): 4.472; 95% CI: 1.069-18.718; p&#x2009;=&#x2009;0.040].<br><br>CONCLUSION: In patients with symptomatic ICAS treated with endovascular treatment, recurrent stroke may be related to periprocedural cerebral perfusion non-improvement.<br><br>REGISTRATION: http://www.chictr.org.cn. Unique identifier: ChiCTR2100052925.}, }
@article {pmid36600620, year = {2023}, author = {Zhang, Y and Tao, S and Coid, J and Wei, W and Wang, Q and Yue, W and Yan, H and Tan, L and Chen, Q and Yang, G and Lu, T and Wang, L and Zhang, F and Yang, J and Li, K and Lv, L and Tan, Q and Zhang, H and Ma, X and Yang, F and Lingjiang,  and Wang, C and Zhao, L and Deng, W and Guo, W and Ma, X and Zhang, D and Li, }, title = {The Role of Total White Blood Cell Count in Antipsychotic Treatment for Patients with Schizophrenia.}, journal = {Current neuropharmacology}, volume = {}, number = {}, pages = {}, doi = {10.2174/1570159X21666230104090046}, pmid = {36600620}, issn = {1875-6190}, abstract = {BACKGROUND: Total white blood cell count (TWBCc), an index of chronic and low-grade inflammation, is associated with clinical symptoms and metabolic alterations in patients with schizophrenia. The effect of antipsychotics on TWBCc, predictive values of TWBCc for drug response, and role of metabolic alterations require further study.<br><br>METHODS: Patients with schizophrenia were randomized to monotherapy with risperidone, olanzapine,quetiapine, aripiprazole, ziprasidone, perphenazine or haloperidol in a 6-week pharmacological trial. We repeatedly measured clinical symptoms, TWBCc, and metabolic measures (body mass index, blood pressure, waist circumference, fasting blood lipids and glucose). We used mixed-effect linear regression models to test whether TWBCc can predict drug response. Mediation analysis to investigate metabolic alteration effects on drug response.<br><br>RESULTS: At baseline, TWBCc was higher among patients previously medicated. After treatment with risperidone, olanzapine, quetiapine, perphenazine, and haloperidol, TWBCc decreased significantly (p<0.05). Lower baseline TWBCc predicted greater reductions in Positive and Negative Syndrome Scale (PANSS) total and negative scores over time (p<0.05). We found significant mediation of TWBCc for effects of waist circumference, fasting low-density lipoprotein cholesterol, and glucose on reductions in PANSS total scores and PANSS negative subscale scores (p<0.05).<br><br>CONCLUSION: TWBCc is affected by certain antipsychotics among patients with schizophrenia, with decreases observed following short-term, but increases following long-term treatment. TWBCc is predictive of drug response, with lower TWBCc predicting better responses to antipsychotics. It also mediates the effects of certain metabolic measures on improvement of negative symptoms. This.}, }
@article {pmid36600612, year = {2023}, author = {Kodama, M and Iwama, S and Morishige, M and Ushiba, J}, title = {Thirty-minute motor imagery exercise aided by EEG sensorimotor rhythm neurofeedback enhances morphing of sensorimotor cortices: a double-blind sham-controlled study.}, journal = {Cerebral cortex (New York, N.Y. : 1991)}, volume = {}, number = {}, pages = {}, doi = {10.1093/cercor/bhac525}, pmid = {36600612}, issn = {1460-2199}, abstract = {Neurofeedback training using electroencephalogram (EEG)-based brain-computer interfaces (BCIs) combined with mental rehearsals of motor behavior has demonstrated successful self-regulation of motor cortical excitability. However, it remains unclear whether the acquisition of skills to voluntarily control neural excitability is accompanied by structural plasticity boosted by neurofeedback. Here, we sought short-term changes in cortical structures induced by 30 min of BCI-based neurofeedback training, which aimed at the regulation of sensorimotor rhythm (SMR) in scalp EEG. When participants performed kinesthetic motor imagery of right finger movement with online feedback of either event-related desynchronisation (ERD) of SMR magnitude from the contralateral sensorimotor cortex (SM1) or those from other participants (i.e. placebo), the learning rate of SMR-ERD control was significantly different. Although overlapped structural changes in gray matter volumes were found in both groups, significant differences revealed by group-by-group comparison were spatially different; whereas the veritable neurofeedback group exhibited sensorimotor area-specific changes, the placebo exhibited spatially distributed changes. The white matter change indicated a significant decrease in the corpus callosum in the verum group. Furthermore, the learning rate of SMR regulation was correlated with the volume changes in the ipsilateral SM1, suggesting the involvement of interhemispheric motor control circuitries in BCI control tasks.}, }
@article {pmid36598169, year = {2023}, author = {Sinha, S and Hashim, H and Finazzi-Agrò, E and Dmochowski, RR and Iacovelli, V}, title = {The bladder contractility and bladder outlet obstruction indices in children. Results of a global Delphi consensus study.}, journal = {Neurourology and urodynamics}, volume = {}, number = {}, pages = {}, doi = {10.1002/nau.25129}, pmid = {36598169}, issn = {1520-6777}, abstract = {AIMS: This Delphi study was planned to examine global expert consensus with regard to utility, accuracy, and categorization of Bladder Contractility Index (BCI), Bladder Outlet Obstruction Index (BOOI), and the related evidence. This manuscript deals with children and follows previous manuscripts reporting on adult men and women.<br><br>METHODS: Eighteen experts were invited to answer the two-round survey including three&#xa0;foundation questions and four&#xa0;survey questions. Consensus was defined as &#x2265;75% agreement. The ordinal scale (0-10) in Round 1 was classified into "strongly agree,"&#xa0;"agree,"&#xa0;"neutral,"&#xa0;"disagree," and "strongly disagree"&#xa0;for the final round. A systematic search for evidence was conducted for therapeutic studies that have examined outcome stratified by the indices in children.<br><br>RESULTS: Eleven experts participated in the survey with 100% completion. Consensus was not noted with regard to any of the questions. There was a general trend toward disagreement with the utility of the BCI and BOOI in children. Systematic search yielded one&#xa0;publication pertaining the value of the indices in predicting long-term outcome in boys treated for posterior urethral valves.<br><br>CONCLUSIONS: This global Delphi survey of experts showed a general disinclination to use numerical indices for bladder contractility and bladder outflow obstruction in children. There is very little data on the use of the BCI and BOOI indices in children. The establishment of urodynamic indices in children might help refine the treatment of functional urological disorders in children.}, }
@article {pmid36595316, year = {2022}, author = {Yasemin, M and Cruz, A and Nunes, UJ and Pires, G}, title = {Single trial detection of error-related potentials in brain-machine interfaces: A survey and comparison of methods.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acabe9}, pmid = {36595316}, issn = {1741-2552}, abstract = {OBJECTIVE: Error-related Potential (ErrP) is a potential elicited in the brain when humans perceive an error. ErrPs have been researched in a variety of contexts, such as to increase the reliability of brain-computer interfaces (BCIs), increase the naturalness of human-machine interaction systems, teach systems, as well as study clinical conditions. Still, there is a significant challenge in detecting ErrP from a single trial, which may hamper its effective use. The literature presents ErrP detection accuracies quite variable across studies, which raises the question of whether this variability depends more on classification pipelines or on the quality of elicited ErrPs (mostly directly related to the underlying paradigms).<br><br>APPROACH: With this purpose, 11 datasets have been used to compare several classification pipelines which were selected according to the studies that reported online performance above 75%. We also analyze the effects of different steps of the pipelines, such as resampling, window selection, augmentation, feature extraction, and classification.<br><br>MAIN RESULTS: From our analysis, we have found that shrinkage-regularized Linear Discriminant Analysis (sLDA) is the most robust method for classification, and for feature extraction, using Fisher Criterion Beamformer (FCB) spatial features and overlapped window averages result in better classification performance. The overall experimental results suggest that classification accuracy is highly dependent on user tasks in BCI experiments and on signal quality (in terms of ErrP morphology, signal-to-noise ratio (SNR), and discrimination).<br><br>SIGNIFICANCE: This study contributes to the BCI research field by responding to the need for a guideline that can direct researchers in designing ErrP-based BCI tasks by accelerating the design steps.}, }
@article {pmid36594734, year = {2023}, author = {Huang, Z and Wu, J and Zhao, Y and Zhang, D and Tong, L and Gao, F and Liu, C and Chen, F}, title = {Starch-based shape memory sponge for rapid hemostasis in penetrating wounds.}, journal = {Journal of materials chemistry. B}, volume = {}, number = {}, pages = {}, doi = {10.1039/d2tb02364d}, pmid = {36594734}, issn = {2050-7518}, abstract = {Death caused by excessive blood loss has always been a global concern. Timely control of bleeding in incompressible penetrated wounds remains a great challenge. Here, we developed a shape memory sponge (SQG) based on modified starch and gelatin (Gel) to control the hemorrhage of penetrating wounds. The porous structure of SQG greatly enhanced the absorption of blood, and the adhesion of erythrocytes and platelets. The water absorption rate of SQG reached 1178.72 ± 12.18% in 10 s. SQG quickly recovered its shape in water (∼3 s) and exhibited high mechanical strength (∼38 kPa), acting as a physically packed barrier to facilitate hemostasis. Furthermore, the positively charged sponges were conducive to activating platelets and promoting the release of coagulation factors. SQG sponges possessed the lowest blood coagulation index (BCI) of 21.32 ± 0.19%, and presented good biocompatibility and obvious inhibitory effect on Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus). Moreover, SQG sponges controlled complete bleeding in 69 ± 20 s and a bleeding loss of 334 ± 138 mg was observed, nearly 50% lower than that of gelatin sponge in rabbit liver penetrating wounds. Overall, SQG possesses a combination of potent shape recovery, rapid hemostasis, and excellent antibacterial and degradation ability, enabling promising applications for hemostasis in non-compressible penetrating wounds.}, }
@article {pmid36591913, year = {2023}, author = {Wang, W and Li, B}, title = {A novel model based on a 1D-ResCNN and transfer learning for processing EEG attenuation.}, journal = {Computer methods in biomechanics and biomedical engineering}, volume = {}, number = {}, pages = {1-14}, doi = {10.1080/10255842.2022.2162339}, pmid = {36591913}, issn = {1476-8259}, abstract = {EEG signals are valuable signals in clinical medicine, brain research, and the study of neurological illnesses. However, EEG signal attenuation may occur at any time from signal generation through BCI device acquisition due to defects in the brain-computer interface (BCI) devices, restrictions in the dynamic network, and individual variations across the subjects. The attenuation of EEG data will alter the data distribution and lead to information fuzziness, substantially influencing subsequent EEG research. A model based on one-dimensional residual convolutional neural networks (1D-ResCNN) and transfer learning is proposed in this article to reduce the negative impacts of EEG attenuation. An end-to-end manner maps an attenuated EEG signal to a normal EEG signal. The structure employs a multi-level residual connection structure with varying weight coefficients, transferring characteristics from the bottom to the top of the convolutional neural network, enhancing feature learning. In addition, we initialize the subsequent denoising model using the transfer learning method. The combination of these two networks can well solve the attenuation problem of EEG signals. Experiments are carried out using the EEG-denoisenet data set. According to the findings, the model can yield a clear waveform with a decent SNR and RRMSE value.}, }
@article {pmid36590466, year = {2022}, author = {Tonin, L and Perdikis, S and Kuzu, TD and Pardo, J and Orset, B and Lee, K and Aach, M and Schildhauer, TA and Martínez-Olivera, R and Millán, JDR}, title = {Learning to control a BMI-driven wheelchair for people with severe tetraplegia.}, journal = {iScience}, volume = {25}, number = {12}, pages = {105418}, pmid = {36590466}, issn = {2589-0042}, abstract = {Mind-controlled wheelchairs are an intriguing assistive mobility solution applicable in complete paralysis. Despite progress in brain-machine interface (BMI) technology, its translation remains elusive. The primary objective of this study is to probe the hypothesis that BMI skill acquisition by end-users is fundamental to control a non-invasive brain-actuated intelligent wheelchair in real-world settings. We demonstrate that three tetraplegic spinal-cord injury users could be trained to operate a non-invasive, self-paced thought-controlled wheelchair and execute complex navigation tasks. However, only the two users exhibiting increasing decoding performance and feature discriminancy, significant neuroplasticity changes and improved BMI command latency, achieved high navigation performance. In addition, we show that dexterous, continuous control of robots is possible through low-degree of freedom, discrete and uncertain control channels like a motor imagery BMI, by blending human and artificial intelligence through shared-control methodologies. We posit that subject learning and shared-control are the key components paving the way for translational non-invasive BMI.}, }
@article {pmid36589747, year = {2022}, author = {Benito-León, M and Gil-Redondo, JC and Perez-Sen, R and Delicado, EG and Ortega, F and Gomez-Villafuertes, R}, title = {BCI, an inhibitor of the DUSP1 and DUSP6 dual specificity phosphatases, enhances P2X7 receptor expression in neuroblastoma cells.}, journal = {Frontiers in cell and developmental biology}, volume = {10}, number = {}, pages = {1049566}, pmid = {36589747}, issn = {2296-634X}, abstract = {P2X7 receptor (P2RX7) is expressed strongly by most human cancers, including neuroblastoma, where high levels of P2RX7 are correlated with a poor prognosis for patients. Tonic activation of P2X7 receptor favors cell metabolism and angiogenesis, thereby promoting cancer cell proliferation, immunosuppression, and metastasis. Although understanding the mechanisms that control P2X7 receptor levels in neuroblastoma cells could be biologically and clinically relevant, the intracellular signaling pathways involved in this regulation remain poorly understood. Here we show that (E)-2-benzylidene-3-(cyclohexylamino)-2,3-dihydro-1H-inden-1-one (BCI), an allosteric inhibitor of dual specificity phosphatases (DUSP) 1 and 6, enhances the expression of P2X7 receptor in N2a neuroblastoma cells. We found that exposure to BCI induces the phosphorylation of mitogen-activated protein kinases p38 and JNK, while it prevents the phosphorylation of ERK1/2. BCI enhanced dual specificity phosphatase 1 expression, whereas it induced a decrease in the dual specificity phosphatase 6 transcripts, suggesting that BCI-dependent inhibition of dual specificity phosphatase 1 may be responsible for the increase in p38 and JNK phosphorylation. The weaker ERK phosphorylation induced by BCI was reversed by p38 inhibition, indicating that this MAPK is involved in the regulatory loop that dampens ERK activity. The PP2A phosphatase appears to be implicated in the p38-dependent dephosphorylation of ERK1/2. In addition, the PTEN phosphatase inhibition also prevented ERK1/2 dephosphorylation, probably through p38 downregulation. By contrast, inhibition of the p53 nuclear factor decreased ERK phosphorylation, probably enhancing the activity of p38. Finally, the inhibition of either p38 or Sp1-dependent transcription halved the increase in P2X7 receptor expression induced by BCI. Moreover, the combined inhibition of both p38 and Sp1 completely prevented the effect exerted by BCI. Together, our results indicate that dual specificity phosphatase 1 acts as a novel negative regulator of P2X7 receptor expression in neuroblastoma cells due to the downregulation of the p38 pathway.}, }
@article {pmid36589278, year = {2022}, author = {Shi, B and Chen, X and Yue, Z and Zeng, F and Yin, S and Wang, B and Wang, J}, title = {Feature optimization based on improved novel global harmony search algorithm for motor imagery electroencephalogram classification.}, journal = {Frontiers in computational neuroscience}, volume = {16}, number = {}, pages = {1004301}, pmid = {36589278}, issn = {1662-5188}, abstract = {BACKGROUND: Effectively decoding electroencephalogram (EEG) pattern for specific mental tasks is a crucial topic in the development of brain-computer interface (BCI). Extracting common spatial pattern (CSP) features from motor imagery EEG signals is often highly dependent on the selection of frequency band and time interval. Therefore, optimizing frequency band and time interval would contribute to effective feature extraction and accurate EEG decoding.<br><br>OBJECTIVE: This study proposes an approach based on an improved novel global harmony search (INGHS) to optimize frequency-time parameters for effective CSP feature extraction.<br><br>METHODS: The INGHS algorithm is applied to find the optimal frequency band and temporal interval. The linear discriminant analysis and support vector machine are used for EEG pattern decoding. Extensive experimental studies are conducted on three EEG datasets to assess the effectiveness of our proposed method.<br><br>RESULTS: The average test accuracy obtained by the time-frequency parameters selected by the proposed INGHS method is slightly better than artificial bee colony (ABC) and particle swarm optimization (PSO) algorithms. Furthermore, the INGHS algorithm is superior to PSO and ABC in running time.<br><br>CONCLUSION: These superior experimental results demonstrate that the optimal frequency band and time interval selected by the INGHS algorithm could significantly improve the decoding accuracy compared with the traditional CSP method. This method has a potential to improve the performance of MI-based BCI systems.}, }
@article {pmid36588886, year = {2022}, author = {Xu, G and Hao, F and Zhao, W and Qiu, J and Zhao, P and Zhang, Q}, title = {The influential factors and non-pharmacological interventions of cognitive impairment in children with ischemic stroke.}, journal = {Frontiers in neurology}, volume = {13}, number = {}, pages = {1072388}, pmid = {36588886}, issn = {1664-2295}, abstract = {BACKGROUND: The prevalence of pediatric ischemic stroke rose by 35% between 1990 and 2013. Affected patients can experience the gradual onset of cognitive impairment in the form of impaired language, memory, intelligence, attention, and processing speed, which affect 20-50% of these patients. Only few evidence-based treatments are available due to significant heterogeneity in age, pathological characteristics, and the combined epilepsy status of the affected children.<br><br>METHODS: We searched the literature published by Web of Science, Scopus, and PubMed, which researched non-pharmacological rehabilitation interventions for cognitive impairment following pediatric ischemic stroke. The search period is from the establishment of the database to January 2022.<br><br>RESULTS: The incidence of such impairment is influenced by patient age, pathological characteristics, combined epilepsy status, and environmental factors. Non-pharmacological treatments for cognitive impairment that have been explored to date mainly include exercise training, psychological intervention, neuromodulation strategies, computer-assisted cognitive training, brain-computer interfaces (BCI), virtual reality, music therapy, and acupuncture. In childhood stroke, the only interventions that can be retrieved are psychological intervention and neuromodulation strategies.<br><br>CONCLUSION: However, evidence regarding the efficacy of these interventions is relatively weak. In future studies, the active application of a variety of interventions to improve pediatric cognitive function will be necessary, and neuroimaging and electrophysiological measurement techniques will be of great value in this context. Larger multi-center prospective longitudinal studies are also required to offer more accurate evidence-based guidance for the treatment of patients with pediatric stroke.}, }
@article {pmid36590862, year = {2019}, author = {Sharpee, TO and Berkowitz, JA}, title = {Linking neural responses to behavior with information-preserving population vectors.}, journal = {Current opinion in behavioral sciences}, volume = {29}, number = {}, pages = {37-44}, pmid = {36590862}, issn = {2352-1546}, abstract = {All systems for processing signals, both artificial and within animals, must obey fundamental statistical laws for how information can be processed. We discuss here recent results using information theory that provide a blueprint for building circuits where signals can be read-out without information loss. Many properties that are necessary to build information-preserving circuits are actually observed in real neurons, at least approximately. One such property is the use of logistic nonlinearity for relating inputs to neural response probability. Such nonlinearities are common in neural and intracellular networks. With this nonlinearity type, there is a linear combination of neural responses that is guaranteed to preserve Shannon information contained in the response of a neural population, no matter how many neurons it contains. This read-out measure is related to a classic quantity known as the population vector that has been quite successful in relating neural responses to animal behavior in a wide variety of cases. Nevertheless, the population vector did not withstand the scrutiny of detailed information-theoretical analyses that showed that it discards substantial amounts of information contained in the responses of a neural population. We discuss recent theoretical results showing how to modify the population vector expression to make it 'information-preserving', and what is necessary in terms of neural circuit organization to allow for lossless information transfer. Implementing these strategies within artificial systems is likely to increase their efficiency, especially for brain-machine interfaces.}, }
@article {pmid36587114, year = {2023}, author = {Pan, W and Huang, X and Yu, Z and Ding, Q and Xia, L and Hua, J and Gu, B and Xiong, Q and Yu, H and Wang, J and Xu, Z and Zeng, L and Bai, G and Liu, H}, title = {Netrin-3 Suppresses Diabetic Neuropathic Pain by Gating the Intra-epidermal Sprouting of Sensory Axons.}, journal = {Neuroscience bulletin}, volume = {}, number = {}, pages = {}, pmid = {36587114}, issn = {1995-8218}, abstract = {Diabetic neuropathic pain (DNP) is the most common disabling complication of diabetes. Emerging evidence has linked the pathogenesis of DNP to the aberrant sprouting of sensory axons into the epidermal area; however, the underlying molecular events remain poorly understood. Here we found that an axon guidance molecule, Netrin-3 (Ntn-3), was expressed in the sensory neurons of mouse dorsal root ganglia (DRGs), and downregulation of Ntn-3 expression was highly correlated with the severity of DNP in a diabetic mouse model. Genetic ablation of Ntn-3 increased the intra-epidermal sprouting of sensory axons and worsened the DNP in diabetic mice. In contrast, the elevation of Ntn-3 levels in DRGs significantly inhibited the intra-epidermal axon sprouting and alleviated DNP in diabetic mice. In conclusion, our studies identified Ntn-3 as an important regulator of DNP pathogenesis by gating the aberrant sprouting of sensory axons, indicating that Ntn-3 is a potential druggable target for DNP treatment.}, }
@article {pmid36586179, year = {2022}, author = {Havaei, P and Zekri, M and Mahmoudzadeh, E and Rabbani, H}, title = {An efficient deep learning framework for P300 evoked related potential detection in EEG signal.}, journal = {Computer methods and programs in biomedicine}, volume = {229}, number = {}, pages = {107324}, doi = {10.1016/j.cmpb.2022.107324}, pmid = {36586179}, issn = {1872-7565}, abstract = {BACKGROUND: Incorporating the time-frequency localization properties of Gabor transform (GT), the complexity understandings of convolutional neural network (CNN), and histogram of oriented gradients (HOG) efficacy in distinguishing positive peaks can exhibit their characteristics to reveal an effective solution in the detection of P300 evoked related potential (ERP). By applying a drastic number of convolutional layers, the majority of deep networks elicit sufficient properties for the output determination, leading to gigantic and time-consuming structures. In this paper, we propose a novel deep learning framework by the combination of tuned GT, and modified HOG with the CNN as "TGT-MHOG-CNN" for detection of P300 ERP in EEG signal.<br><br>METHOD: In the proposed method, GT is tuned based on triangular function for EEG signals, and then spectrograms including time-frequency information are captured. The function's parameters are justified to differentiate the signals with the P300 component. Furthermore, HOG is modified (MHOG) for the 2-D EEG signal, and consequently, gradients patterns are extracted for the target potentials. MHOG is potent in distinguishing the positive peak in the general waveform; however, GT unravels time-frequency information, which is ignored in the gradient histogram. These outputs of GT and MHOG do not share the same nature in the images nor overlap. Therefore, more extensive information is reached without redundancy or excessive information by fusing them. Combining GT and MHOG provides different patterns which benefit CNN for more precise detection. Consequently, TGT-MHOG-CNN ends in a more straightforward structure than other networks, and therefore, the whole performance is acceptable with faster rates and very high accuracy.<br><br>RESULTS: BCI Competition II and III datasets are used to evaluate the performance of the proposed method. These datasets include a complete record for P300 ERP with BCI2000 using a paradigm, and it has numerous noises, including power and muscle-based noises. The objective is to predict the correct character in each provided character selection epochs. Compared to state-of-the-art methods, simulation results indicate striking abilities of the proposed framework for P300 ERP detection. Our best record reached the P300 ERP classification rates of over 98.7% accuracy and 98.7% precision for BCI Competition II and 99% accuracy and 100% precision for BCI Competition III datasets, with superiority in execution time for the mentioned datasets.}, }
@article {pmid36586146, year = {2022}, author = {Borda, E and Medagoda, DI and Airaghi Leccardi, MJI and Zollinger, EG and Ghezzi, D}, title = {Conformable neural interface based on off-stoichiometry thiol-ene-epoxy thermosets.}, journal = {Biomaterials}, volume = {293}, number = {}, pages = {121979}, doi = {10.1016/j.biomaterials.2022.121979}, pmid = {36586146}, issn = {1878-5905}, abstract = {Off-stoichiometry thiol-ene-epoxy (OSTE+) thermosets show low permeability to gases and little absorption of dissolved molecules, allow direct low-temperature dry bonding without surface treatments, have a low Young's modulus, and can be manufactured via UV polymerisation. For these reasons, OSTE+ thermosets have recently gained attention for the rapid prototyping of microfluidic chips. Moreover, their compatibility with standard clean-room processes and outstanding mechanical properties make OSTE+ an excellent candidate as a novel material for neural implants. Here we exploit OSTE+ to manufacture a conformable multilayer micro-electrocorticography array with 16 platinum electrodes coated with platinum black. The mechanical properties allow conformability to curved surfaces such as the brain. The low permeability and strong adhesion between layers improve the stability of the device. Acute experiments in mice show the multimodal capacity of the array to record and stimulate the neural tissue by smoothly conforming to the mouse cortex. Devices are not cytotoxic, and immunohistochemistry stainings reveal only modest foreign body reaction after two and six weeks of chronic implantation. This work introduces OSTE+ as a promising material for implantable neural interfaces.}, }
@article {pmid36583387, year = {2022}, author = {Sato, A and Nakatani, S}, title = {Independent bilateral-eye stimulation for gaze pattern recognition based on steady-state pupil light reflex.}, journal = {Journal of neural engineering}, volume = {19}, number = {6}, pages = {}, doi = {10.1088/1741-2552/acab31}, pmid = {36583387}, issn = {1741-2552}, abstract = {Objective:recently, pupil oscillations synchronized with steady visual stimuli were used as input for an interface. The proposed system, inspired by a brain-computer interface based on steady-state visual evoked potentials, does not require contact with the participant. However, the pupil oscillation mechanism limits the stimulus frequency to 2.5 Hz or less, making it hard to enhance the information transfer rate (ITR).Approach:here, we compared multiple conditions for stimulation to increase the ITR of the pupil vibration-based interface, which were called monocular-single, monocular-superposed, and binocular-independent conditions. The binocular-independent condition stimulates each eye at different frequencies respectively and mixes them by using the visual stereoscopic perception of users. The monocular-superposed condition stimulates both eyes by a mixed signal of two different frequencies. We selected the shape of the stimulation signal, evaluated the amount of spectral leakage in the monocular-superposed and binocular-independent conditions, and compared the power spectrum density at the stimulation frequency. Moreover, 5, 10, and 15 patterns of stimuli were classified in each condition.Main results:a square wave, which causes an efficient pupil response, was used as the stimulus. Spectral leakage at the beat frequency was higher in the monocular-superposed condition than in the binocular-independent one. The power spectral density of stimulus frequencies was greatest in the monocular-single condition. Finally, we could classify the 15-stimulus pattern, with ITRs of 14.4 (binocular-independent, using five frequencies), 14.5 (monocular-superimposed, using five frequencies), and 23.7 bits min[-1](monocular-single, using 15 frequencies). There were no significant differences for the binocular-independent and monocular-superposed conditions.Significance:this paper shows a way to increase the number of stimuli that can be simultaneously displayed without decreasing ITR, even when only a small number of frequencies are available. This could lead to the provision of an interface based on pupil oscillation to a wider range of users.}, }
@article {pmid36583011, year = {2022}, author = {Phunruangsakao, C and Achanccaray, D and Izumi, SI and Hayashibe, M}, title = {Multibranch convolutional neural network with contrastive representation learning for decoding same limb motor imagery tasks.}, journal = {Frontiers in human neuroscience}, volume = {16}, number = {}, pages = {1032724}, pmid = {36583011}, issn = {1662-5161}, abstract = {INTRODUCTION: Emerging deep learning approaches to decode motor imagery (MI) tasks have significantly boosted the performance of brain-computer interfaces. Although recent studies have produced satisfactory results in decoding MI tasks of different body parts, the classification of such tasks within the same limb remains challenging due to the activation of overlapping brain regions. A single deep learning model may be insufficient to effectively learn discriminative features among tasks.<br><br>METHODS: The present study proposes a framework to enhance the decoding of multiple hand-MI tasks from the same limb using a multi-branch convolutional neural network. The CNN framework utilizes feature extractors from established deep learning models, as well as contrastive representation learning, to derive meaningful feature representations for classification.<br><br>RESULTS: The experimental results suggest that the proposed method outperforms several state-of-the-art methods by obtaining a classification accuracy of 62.98% with six MI classes and 76.15 % with four MI classes on the Tohoku University MI-BCI and BCI Competition IV datasets IIa, respectively.<br><br>DISCUSSION: Despite requiring heavy data augmentation and multiple optimization steps, resulting in a relatively long training time, this scheme is still suitable for online use. However, the trade-of between the number of base learners, training time, prediction time, and system performance should be carefully considered.}, }
@article {pmid36582164, year = {2022}, author = {Kotov, SV and Slyunkova, EV and Borisova, VA and Isakova, EV}, title = {[Effectiveness of brain-computer interfaces and cognitive training using computer technologies in restoring cognitive functions in patients after stroke].}, journal = {Zhurnal nevrologii i psikhiatrii imeni S.S. Korsakova}, volume = {122}, number = {12. Vyp. 2}, pages = {67-75}, doi = {10.17116/jnevro202212212267}, pmid = {36582164}, issn = {1997-7298}, abstract = {OBJECTIVE: To study the effectiveness of brain-computer interfaces (BCI) and cognitive training using computer technologies in restoring cognitive functions in poststroke patients.<br><br>MATERIAL AND METHODS: Thirty-four stroke patients (mean age 59.3&#xb1;10.8 years) with stroke duration of 5.1&#xb1;4.7 months, were included. To assess the effectiveness of treatment, patients before and after treatment were tested using memorization of words according to the method of Luria A.R. &#xab;10 words&#xbb;, the Montreal Cognitive Assessment Scale (MoCA), the Clock Drawing Test (CDT). All patients received standard rehabilitation therapy (exercise therapy, physiotherapy, sessions with a speech therapist-neuropsychologist). Patients of the first group additionally received training on the &#xab;Neurochat&#xbb; complex, patients of the second group - on the &#xab;Exokist-2&#xbb; complex, patients of the third group - cognitive training according to standard programs using computer technology and visual material.<br><br>RESULTS: Patients of the three groups showed a significant improvement in the total MoCA score: in the 1[st] and 2[nd] groups - p<0.01, in the 3[rd] group - p<0.05. According to CDT, there was a significant change in the 2[nd] group (p=0.018). The Luria method &#xab;10 words&#xbb; revealed an improvement in memory in all groups (p<0.01, p<0.05), being more pronounced in the 1[st] and 2[nd] groups.<br><br>CONCLUSION: The effectiveness of BCI in restoring cognitive functions in patients after a stroke in comparison with cognitive training without BCI has been demonstrated. However, there are reasons to believe that various BCIs have a specific effect on cognitive functions and have their own target group.}, }
@article {pmid36582163, year = {2022}, author = {Borisova, VA and Isakova, EV and Kotov, SV}, title = {[Possibilities of the brain-computer interface in the correction of post-stroke cognitive impairments].}, journal = {Zhurnal nevrologii i psikhiatrii imeni S.S. Korsakova}, volume = {122}, number = {12. Vyp. 2}, pages = {60-66}, doi = {10.17116/jnevro202212212260}, pmid = {36582163}, issn = {1997-7298}, abstract = {In recent years, brain-computer interfaces have been widely used in neurorehabilitation, and an extensive database of results from clinical studies conducted around the world has been accumulated, demonstrating their effectiveness in restoring motor function after a stroke. Currently, their use in post-stroke cognitive impairment is expanding. This article discusses the potential and prospects for using brain-computer interfaces for the treatment of cognitive disorders, reviews the experience of using it, presents the results of clinical studies in stroke patients, evaluates the possibilities of using this technology, describes the prospects, new directions of work on studying its effects.}, }
@article {pmid36579369, year = {2022}, author = {Goueytes, D and Lassagne, H and Shulz, DE and Ego-Stengel, V and Estebanez, L}, title = {Learning in a closed-loop brain-machine interface with distributed optogenetic cortical feedback.}, journal = {Journal of neural engineering}, volume = {19}, number = {6}, pages = {}, doi = {10.1088/1741-2552/acab87}, pmid = {36579369}, issn = {1741-2552}, abstract = {Objective.Distributed microstimulations at the cortical surface can efficiently deliver feedback to a subject during the manipulation of a prosthesis through a brain-machine interface (BMI). Such feedback can convey vast amounts of information to the prosthesis user and may be key to obtain an accurate control and embodiment of the prosthesis. However, so far little is known of the physiological constraints on the decoding of such patterns. Here, we aimed to test a rotary optogenetic feedback that was designed to encode efficiently the 360° movements of the robotic actuators used in prosthetics. We sought to assess its use by mice that controlled a prosthesis joint through a closed-loop BMI.Approach.We tested the ability of mice to optimize the trajectory of a virtual prosthesis joint in order to solve a rewarded reaching task. They could control the speed of the joint by modulating the activity of individual neurons in the primary motor cortex. During the task, the patterned optogenetic stimulation projected on the primary somatosensory cortex continuously delivered information to the mouse about the position of the joint.Main results.We showed that mice are able to exploit the continuous, rotating cortical feedback in the active behaving context of the task. Mice achieved better control than in the absence of feedback by detecting reward opportunities more often, and also by moving the joint faster towards the reward angular zone, and by maintaining it longer in the reward zone. Mice controlling acceleration rather than speed of the joint failed to improve motor control.Significance.These findings suggest that in the context of a closed-loop BMI, distributed cortical feedback with optimized shapes and topology can be exploited to control movement. Our study has direct applications on the closed-loop control of rotary joints that are frequently encountered in robotic prostheses.}, }
@article {pmid36578777, year = {2022}, author = {Alwasiti, H and Yusoff, MZ}, title = {Motor Imagery Classification for Brain Computer Interface Using Deep Convolutional Neural Networks and Mixup Augmentation.}, journal = {IEEE open journal of engineering in medicine and biology}, volume = {3}, number = {}, pages = {171-177}, pmid = {36578777}, issn = {2644-1276}, abstract = {Goal: Building a DL model that can be trained on small EEG training set of a single subject presents an interesting challenge that this work is trying to address. In particular, this study is trying to avoid the need for long EEG data collection sessions, and without combining multiple subjects training datasets, which has a detrimental effect on the classification performance due to the inter-individual variability among subjects. Methods: A customized Convolutional Neural Network with mixup augmentation was trained with [Formula: see text]120 EEG trials for only one subject per model. Results: Modified ResNet18 and DenseNet121 models with mixup augmentation achieved 0.920 (95% Confidence Interval: 0.908, 0.933) and 0.933 (95% Confidence Interval: 0.922, 0.945) classification accuracy, respectively. Conclusions: We show that the designed classifiers resulted in a higher classification performance in comparison to other DL classifiers of previous studies on the same dataset, despite the limited training dataset used in this work.}, }
@article {pmid36577882, year = {2022}, author = {Cao, K and Hu, Y and Gao, Z}, title = {Sense to Tune: Engaging Microglia with Dynamic Neuronal Activity.}, journal = {Neuroscience bulletin}, volume = {}, number = {}, pages = {}, pmid = {36577882}, issn = {1995-8218}, }
@article {pmid36577144, year = {2022}, author = {Duan, X and Xie, S and Lv, Y and Xie, X and Obermayer, K and Yan, H}, title = {A transfer learning-based feedback training motivates the performance of SMR-BCI.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acaee7}, pmid = {36577144}, issn = {1741-2552}, abstract = {OBJECTIVE: Feedback training is a practical approach to brain-computer interface (BCI) end-users learning to modulate their sensorimotor rhythms (SMR). BCI self-regulation learning has been shown to be influenced by subjective psychological factors, such as motivation. However, few studies have taken into account the users' self-motivation as additional guidance for the cognitive process involved in BCI learning. In this study we tested a transfer learning (TL) feedback method designed to increase self-motivation by providing information about past performance.<br><br>APPROACH: EEG signals from the previous runs were affine transformed and displayed as points on the screen, along with the newly recorded EEG signals in the current run, giving the subjects a context for self-motivation. Subjects were asked to separate the feedback points for the current run under the display of the separability of prior training. We conducted a between-subject feedback training experiment, in which 24 healthy SMR-BCI naive subjects were trained to imagine left- and right-hand movements. The participants were provided with either TL feedback or typical cursorbar (CB) feedback (control condition), for three sessions on separate days.<br><br>MAIN RESULTS: The behavioral results showed an increased challenge and stable mastery confidence, suggesting that subjects' motivation grew as the feedback training went on. The EEG results showed favorable overall training effects with TL feedback in terms of the class distinctiveness and EEG discriminancy. Performance was 28.5% higher in the third session than in the first. 41.7% of the subjects were learners including not only low-performance subjects, but also good-performance subjects who might be affected by the ceiling effect. Subjects were able to control BCI with TL feedback with a higher performance of 60.5% during the last session compared to CB feedback.<br><br>SIGNIFICANCE: The present study demonstrated that the proposed TL feedback method boosted psychological engagement through the self-motivated context, and further allowed subjects to modulate SMR effectively. The proposed TL feedback method also provided an alternative to typical CB feedback.}, }
@article {pmid36576451, year = {2022}, author = {Rinoldi, C and Ziai, Y and Zargarian, SS and Nakielski, P and Zembrzycki, K and Haghighat Bayan, MA and Zakrzewska, AB and Fiorelli, R and Lanzi, M and Kostrzewska-Księżyk, A and Czajkowski, R and Kublik, E and Kaczmarek, L and Pierini, F}, title = {In Vivo Chronic Brain Cortex Signal Recording Based on a Soft Conductive Hydrogel Biointerface.}, journal = {ACS applied materials &amp; interfaces}, volume = {}, number = {}, pages = {}, doi = {10.1021/acsami.2c17025}, pmid = {36576451}, issn = {1944-8252}, abstract = {In neuroscience, the acquisition of neural signals from the brain cortex is crucial to analyze brain processes, detect neurological disorders, and offer therapeutic brain-computer interfaces. The design of neural interfaces conformable to the brain tissue is one of today's major challenges since the insufficient biocompatibility of those systems provokes a fibrotic encapsulation response, leading to an inaccurate signal recording and tissue damage precluding long-term/permanent implants. The design and production of a novel soft neural biointerface made of polyacrylamide hydrogels loaded with plasmonic silver nanocubes are reported herein. Hydrogels are surrounded by a silicon-based template as a supporting element for guaranteeing an intimate neural-hydrogel contact while making possible stable recordings from specific sites in the brain cortex. The nanostructured hydrogels show superior electroconductivity while mimicking the mechanical characteristics of the brain tissue. Furthermore, in vitro biological tests performed by culturing neural progenitor cells demonstrate the biocompatibility of hydrogels along with neuronal differentiation. In vivo chronic neuroinflammation tests on a mouse model show no adverse immune response toward the nanostructured hydrogel-based neural interface. Additionally, electrocorticography acquisitions indicate that the proposed platform permits long-term efficient recordings of neural signals, revealing the suitability of the system as a chronic neural biointerface.}, }
@article {pmid36575664, year = {2022}, author = {Guo, Z and Wang, F and Wang, L and Tu, K and Jiang, C and Xi, Y and Hong, W and Xu, Q and Wang, X and Yang, B and Sun, B and Lin, Z and Liu, J}, title = {A flexible neural implant with ultrathin substrate for low-invasive brain-computer interface applications.}, journal = {Microsystems &amp; nanoengineering}, volume = {8}, number = {}, pages = {133}, pmid = {36575664}, issn = {2055-7434}, abstract = {Implantable brain-computer interface (BCI) devices are an effective tool to decipher fundamental brain mechanisms and treat neural diseases. However, traditional neural implants with rigid or bulky cross-sections cause trauma and decrease the quality of the neuronal signal. Here, we propose a MEMS-fabricated flexible interface device for BCI applications. The microdevice with a thin film substrate can be readily reduced to submicron scale for low-invasive implantation. An elaborate silicon shuttle with an improved structure is designed to reliably implant the flexible device into brain tissue. The flexible substrate is temporarily bonded to the silicon shuttle by polyethylene glycol. On the flexible substrate, eight electrodes with different diameters are distributed evenly for local field potential and neural spike recording, both of which are modified by Pt-black to enhance the charge storage capacity and reduce the impedance. The mechanical and electrochemical characteristics of this interface were investigated in vitro. In vivo, the small cross-section of the device promises reduced trauma, and the neuronal signals can still be recorded one month after implantation, demonstrating the promise of this kind of flexible BCI device as a low-invasive tool for brain-computer communication.}, }
@article {pmid36575091, year = {2022}, author = {Wang, H and Wang, S and Qiu, Z and Zhang, Q and Xu, S}, title = {[Design and preliminary application of outdoor flying pigeon-robot].}, journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi}, volume = {39}, number = {6}, pages = {1209-1217}, doi = {10.7507/1001-5515.202207077}, pmid = {36575091}, issn = {1001-5515}, abstract = {Control at beyond-visual ranges is of great significance to animal-robots with wide range motion capability. For pigeon-robots, such control can be done by the way of onboard preprogram, but not constitute a closed-loop yet. This study designed a new control system for pigeon-robots, which integrated the function of trajectory monitoring to that of brain stimulation. It achieved the closed-loop control in turning or circling by estimating pigeons' flight state instantaneously and the corresponding logical regulation. The stimulation targets located at the formation reticularis medialis mesencephali (FRM) in the left and right brain, for the purposes of left- and right-turn control, respectively. The stimulus was characterized by the waveform mimicking the nerve cell membrane potential, and was activated intermittently. The wearable control unit weighted 11.8 g totally. The results showed a 90% success rate by the closed-loop control in pigeon-robots. It was convenient to obtain the wing shape during flight maneuver, by equipping a pigeon-robot with a vivo camera. It was also feasible to regulate the evolution of pigeon flocks by the pigeon-robots at different hierarchical level. All of these lay the groundwork for the application of pigeon-robots in scientific researches.}, }
@article {pmid36575087, year = {2022}, author = {Pan, L and Ding, Y and Wang, S and Song, A}, title = {[Research on the feature representation of motor imagery electroencephalogram signal based on individual adaptation].}, journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi}, volume = {39}, number = {6}, pages = {1173-1180}, doi = {10.7507/1001-5515.202112023}, pmid = {36575087}, issn = {1001-5515}, abstract = {Aiming at the problem of low recognition accuracy of motor imagery electroencephalogram signal due to individual differences of subjects, an individual adaptive feature representation method of motor imagery electroencephalogram signal is proposed in this paper. Firstly, based on the individual differences and signal characteristics in different frequency bands, an adaptive channel selection method based on expansive relevant features with label F (ReliefF) was proposed. By extracting five time-frequency domain observation features of each frequency band signal, ReliefF algorithm was employed to evaluate the effectiveness of the frequency band signal in each channel, and then the corresponding signal channel was selected for each frequency band. Secondly, a feature representation method of common space pattern (CSP) based on fast correlation-based filter (FCBF) was proposed (CSP-FCBF). The features of electroencephalogram signal were extracted by CSP, and the best feature sets were obtained by using FCBF to optimize the features, so as to realize the effective state representation of motor imagery electroencephalogram signal. Finally, support vector machine (SVM) was adopted as a classifier to realize identification. Experimental results show that the proposed method in this research can effectively represent the states of motor imagery electroencephalogram signal, with an average identification accuracy of (83.0±5.5)% for four types of states, which is 6.6% higher than the traditional CSP feature representation method. The research results obtained in the feature representation of motor imagery electroencephalogram signal lay the foundation for the realization of adaptive electroencephalogram signal decoding and its application.}, }
@article {pmid36575075, year = {2022}, author = {Song, H and Xu, S and Liu, G and Liu, J and Xiong, P}, title = {[Automatic removal algorithm of electrooculographic artifacts in non-invasive brain-computer interface based on independent component analysis].}, journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi}, volume = {39}, number = {6}, pages = {1074-1081}, doi = {10.7507/1001-5515.202111060}, pmid = {36575075}, issn = {1001-5515}, abstract = {The non-invasive brain-computer interface (BCI) has gradually become a hot spot of current research, and it has been applied in many fields such as mental disorder detection and physiological monitoring. However, the electroencephalography (EEG) signals required by the non-invasive BCI can be easily contaminated by electrooculographic (EOG) artifacts, which seriously affects the analysis of EEG signals. Therefore, this paper proposed an improved independent component analysis method combined with a frequency filter, which automatically recognizes artifact components based on the correlation coefficient and kurtosis dual threshold. In this method, the frequency difference between EOG and EEG was used to remove the EOG information in the artifact component through frequency filter, so as to retain more EEG information. The experimental results on the public datasets and our laboratory data showed that the method in this paper could effectively improve the effect of EOG artifact removal and improve the loss of EEG information, which is helpful for the promotion of non-invasive BCI.}, }
@article {pmid36575074, year = {2022}, author = {Hu, Y and Liu, Y and Cheng, C and Geng, C and Dai, B and Peng, B and Zhu, J and Dai, Y}, title = {[Multi-task motor imagery electroencephalogram classification based on adaptive time-frequency common spatial pattern combined with convolutional neural network].}, journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi}, volume = {39}, number = {6}, pages = {1065-1073}, doi = {10.7507/1001-5515.202206052}, pmid = {36575074}, issn = {1001-5515}, abstract = {The effective classification of multi-task motor imagery electroencephalogram (EEG) is helpful to achieve accurate multi-dimensional human-computer interaction, and the high frequency domain specificity between subjects can improve the classification accuracy and robustness. Therefore, this paper proposed a multi-task EEG signal classification method based on adaptive time-frequency common spatial pattern (CSP) combined with convolutional neural network (CNN). The characteristics of subjects' personalized rhythm were extracted by adaptive spectrum awareness, and the spatial characteristics were calculated by using the one-versus-rest CSP, and then the composite time-domain characteristics were characterized to construct the spatial-temporal frequency multi-level fusion features. Finally, the CNN was used to perform high-precision and high-robust four-task classification. The algorithm in this paper was verified by the self-test dataset containing 10 subjects (33 ± 3 years old, inexperienced) and the dataset of the 4th 2018 Brain-Computer Interface Competition (BCI competition Ⅳ-2a). The average accuracy of the proposed algorithm for the four-task classification reached 93.96% and 84.04%, respectively. Compared with other advanced algorithms, the average classification accuracy of the proposed algorithm was significantly improved, and the accuracy range error between subjects was significantly reduced in the public dataset. The results show that the proposed algorithm has good performance in multi-task classification, and can effectively improve the classification accuracy and robustness.}, }
@article {pmid36572173, year = {2022}, author = {Zhu, X and Zhou, H and Geng, F and Wang, J and Xu, H and Hu, Y}, title = {Functional connectivity between basal forebrain and superficial amygdala negatively correlates with social fearfulness.}, journal = {Neuroscience}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.neuroscience.2022.12.020}, pmid = {36572173}, issn = {1873-7544}, abstract = {Social anxiety is characterized by an intense fear of evaluation from others and/or withdrawal from social situations. Extreme social anxiety can lead to social anxiety disorder. There remains an urgent need to investigate the neural substrates of subclinical social anxiety for early diagnosis and intervention to reduce the risk to develop social anxiety disorder. Twenty-nine young adults were recruited (10 males/19 females; mean age (SD) = 20.34 (2.29)). Trait-like social anxiety was assessed by Liebowitz Social Anxiety Scale. Functional magnetic resonance imaging was used with an emotional face-matching paradigm to probe brain activation in response to emotional stimuli including angry, fearful, and happy faces, with shape-matching as a control condition. Behavioral results showed positive correlations between Liebowitz Social Anxiety Scale scores and the reaction time in both angry and fearful conditions. The activation of superficial amygdala and the deactivation of basal forebrain in response to angry condition showed positive correlations with the level of social anxiety. In addition, the resting-state functional connectivity between these two regions was negatively correlated with the level of social anxiety. These results may help to understand the individual difference and corresponding neural underpinnings of social anxiety in the subclinical population, and might provide some insight to develop strategies for early diagnosis and interventions of social anxiety to reduce the risk of deterioration from subclinical to clinical level of social anxiety.}, }
@article {pmid36569472, year = {2022}, author = {Lee Friesen, C and Lawrence, M and Ingram, TGJ and Boe, SG}, title = {Home-based portable fNIRS-derived cortical laterality correlates with impairment and function in chronic stroke.}, journal = {Frontiers in human neuroscience}, volume = {16}, number = {}, pages = {1023246}, pmid = {36569472}, issn = {1662-5161}, abstract = {INTRODUCTION: Improved understanding of the relationship between post-stroke rehabilitation interventions and functional motor outcomes could result in improvements in the efficacy of post-stroke physical rehabilitation. The laterality of motor cortex activity (M1-LAT) during paretic upper-extremity movement has been documented as a useful biomarker of post-stroke motor recovery. However, the expensive, labor intensive, and laboratory-based equipment required to take measurements of M1-LAT limit its potential clinical utility in improving post-stroke physical rehabilitation. The present study tested the ability of a mobile functional near-infrared spectroscopy (fNIRS) system (designed to enable independent measurement by stroke survivors) to measure cerebral hemodynamics at the motor cortex in the homes of chronic stroke survivors.<br><br>METHODS: Eleven chronic stroke survivors, ranging widely in their level of upper-extremity motor deficit, used their stroke-affected upper-extremity to perform a simple unilateral movement protocol in their homes while a wireless prototype fNIRS headband took measurements at the motor cortex. Measures of participants' upper-extremity impairment and function were taken.<br><br>RESULTS: Participants demonstrated either a typically lateralized response, with an increase in contralateral relative oxyhemoglobin (&#x394;HbO), or response showing a bilateral pattern of increase in &#x394;HbO during the motor task. During the simple unilateral task, M1-LAT correlated significantly with measures of both upper-extremity impairment and function, indicating that participants with more severe motor deficits had more a more atypical (i.e., bilateral) pattern of lateralization.<br><br>DISCUSSION: These results indicate it is feasible to gain M1-LAT measures from stroke survivors in their homes using fNIRS. These findings represent a preliminary step toward the goals of using ergonomic functional neuroimaging to improve post-stroke rehabilitative care, via the capture of neural biomarkers of post-stroke motor recovery, and/or via use as part of an accessible rehabilitation brain-computer-interface.}, }
@article {pmid36560172, year = {2022}, author = {Kartsch, VJ and Kumaravel, VP and Benatti, S and Vallortigara, G and Benini, L and Farella, E and Buiatti, M}, title = {Efficient Low-Frequency SSVEP Detection with Wearable EEG Using Normalized Canonical Correlation Analysis.}, journal = {Sensors (Basel, Switzerland)}, volume = {22}, number = {24}, pages = {}, pmid = {36560172}, issn = {1424-8220}, support = {842243/ERC_/European Research Council/International ; }, mesh = {Electroencephalography/methods ; Evoked Potentials, Visual ; Canonical Correlation Analysis ; *Brain-Computer Interfaces ; Photic Stimulation/methods ; *Wearable Electronic Devices ; Algorithms ; }, abstract = {Recent studies show that the integrity of core perceptual and cognitive functions may be tested in a short time with Steady-State Visual Evoked Potentials (SSVEP) with low stimulation frequencies, between 1 and 10 Hz. Wearable EEG systems provide unique opportunities to test these brain functions on diverse populations in out-of-the-lab conditions. However, they also pose significant challenges as the number of EEG channels is typically limited, and the recording conditions might induce high noise levels, particularly for low frequencies. Here we tested the performance of Normalized Canonical Correlation Analysis (NCCA), a frequency-normalized version of CCA, to quantify SSVEP from wearable EEG data with stimulation frequencies ranging from 1 to 10 Hz. We validated NCCA on data collected with an 8-channel wearable wireless EEG system based on BioWolf, a compact, ultra-light, ultra-low-power recording platform. The results show that NCCA correctly and rapidly detects SSVEP at the stimulation frequency within a few cycles of stimulation, even at the lowest frequency (4 s recordings are sufficient for a stimulation frequency of 1 Hz), outperforming a state-of-the-art normalized power spectral measure. Importantly, no preliminary artifact correction or channel selection was required. Potential applications of these results to research and clinical studies are discussed.}, }
@article {pmid36567362, year = {2022}, author = {Sorkhi, M and Jahed-Motlagh, MR and Minaei-Bidgoli, B and Daliri, MR}, title = {Hybrid fuzzy deep neural network toward temporal-spatial-frequency features learning of motor imagery signals.}, journal = {Scientific reports}, volume = {12}, number = {1}, pages = {22334}, pmid = {36567362}, issn = {2045-2322}, abstract = {Achieving an efficient and reliable method is essential to interpret a user's brain wave and deliver an accurate response in biomedical signal processing. However, EEG patterns exhibit high variability across time and uncertainty due to noise and it is a significant problem to be addressed in mental task as motor imagery. Therefore, fuzzy components may help to enable a higher tolerance to noisy conditions. With the advent of Deep Learning and its considerable contributions to Artificial intelligence and data analysis, numerous efforts have been made to evaluate and analyze brain signals. In this study, to make use of neural activity phenomena, the feature extraction preprocessing is applied based on Multi-scale filter bank CSP. In the following, the hybrid series architecture named EEG-CLFCNet is proposed which extract the frequency and spatial features by Compact-CNN and the temporal features by the LSTM network. However, the classification results are evaluated by merging the fully connected network and fuzzy neural block. Here, the proposed method is further validated by the BCI competition IV-2a dataset and compare with two hyperparameter tuning methods, Coordinate-descent and Bayesian optimization algorithm. The proposed architecture that used fuzzy neural block and Bayesian optimization as tuning approach, results in better classification accuracy compared with the state-of-the-art literatures. As results shown, the remarkable performance of the proposed model, EEG-CLFCNet, and the general integration of fuzzy units to other classifiers would pave the way for enhanced MI-based BCI systems.}, }
@article {pmid36565984, year = {2022}, author = {West, TO and Duchet, B and Farmer, SF and Friston, KJ and Cagnan, H}, title = {When do Bursts Matter in the Primary Motor Cortex? Investigating Changes in the Intermittencies of Beta Rhythms Associated With Movement States.}, journal = {Progress in neurobiology}, volume = {}, number = {}, pages = {102397}, doi = {10.1016/j.pneurobio.2022.102397}, pmid = {36565984}, issn = {1873-5118}, abstract = {Brain activity exhibits significant temporal structure that is not well captured in the power spectrum. Recently, attention has shifted to characterising the properties of intermittencies in rhythmic neural activity (i.e. bursts), yet the mechanisms regulating them are unknown. Here, we present evidence from electrocorticography recordings made from the motor cortex to show that the statistics of bursts, such as duration or amplitude, in beta frequency (14-30Hz) rhythms significantly aid the classification of motor states such as rest, movement preparation, execution, and imagery. These features reflect nonlinearities not detectable in the power spectrum, with states increasing in nonlinearity from movement execution to preparation to rest. Further, we show using a computational model of the cortical microcircuit, constrained to account for burst features, that modulations of laminar specific inhibitory interneurons are responsible for temporal organization of activity. Finally, we show that temporal characteristics of spontaneous activity can be used to infer the balance of cortical integration between incoming sensory information and endogenous activity. Critically, we contribute to the understanding of how transient brain rhythms may underwrite cortical processing, which in turn, could inform novel approaches for brain state classification, and modulation with novel brain-computer interfaces.}, }
@article {pmid36563409, year = {2022}, author = {Liang, Q and Shen, Z and Sun, X and Yu, D and Liu, K and Mugo, SM and Chen, W and Wang, D and Zhang, Q}, title = {Electron Conductive and Transparent Hydrogels for Recording Brain Neural Signals and Neuromodulation.}, journal = {Advanced materials (Deerfield Beach, Fla.)}, volume = {}, number = {}, pages = {e2211159}, doi = {10.1002/adma.202211159}, pmid = {36563409}, issn = {1521-4095}, abstract = {Recording brain neural signals and optogenetic neuromodulations open frontiers in decoding brain neural information and neurodegenerative diseases therapeutics. Conventional implantable probes suffer from modulus mismatch with biological tissues and an irreconcilable tradeoff between transparency and electron conductivity. Herein, a strategy was proposed to address these tradeoffs, which generates conductive and transparent hydrogels with polypyrrole-decorated microgels as crosslinkers. The optical transparency of the electrodes can be attributed to the special structures that allow light waves to bypass the microgel particles and minimize their interaction. Demonstrated by probing the hippocampus of rat brains, the biomimetic electrode shows a prolonged capacity for simultaneous optogenetic neuromodulation and recording of brain neural signals. More importantly, an intriguing brain-machine interaction was realized, which involved signal input to the brain, brain neural signal generation, and controlling limb behaviors. This breakthrough work represents a significant scientific advancement toward decoding brain neural information and in neurodegenerative disease therapy. This article is protected by copyright. All rights reserved.}, }
@article {pmid36560369, year = {2022}, author = {Akram, F and Alwakeel, A and Alwakeel, M and Hijji, M and Masud, U}, title = {A Symbols Based BCI Paradigm for Intelligent Home Control Using P300 Event-Related Potentials.}, journal = {Sensors (Basel, Switzerland)}, volume = {22}, number = {24}, pages = {}, doi = {10.3390/s222410000}, pmid = {36560369}, issn = {1424-8220}, abstract = {Brain-Computer Interface (BCI) is a technique that allows the disabled to interact with a computer directly from their brain. P300 Event-Related Potentials (ERP) of the brain have widely been used in several applications of the BCIs such as character spelling, word typing, wheelchair control for the disabled, neurorehabilitation, and smart home control. Most of the work done for smart home control relies on an image flashing paradigm where six images are flashed randomly, and the users can select one of the images to control an object of interest. The shortcoming of such a scheme is that the users have only six commands available in a smart home to control. This article presents a symbol-based P300-BCI paradigm for controlling home appliances. The proposed paradigm comprises of a 12-symbols, from which users can choose one to represent their desired command in a smart home. The proposed paradigm allows users to control multiple home appliances from signals generated by the brain. The proposed paradigm also allows the users to make phone calls in a smart home environment. We put our smart home control system to the test with ten healthy volunteers, and the findings show that the proposed system can effectively operate home appliances through BCI. Using the random forest classifier, our participants had an average accuracy of 92.25 percent in controlling the home devices. As compared to the previous studies on the smart home control BCIs, the proposed paradigm gives the users more degree of freedom, and the users are not only able to control several home appliances but also have an option to dial a phone number and make a call inside the smart home. The proposed symbols-based smart home paradigm, along with the option of making a phone call, can effectively be used for controlling home through signals of the brain, as demonstrated by the results.}, }
@article {pmid36560158, year = {2022}, author = {Saichoo, T and Boonbrahm, P and Punsawad, Y}, title = {Investigating User Proficiency of Motor Imagery for EEG-Based BCI System to Control Simulated Wheelchair.}, journal = {Sensors (Basel, Switzerland)}, volume = {22}, number = {24}, pages = {}, doi = {10.3390/s22249788}, pmid = {36560158}, issn = {1424-8220}, abstract = {The research on the electroencephalography (EEG)-based brain-computer interface (BCI) is widely utilized for wheelchair control. The ability of the user is one factor of BCI efficiency. Therefore, we focused on BCI tasks and protocols to yield high efficiency from the robust EEG features of individual users. This study proposes a task-based brain activity to gain the power of the alpha band, which included eyes closed for alpha response at the occipital area, attention to an upward arrow for alpha response at the frontal area, and an imagined left/right motor for alpha event-related desynchronization at the left/right motor cortex. An EPOC X neuroheadset was used to acquire the EEG signals. We also proposed user proficiency in motor imagery sessions with limb movement paradigms by recommending motor imagination tasks. Using the proposed system, we verified the feature extraction algorithms and command translation. Twelve volunteers participated in the experiment, and the conventional paradigm of motor imagery was used to compare the efficiencies. With utilized user proficiency in motor imagery, an average accuracy of 83.7% across the left and right commands was achieved. The recommended MI paradigm via user proficiency achieved an approximately 4% higher accuracy than the conventional MI paradigm. Moreover, the real-time control results of a simulated wheelchair revealed a high efficiency based on the time condition. The time results for the same task as the joystick-based control were still approximately three times longer. We suggest that user proficiency be used to recommend an individual MI paradigm for beginners. Furthermore, the proposed BCI system can be used for electric wheelchair control by people with severe disabilities.}, }
@article {pmid36557761, year = {2022}, author = {Kiel, A and Creutz, I and Rückert, C and Kaltschmidt, BP and Hütten, A and Niehaus, K and Busche, T and Kaltschmidt, B and Kaltschmidt, C}, title = {Genome-Based Analysis of Virulence Factors and Biofilm Formation in Novel P. aeruginosa Strains Isolated from Household Appliances.}, journal = {Microorganisms}, volume = {10}, number = {12}, pages = {}, doi = {10.3390/microorganisms10122508}, pmid = {36557761}, issn = {2076-2607}, abstract = {In household washing machines, opportunistic pathogens such as Pseudomonas aeruginosa are present, which represent the household as a possible reservoir for clinical pathogens. Here, four novel P. aeruginosa strains, isolated from different sites of household appliances, were investigated regarding their biofilm formation. Only two isolates showed strong surface-adhered biofilm formation. In consequence of these phenotypic differences, we performed whole genome sequencing using Oxford Nanopore Technology together with Illumina MiSeq. Whole genome data were screened for the prevalence of 285 virulence- and biofilm-associated genes as well as for prophages. Linking biofilm phenotypes and parallelly appearing gene compositions, we assume a relevancy of the las quorum sensing system and the phage-encoded bacteriophage control infection gene bci, which was found on integrated phi297 DNA in all biofilm-forming isolates. Additionally, only the isolates revealing strong biofilm formation harbored the ϕCTX-like prophage Dobby, implicating a role of this prophage on biofilm formation. Investigations on clinically relevant pathogens within household appliances emphasize their adaptability to harsh environments, with high concentrations of detergents, providing greater insights into pathogenicity and underlying mechanisms. This in turn opens the possibility to map and characterize potentially relevant strains even before they appear as pathogens in society.}, }
@article {pmid36553544, year = {2022}, author = {Jardillier, R and Koca, D and Chatelain, F and Guyon, L}, title = {Optimal microRNA Sequencing Depth to Predict Cancer Patient Survival with Random Forest and Cox Models.}, journal = {Genes}, volume = {13}, number = {12}, pages = {}, doi = {10.3390/genes13122275}, pmid = {36553544}, issn = {2073-4425}, abstract = {(1) Background: tumor profiling enables patient survival prediction. The two essential parameters to be calibrated when designing a study based on tumor profiles from a cohort are the sequencing depth of RNA-seq technology and the number of patients. This calibration is carried out under cost constraints, and a compromise has to be found. In the context of survival data, the goal of this work is to benchmark the impact of the number of patients and of the sequencing depth of miRNA-seq and mRNA-seq on the predictive capabilities for both the Cox model with elastic net penalty and random survival forest. (2) Results: we first show that the Cox model and random survival forest provide comparable prediction capabilities, with significant differences for some cancers. Second, we demonstrate that miRNA and/or mRNA data improve prediction over clinical data alone. mRNA-seq data leads to slightly better prediction than miRNA-seq, with the notable exception of lung adenocarcinoma for which the tumor miRNA profile shows higher predictive power. Third, we demonstrate that the sequencing depth of RNA-seq data can be reduced for most of the investigated cancers without degrading the prediction abilities, allowing the creation of independent validation sets at a lower cost. Finally, we show that the number of patients in the training dataset can be reduced for the Cox model and random survival forest, allowing the use of different models on different patient subgroups.}, }
@article {pmid36552139, year = {2022}, author = {Qiu, P and Dai, J and Wang, T and Li, H and Ma, C and Xi, X}, title = {Altered Functional Connectivity and Complexity in Major Depressive Disorder after Musical Stimulation.}, journal = {Brain sciences}, volume = {12}, number = {12}, pages = {}, doi = {10.3390/brainsci12121680}, pmid = {36552139}, issn = {2076-3425}, abstract = {Major depressive disorder (MDD) is a common mental illness. This study used electroencephalography (EEG) to explore the effects of music therapy on brain networks in MDD patients and to elucidate changes in functional brain connectivity in subjects before and after musical stimulation. EEG signals were collected from eight MDD patients and eight healthy controls. The phase locking value was adopted to calculate the EEG correlation of different channels in different frequency bands. Correlation matrices and network topologies were studied to analyze changes in functional connectivity between brain regions. The results of the experimental analysis found that the connectivity of the delta and beta bands decreased, while the connectivity of the alpha band increased. Regarding the characteristics of the EEG functional network, the average clustering coefficient, characteristic path length and degree of each node in the delta band decreased significantly after musical stimulation, while the characteristic path length in the beta band increased significantly. Characterized by the average clustering coefficient and characteristic path length, the classification of depression and healthy controls reached 93.75% using a support vector machine.}, }
@article {pmid36551135, year = {2022}, author = {Luo, J and Xue, N and Chen, J}, title = {A Review: Research Progress of Neural Probes for Brain Research and Brain-Computer Interface.}, journal = {Biosensors}, volume = {12}, number = {12}, pages = {}, doi = {10.3390/bios12121167}, pmid = {36551135}, issn = {2079-6374}, abstract = {Neural probes, as an invasive physiological tool at the mesoscopic scale, can decipher the code of brain connections and communications from the cellular or even molecular level, and realize information fusion between the human body and external machines. In addition to traditional electrodes, two new types of neural probes have been developed in recent years: optoprobes based on optogenetics and magnetrodes that record neural magnetic signals. In this review, we give a comprehensive overview of these three kinds of neural probes. We firstly discuss the development of microelectrodes and strategies for their flexibility, which is mainly represented by the selection of flexible substrates and new electrode materials. Subsequently, the concept of optogenetics is introduced, followed by the review of several novel structures of optoprobes, which are divided into multifunctional optoprobes integrated with microfluidic channels, artifact-free optoprobes, three-dimensional drivable optoprobes, and flexible optoprobes. At last, we introduce the fundamental perspectives of magnetoresistive (MR) sensors and then review the research progress of magnetrodes based on it.}, }
@article {pmid36551100, year = {2022}, author = {Said, RR and Heyat, MBB and Song, K and Tian, C and Wu, Z}, title = {A Systematic Review of Virtual Reality and Robot Therapy as Recent Rehabilitation Technologies Using EEG-Brain-Computer Interface Based on Movement-Related Cortical Potentials.}, journal = {Biosensors}, volume = {12}, number = {12}, pages = {}, doi = {10.3390/bios12121134}, pmid = {36551100}, issn = {2079-6374}, abstract = {To enhance the treatment of motor function impairment, patients' brain signals for self-control as an external tool may be an extraordinarily hopeful option. For the past 10 years, researchers and clinicians in the brain-computer interface (BCI) field have been using movement-related cortical potential (MRCP) as a control signal in neurorehabilitation applications to induce plasticity by monitoring the intention of action and feedback. Here, we reviewed the research on robot therapy (RT) and virtual reality (VR)-MRCP-based BCI rehabilitation technologies as recent advancements in human healthcare. A list of 18 full-text studies suitable for qualitative review out of 322 articles published between 2000 and 2022 was identified based on inclusion and exclusion criteria. We used PRISMA guidelines for the systematic review, while the PEDro scale was used for quality evaluation. Bibliometric analysis was conducted using the VOSviewer software to identify the relationship and trends of key items. In this review, 4 studies used VR-MRCP, while 14 used RT-MRCP-based BCI neurorehabilitation approaches. The total number of subjects in all identified studies was 107, whereby 4.375 ± 6.3627 were patient subjects and 6.5455 ± 3.0855 were healthy subjects. The type of electrodes, the epoch, classifiers, and the performance information that are being used in the RT- and VR-MRCP-based BCI rehabilitation application are provided in this review. Furthermore, this review also describes the challenges facing this field, solutions, and future directions of these smart human health rehabilitation technologies. By key items relationship and trends analysis, we found that motor control, rehabilitation, and upper limb are important key items in the MRCP-based BCI field. Despite the potential of these rehabilitation technologies, there is a great scarcity of literature related to RT and VR-MRCP-based BCI. However, the information on these rehabilitation methods can be beneficial in developing RT and VR-MRCP-based BCI rehabilitation devices to induce brain plasticity and restore motor impairment. Therefore, this review will provide the basis and references of the MRCP-based BCI used in rehabilitation applications for further clinical and research development.}, }
@article {pmid36550974, year = {2022}, author = {Orban, M and Elsamanty, M and Guo, K and Zhang, S and Yang, H}, title = {A Review of Brain Activity and EEG-Based Brain-Computer Interfaces for Rehabilitation Application.}, journal = {Bioengineering (Basel, Switzerland)}, volume = {9}, number = {12}, pages = {}, doi = {10.3390/bioengineering9120768}, pmid = {36550974}, issn = {2306-5354}, abstract = {Patients with severe CNS injuries struggle primarily with their sensorimotor function and communication with the outside world. There is an urgent need for advanced neural rehabilitation and intelligent interaction technology to provide help for patients with nerve injuries. Recent studies have established the brain-computer interface (BCI) in order to provide patients with appropriate interaction methods or more intelligent rehabilitation training. This paper reviews the most recent research on brain-computer-interface-based non-invasive rehabilitation systems. Various endogenous and exogenous methods, advantages, limitations, and challenges are discussed and proposed. In addition, the paper discusses the communication between the various brain-computer interface modes used between severely paralyzed and locked patients and the surrounding environment, particularly the brain-computer interaction system utilizing exogenous (induced) EEG signals (such as P300 and SSVEP). This discussion reveals with an examination of the interface for collecting EEG signals, EEG components, and signal postprocessing. Furthermore, the paper describes the development of natural interaction strategies, with a focus on signal acquisition, data processing, pattern recognition algorithms, and control techniques.}, }
@article {pmid36550932, year = {2022}, author = {Abdullah,  and Faye, I and Islam, MR}, title = {EEG Channel Selection Techniques in Motor Imagery Applications: A Review and New Perspectives.}, journal = {Bioengineering (Basel, Switzerland)}, volume = {9}, number = {12}, pages = {}, doi = {10.3390/bioengineering9120726}, pmid = {36550932}, issn = {2306-5354}, abstract = {Communication, neuro-prosthetics, and environmental control are just a few applications for disabled persons who use robots and manipulators that use brain-computer interface (BCI) systems. The brain's motor imagery (MI) signal is an essential input for a brain-related task in BCI applications. Due to their noninvasive, portability, and cost-effectiveness, electroencephalography (EEG) signals are the most widely used input in BCI systems. The EEG data are often collected from more than 100 different locations in the brain; channel selection techniques are critical for selecting the optimum channels for a given application. However, when analyzing EEG data, the principal purpose of channel selection is to reduce computational complexity, improve classification accuracy by avoiding overfitting, and reduce setup time. Several channel selection assessment algorithms, both with and without classification-based methods, extracted appropriate channel subsets using defined criteria. Therefore, based on the exhaustive analysis of the EEG channel selection, this manuscript analyses several existing studies to reduce the number of noisy channels and improve system performance. We review several existing works to find the most promising MI-based EEG channel selection algorithms and associated classification methodologies on various datasets. Moreover, we focus on channel selection methods that choose fewer channels with great precision. Finally, our main finding is that a smaller channel set, typically 10-30% of total channels, provided excellent performance compared to other existing studies.}, }
@article {pmid36550229, year = {2022}, author = {Latheef, S}, title = {Brain to Brain Interfaces (BBIs) in future military operations; blurring the boundaries of individual responsibility.}, journal = {Monash bioethics review}, volume = {}, number = {}, pages = {}, pmid = {36550229}, issn = {1836-6716}, abstract = {Developments in neurotechnology took a leap forward with the demonstration of the first Brain to Brain Interface (BBI). BBIs enable direct communication between two brains via a Brain Computer Interface (BCI) and bypasses the peripheral nervous system. This discovery promises new possibilities for future battlefield technology. As battlefield technology evolves, it is more likely to place greater demands on future soldiers. Future soldiers are more likely to process large amounts of data derived from an extensive networks of humans and machines. This raises several ethical and philosophical concerns. This paper will look at BBI technology in current stages of research, future BBI applications in the military and how the potential use of BBIs in military operations challenges the way we understand the concept of responsibility. In this paper, I propose that an individual connected to a BBI ought not to be held fully responsible for her actions. The justification for this proposition is based on three key points such as an individual connected to a BBI does not have the ability to act freely, has a diminished sense of self-agency and may not be able to demonstrate authenticity of the thoughts and memories generated when connected to the interface.}, }
@article {pmid36548997, year = {2022}, author = {Nagarajan, A and Robinson, N and Guan, C}, title = {Relevance based channel selection in motor imagery brain-computer interface.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acae07}, pmid = {36548997}, issn = {1741-2552}, abstract = {OBJECTIVE: Channel selection in electroencephalogram (EEG)-based brain-computer interface (BCI) has been extensively studied for over two decades, with the goal to select optimal subject-specific channels that can enhance the overall decoding efficacy of BCI. With the emergence of deep learning (DL) based BCI models, there arises a need for fresh perspectives and novel techniques to conduct channel selection. In this regard, subject-independent channel selection is relevant, since DL models trained using cross-subject data offer superior performance, and the impact of inherent inter-subject variability of EEG characteristics on subject-independent DL training is not yet fully understood.<br><br>APPROACH: Here, we propose a novel methodology for implementing subject-independent channel selection in DL based motor imagery (MI)-BCI, using layer-wise relevance propagation (LRP) and neural network pruning. Experiments were conducted using Deep ConvNet and 62-channel MI data from Korea University (KU) EEG dataset.<br><br>MAIN RESULTS: Using our proposed methodology, we achieved a 61% reduction in the number of channels without any significant drop (p=0.09) in subject-independent classification accuracy, due to the selection of highly relevant channels by LRP. LRP relevance based channel selections provide significantly better accuracies compared to conventional weight based selections while using less than 40% of the total number of channels, with differences in accuracies ranging from 5.96% to 1.72%. The performance of the adapted sparse-LRP model using only 16% of the total number of channels is similar to that of the adapted baseline model (p=0.13). Furthermore, the accuracy of the adapted sparse-LRP model using only 35% of the total number of channels exceeded that of the adapted baseline model by 0.53% (p=0.81). Analyses of channels chosen by LRP confirm the neurophysiological plausibility of selection, and emphasize the influence of motor, parietal, and occipital channels in MI-EEG classification.<br><br>SIGNIFICANCE: The proposed method addresses a traditional issue in EEG-BCI decoding, while being relevant and applicable to the latest developments in the field of BCI. We believe that our work brings forth an interesting and important application of model interpretability as a problem-solving technique.}, }
@article {pmid36548189, year = {2022}, author = {Tsiamalou, A and Dardiotis, E and Paterakis, K and Fotakopoulos, G and Liampas, I and Sgantzos, M and Siokas, V and Brotis, AG}, title = {EEG in Neurorehabilitation: A Bibliometric Analysis and Content Review.}, journal = {Neurology international}, volume = {14}, number = {4}, pages = {1046-1061}, doi = {10.3390/neurolint14040084}, pmid = {36548189}, issn = {2035-8385}, abstract = {BACKGROUND: There is increasing interest in the role of EEG in neurorehabilitation. We primarily aimed to identify the knowledge base through highly influential studies. Our secondary aims were to imprint the relevant thematic hotspots, research trends, and social networks within the scientific community.<br><br>METHODS: We performed an electronic search in Scopus, looking for studies reporting on rehabilitation in patients with neurological disabilities. We used the most influential papers to outline the knowledge base and carried out a word co-occurrence analysis to identify the research hotspots. We also used depicted collaboration networks between universities, authors, and countries after analyzing the cocitations. The results were presented in summary tables, plots, and maps. Finally, a content review based on the top-20 most cited articles completed our study.<br><br>RESULTS: Our current bibliometric study was based on 874 records from 420 sources. There was vivid research interest in EEG use for neurorehabilitation, with an annual growth rate as high as 14.3%. The most influential paper was the study titled "Brain-computer interfaces, a review" by L.F. Nicolas-Alfonso and J. Gomez-Gill, with 997 citations, followed by "Brain-computer interfaces in neurological rehabilitation" by J. Daly and J.R. Wolpaw (708 citations). The US, Italy, and Germany were among the most productive countries. The research hotspots shifted with time from the use of functional magnetic imaging to EEG-based brain-machine interface, motor imagery, and deep learning.<br><br>CONCLUSIONS: EEG constitutes the most significant input in brain-computer interfaces (BCIs) and can be successfully used in the neurorehabilitation of patients with stroke symptoms, amyotrophic lateral sclerosis, and traumatic brain and spinal injuries. EEG-based BCI facilitates the training, communication, and control of wheelchair and exoskeletons. However, research is limited to specific scientific groups from developed countries. Evidence is expected to change with the broader availability of BCI and improvement in EEG-filtering algorithms.}, }
@article {pmid36547803, year = {2022}, author = {Zhou, F and Zheng, J and Xu, H}, title = {Lighting up Oxytocin Neurons to Nurture the Brain.}, journal = {Neuroscience bulletin}, volume = {}, number = {}, pages = {}, pmid = {36547803}, issn = {1995-8218}, }
@article {pmid36545350, year = {2022}, author = {Galiotta, V and Quattrociocchi, I and D'Ippolito, M and Schettini, F and Aricò, P and Sdoia, S and Formisano, R and Cincotti, F and Mattia, D and Riccio, A}, title = {EEG-based Brain-Computer Interfaces for people with Disorders of Consciousness: Features and applications. A systematic review.}, journal = {Frontiers in human neuroscience}, volume = {16}, number = {}, pages = {1040816}, pmid = {36545350}, issn = {1662-5161}, abstract = {BACKGROUND: Disorders of Consciousness (DoC) are clinical conditions following a severe acquired brain injury (ABI) characterized by absent or reduced awareness, known as coma, Vegetative State (VS)/Unresponsive Wakefulness Syndrome (VS/UWS), and Minimally Conscious State (MCS). Misdiagnosis rate between VS/UWS and MCS is attested around 40% due to the clinical and behavioral fluctuations of the patients during bedside consciousness assessments. Given the large body of evidence that some patients with DoC possess "covert" awareness, revealed by neuroimaging and neurophysiological techniques, they are candidates for intervention with brain-computer interfaces (BCIs).<br><br>OBJECTIVES: The aims of the present work are (i) to describe the characteristics of BCI systems based on electroencephalography (EEG) performed on DoC patients, in terms of control signals adopted to control the system, characteristics of the paradigm implemented, classification algorithms and applications (ii) to evaluate the performance of DoC patients with BCI.<br><br>METHODS: The search was conducted on Pubmed, Web of Science, Scopus and Google Scholar. The PRISMA guidelines were followed in order to collect papers published in english, testing a BCI and including at least one DoC patient.<br><br>RESULTS: Among the 527 papers identified with the first run of the search, 27 papers were included in the systematic review. Characteristics of the sample of participants, behavioral assessment, control signals employed to control the BCI, the classification algorithms, the characteristics of the paradigm, the applications and performance of BCI were the data extracted from the study. Control signals employed to operate the BCI were: P300 (N = 19), P300 and Steady-State Visual Evoked Potentials (SSVEP; hybrid system, N = 4), sensorimotor rhythms (SMRs; N = 5) and brain rhythms elicited by an emotional task (N = 1), while assessment, communication, prognosis, and rehabilitation were the possible applications of BCI in DoC patients.<br><br>CONCLUSION: Despite the BCI is a promising tool in the management of DoC patients, supporting diagnosis and prognosis evaluation, results are still preliminary, and no definitive conclusions may be drawn; even though neurophysiological methods, such as BCI, are more sensitive to covert cognition, it is suggested to adopt a multimodal approach and a repeated assessment strategy.}, }
@article {pmid36543809, year = {2022}, author = {Shen, T and Yue, Y and Ba, F and He, T and Tang, X and Hu, X and Pu, J and Huang, C and Lv, W and Zhang, B and Lai, HY}, title = {Diffusion along perivascular spaces as marker for impairment of glymphatic system in Parkinson's disease.}, journal = {NPJ Parkinson's disease}, volume = {8}, number = {1}, pages = {174}, pmid = {36543809}, issn = {2373-8057}, abstract = {The brain glymphatic system is involved in the clearance of misfolding α-synuclein, the impaired glymphatic system may contribute to the progression of Parkinson's disease (PD). We aimed to analyze the diffusion tensor image along the perivascular space (DTI-ALPS) and perivascular space (PVS) burden to reveal the relationship between the glymphatic system and PD. A cross-sectional study using a 7 T MRI of 76 PD patients and 48 controls was performed to evaluate the brain's glymphatic system. The DTI-ALPS and PVS burden in basal ganglia were calculated. Correlation analyses were conducted between DTI-ALPS, PVS burden and clinical features. We detected lower DTI-ALPS in the PD subgroup relative to controls, and the differences were more pronounced in patients with Hoehn &amp; Yahr stage greater than two. The decreased DTI-ALPS was only evident in the left hemisphere in patients in the early stage but involved both hemispheres in more advanced PD patients. Decreased DTI-ALPS were also correlated with longer disease duration, higher Unified Parkinson's Disease Rating Scale motor score (UPDRS III) and UPDRS total scores, as well as higher levodopa equivalent daily dose. Moreover, the decreased DTI-ALPS correlated with increased PVS burden, and both indexes correlated with PD disease severity. This study demonstrated decreased DTI-ALPS in PD, which might initiate from the left hemisphere and progressively involve right hemisphere with the disease progression. Decreased DTI-ALPS index correlated with increased PVS burden, indicating that both metrics could provide supporting evidence of an impaired glymphatic system. MRI evaluation of the PVS burden and diffusion along PVS are potential imaging biomarkers for PD for disease progression.}, }
@article {pmid36542992, year = {2022}, author = {Wang, X and Sun, X and Ma, C and Zhang, Y and Kong, L and Huang, Z and Hu, Y and Wan, H and Wang, P}, title = {Multifunctional AuNPs@HRP@FeMOF immune scaffold with a fully automated saliva analyzer for oral cancer screening.}, journal = {Biosensors &amp; bioelectronics}, volume = {222}, number = {}, pages = {114910}, doi = {10.1016/j.bios.2022.114910}, pmid = {36542992}, issn = {1873-4235}, abstract = {Delayed diagnosis of cancer-causing death is a worldwide concern. General diagnosis methods are invasive, time-consuming, and operation complicated, which are not suitable for preliminary screening. To address these challenges, the sensing platform based on immune scaffold and fully automated saliva analyzer (FASA) was proposed for oral cancer screening for the first time by non-invasive detection of Cyfra21-1 in saliva. Through one-step synthesis method with unique covalent and electrostatic adsorption strategy, AuNPs@HRP@FeMOF immune scaffold features multiple functions including antibody carrier, catalytic activity, and signal amplification. Highly integrated FASA with the immune scaffold provides automatic testing to avoid false-positive results and reduce pretreatment time without any user intervention. Compared with the commercial analyzer, FASA has comparable performance for Cyfra21-1 detection with a detection range of 3.1-50.0 ng/mL and R[2] of 0.971, and superior features in full automation, high integration, time saving and low cost. Oral cancer patients could be distinguished accurately by the platform with an excellent correlation (R[2] of 0.904) and average RSD (5.578%) without sample dilution. The proposed platform provides an effective and promising tool for cancer screening in point-of-care applications, which can be further extended for biomarker detection in universal body fluids, disease screening, prognosis review and homecare monitoring.}, }
@article {pmid36541542, year = {2022}, author = {Fang, T and Wang, J and Mu, W and Song, Z and Zhang, X and Zhan, G and Wang, P and Bin, J and Niu, L and Zhang, L and Kang, X}, title = {Noninvasive neuroimaging and spatial filter transform enable ultra low delay motor imagery EEG decoding.}, journal = {Journal of neural engineering}, volume = {19}, number = {6}, pages = {}, doi = {10.1088/1741-2552/aca82d}, pmid = {36541542}, issn = {1741-2552}, abstract = {Objective.The brain-computer interface (BCI) system based on sensorimotor rhythm can convert the human spirit into instructions for machine control, and it is a new human-computer interaction system with broad applications. However, the spatial resolution of scalp electroencephalogram (EEG) is limited due to the presence of volume conduction effects. Therefore, it is very meaningful to explore intracranial activities in a noninvasive way and improve the spatial resolution of EEG. Meanwhile, low-delay decoding is an essential factor for the development of a real-time BCI system.Approach.In this paper, EEG conduction is modeled by using public head anatomical templates, and cortical EEG is obtained using dynamic parameter statistical mapping. To solve the problem of a large amount of computation caused by the increase in the number of channels, the filter bank common spatial pattern method is used to obtain a spatial filter kernel, which reduces the computational cost of feature extraction to a linear level. And the feature classification and selection of important features are completed using a neural network containing band-spatial-time domain self-attention mechanisms.Main results.The results show that the method proposed in this paper achieves high accuracy for the four types of motor imagery EEG classification tasks, with fairly low latency and high physiological interpretability.Significance.The proposed decoding framework facilitates the realization of low-latency human-computer interaction systems.}, }
@article {pmid36541535, year = {2022}, author = {Pires, G and Cruz, A and Jesus, D and Yasemin, M and Nunes, UJ and Sousa, T and Castelo-Branco, M}, title = {A new error-monitoring brain-computer interface based on reinforcement learning for people with autism spectrum disorders.}, journal = {Journal of neural engineering}, volume = {19}, number = {6}, pages = {}, doi = {10.1088/1741-2552/aca798}, pmid = {36541535}, issn = {1741-2552}, abstract = {Objective.Brain-computer interfaces (BCIs) are emerging as promising cognitive training tools in neurodevelopmental disorders, as they combine the advantages of traditional computerized interventions with real-time tailored feedback. We propose a gamified BCI based on non-volitional neurofeedback for cognitive training, aiming at reaching a neurorehabilitation tool for application in autism spectrum disorders (ASDs).Approach.The BCI consists of an emotional facial expression paradigm controlled by an intelligent agent that makes correct and wrong actions, while the user observes and judges the agent's actions. The agent learns through reinforcement learning (RL) an optimal strategy if the participant generates error-related potentials (ErrPs) upon incorrect agent actions. We hypothesize that this training approach will allow not only the agent to learn but also the BCI user, by participating through implicit error scrutiny in the process of learning through operant conditioning, making it of particular interest for disorders where error monitoring processes are altered/compromised such as in ASD. In this paper, the main goal is to validate the whole methodological BCI approach and assess whether it is feasible enough to move on to clinical experiments. A control group of ten neurotypical participants and one participant with ASD tested the proposed BCI approach.Main results.We achieved an online balanced-accuracy in ErrPs detection of 81.6% and 77.1%, respectively for two different game modes. Additionally, all participants achieved an optimal RL strategy for the agent at least in one of the test sessions.Significance.The ErrP classification results and the possibility of successfully achieving an optimal learning strategy, show the feasibility of the proposed methodology, which allows to move towards clinical experimentation with ASD participants to assess the effectiveness of the approach as hypothesized.}, }
@article {pmid36541532, year = {2022}, author = {Wang, X and Chen, HT and Lin, CT}, title = {Error-related potential-based shared autonomy via deep recurrent reinforcement learning.}, journal = {Journal of neural engineering}, volume = {19}, number = {6}, pages = {}, doi = {10.1088/1741-2552/aca4fb}, pmid = {36541532}, issn = {1741-2552}, abstract = {Objective.Error-related potential (ErrP)-based brain-computer interfaces (BCIs) have received a considerable amount of attention in the human-robot interaction community. In contrast to traditional BCI, which requires continuous and explicit commands from an operator, ErrP-based BCI leverages the ErrP, which is evoked when an operator observes unexpected behaviours from the robot counterpart. This paper proposes a novel shared autonomy model for ErrP-based human-robot interaction.Approach.We incorporate ErrP information provided by a BCI as useful observations for an agent and formulate the shared autonomy problem as a partially observable Markov decision process. A recurrent neural network-based actor-critic model is used to address the uncertainty in the ErrP signal. We evaluate the proposed framework in a simulated human-in-the-loop robot navigation task with both simulated users and real users.Main results.The results show that the proposed ErrP-based shared autonomy model enables an autonomous robot to complete navigation tasks more efficiently. In a simulation with 70% ErrP accuracy, agents completed the task 14.1% faster than in the no ErrP condition, while with real users, agents completed the navigation task 14.9% faster.Significance.The evaluation results confirmed that the shared autonomy via deep recurrent reinforcement learning is an effective way to deal with uncertain human feedback in a complex human-robot interaction task.}, }
@article {pmid36538406, year = {2022}, author = {Kostenko, EV and Petrova, LV and Pogonchenkova, IV and Neprintseva, NV and Shurupova, ST and Kopasheva, VD and Rylsky, AV}, title = {[Innovative technologies and multimodal correction in medical rehabilitation of motor and neuropsychological disturbances due to stroke].}, journal = {Voprosy kurortologii, fizioterapii, i lechebnoi fizicheskoi kultury}, volume = {99}, number = {6}, pages = {67-78}, doi = {10.17116/kurort20229906167}, pmid = {36538406}, issn = {0042-8787}, abstract = {The article presents an overview of innovative technologies based on the methods of sensorimotor retraining of the patient using various types of biofeedback (BFB) as the most promising in the medical rehabilitation (MR) of patients with cerebral stroke (CS). The works of a high level of evidence (RCTs, national and international clinical guidelines, meta-analyses, systematic reviews) of the Medline, Pubmed, PubMed Cochrane Library databases are analyzed, ClinicalTrials.gov. It is emphasized that training with multisensory effects on visual, auditory, vestibular and kinesthetic analyzers have a beneficial effect on cognitive-motor training and retraining, neuropsychological status of the patient and increase the level of motivation to achieve success in the rehabilitation process. The synergy of multimodal effects of digital technologies, BFB, virtual reality, and the brain-computer interface will expand the capabilities and improve the efficiency of MR of after stroke-patients.}, }
@article {pmid36317171, year = {2022}, author = {Imambocus, BN and Formozov, A and Zhou, F and Soba, P}, title = {A two-choice assay for noxious light avoidance with temporal distribution analysis in Drosophila melanogaster larvae.}, journal = {STAR protocols}, volume = {3}, number = {4}, pages = {101787}, pmid = {36317171}, issn = {2666-1667}, support = {P40 OD018537/OD/NIH HHS/United States ; }, mesh = {Animals ; *Drosophila melanogaster ; Larva ; *Drosophila ; Biological Assay ; }, abstract = {Two-choice assays allow assessing of different behaviors including light avoidance in Drosophila larvae. Typically, the readout is limited to a preference index at a specific end point. We provide a detailed protocol to set up light avoidance assays and map the temporal distribution of larvae based on analysis of larval intensities. We describe the assay setup and implementation of scripts for analysis, which can be easily adapted to other two-choice assays and different model organisms. For complete details on the use and execution of this protocol, please refer to Imambocus et al. (2022).}, }
@article {pmid36536134, year = {2022}, author = {Hu, Y and Cao, K and Wang, F and Wu, W and Mai, W and Qiu, L and Luo, Y and Ge, WP and Sun, B and Shi, L and Zhu, J and Zhang, J and Wu, Z and Xie, Y and Duan, S and Gao, Z}, title = {Dual roles of hexokinase 2 in shaping microglial function by gating glycolytic flux and mitochondrial activity.}, journal = {Nature metabolism}, volume = {}, number = {}, pages = {}, pmid = {36536134}, issn = {2522-5812}, abstract = {Microglia continuously survey the brain parenchyma and actively shift status following stimulation. These processes demand a unique bioenergetic programme; however, little is known about the metabolic determinants in microglia. By mining large datasets and generating transgenic tools, here we show that hexokinase 2 (HK2), the most active isozyme associated with mitochondrial membrane, is selectively expressed in microglia in the brain. Genetic ablation of HK2 reduced microglial glycolytic flux and energy production, suppressed microglial repopulation, and attenuated microglial surveillance and damage-triggered migration in male mice. HK2 elevation is prominent in immune-challenged or disease-associated microglia. In ischaemic stroke models, however, HK2 deletion promoted neuroinflammation and potentiated cerebral damages. The enhanced inflammatory responses after HK2 ablation in microglia are associated with aberrant mitochondrial function and reactive oxygen species accumulation. Our study demonstrates that HK2 gates both glycolytic flux and mitochondrial activity to shape microglial functions, changes of which contribute to metabolic abnormalities and maladaptive inflammation in brain diseases.}, }
@article {pmid36535036, year = {2022}, author = {Guney, OB and Ozkan, H}, title = {Transfer learning of an ensemble of DNNs for SSVEP BCI spellers without user-specific training.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acacca}, pmid = {36535036}, issn = {1741-2552}, abstract = {OBJECTIVE: Steady-state visually evoked potentials (SSVEPs), measured with EEG (electroencephalogram), yield decent information transfer rates (ITR) in brain-computer interface (BCI) spellers. However, the current high performing SSVEP BCI spellers in the literature require an initial lengthy and tiring user-specific training for each new user for system adaptation, including data collection with EEG experiments, algorithm training and calibration (all are before the actual use of the system). This impedes the widespread use of BCIs. To ensure practicality, we propose a highly novel target identification method based on an ensemble of deep neural networks (DNNs), which does not require any sort of user-specific training.<br><br>APPROACH: We exploit already-existing literature datasets from participants of previously conducted EEG experiments to train a global target identi&#xfb01;er DNN &#xfb01;rst, which is then &#xfb01;ne-tuned to each participant. We transfer this ensemble of &#xfb01;ne-tuned DNNs to the new user instance, determine the k most representative DNNs according to the participants' statistical similarities to the new user, and predict the target character through a weighted combination of the ensemble predictions.<br><br>MAIN RESULTS: The proposed method signi&#xfb01;cantly outperforms all the state-of-the-art alternatives for all stimulation durations in [0.2 - 1.0] seconds on two large-scale benchmark and BETA datasets, and achieves impressive 155.51 bits/min and 114.64 bits/min ITRs. Code is available for reproducibility: https://github.com/osmanberke/Ensemble-of-DNNs Signi&#xfb01;cance: Our Ensemble-DNN method has the potential to promote the practical widespread deployment of BCI spellers in daily lives as we provide the highest performance while enabling the immediate system use without any user-speci&#xfb01;c training.}, }
@article {pmid36535004, year = {2022}, author = {Fodil, Y and Haddab, S and Kachenoura, A and Karfoul, A}, title = {A novel ANN adaptive Riemannian-based kernel classification for Motor Imagery.}, journal = {Biomedical physics &amp; engineering express}, volume = {}, number = {}, pages = {}, doi = {10.1088/2057-1976/acaca2}, pmid = {36535004}, issn = {2057-1976}, abstract = {More recently, a number of studies show the interest of the use of the Riemannian geometry in EEG classification. The idea is to exploit the EEG covariance matrices, instead of the raw EEG data, and use the Riemannian geometry to directly classify these matrices. This paper presents a novel Artificial Neural Network approach based on an Adaptive Riemannian Kernel, named ARK-ANN, to classify Electroencephalographic (EEG) motor imaging signals in the context of Brain Computer Interface (BCI). A multilayer perceptron is used to classify the covariance matrices of Motor Imagery (MI) signals employing an adaptive optimization of the testing set. The contribution of a geodesic filter is also assessed for the ANN and the original method which use an SVM classifier. The results demonstrate that the ARK-ANN performs better than the other methods and the geodesic filter gives slightly better results in the ARK-SVM, considered here as the reference method, in the case of inter-subject classification (accuracy of 87.4% and 86 % for ARK-ANN and ARK-SVM, respectively). Regarding the cross-subject classification, the proposed method gives an accuracy of 77.3% and increases the precision by 8.2% in comparison to the SVM based method.}, }
@article {pmid36532389, year = {2022}, author = {Andrews, A}, title = {Mind Power: Thought-controlled Augmented Reality for Basic Science Education.}, journal = {Medical science educator}, volume = {32}, number = {6}, pages = {1571-1573}, pmid = {36532389}, issn = {2156-8650}, abstract = {The integration of augmented reality (AR) and brain-computer interface (BCI) technologies holds a tremendous potential to improve learning, communication, and teamwork in basic science education. The current study presents a novel interface technology solution to enable AR-BCI interoperability and allow learners to control digital objects in AR using neural commands.}, }
@article {pmid36531919, year = {2022}, author = {Lyu, J and Maýe, A and Görner, M and Ruppel, P and Engel, AK and Zhang, J}, title = {Coordinating human-robot collaboration by EEG-based human intention prediction and vigilance control.}, journal = {Frontiers in neurorobotics}, volume = {16}, number = {}, pages = {1068274}, pmid = {36531919}, issn = {1662-5218}, abstract = {In human-robot collaboration scenarios with shared workspaces, a highly desired performance boost is offset by high requirements for human safety, limiting speed and torque of the robot drives to levels which cannot harm the human body. Especially for complex tasks with flexible human behavior, it becomes vital to maintain safe working distances and coordinate tasks efficiently. An established approach in this regard is reactive servo in response to the current human pose. However, such an approach does not exploit expectations of the human's behavior and can therefore fail to react to fast human motions in time. To adapt the robot's behavior as soon as possible, predicting human intention early becomes a factor which is vital but hard to achieve. Here, we employ a recently developed type of brain-computer interface (BCI) which can detect the focus of the human's overt attention as a predictor for impending action. In contrast to other types of BCI, direct projection of stimuli onto the workspace facilitates a seamless integration in workflows. Moreover, we demonstrate how the signal-to-noise ratio of the brain response can be used to adjust the velocity of the robot movements to the vigilance or alertness level of the human. Analyzing this adaptive system with respect to performance and safety margins in a physical robot experiment, we found the proposed method could improve both collaboration efficiency and safety distance.}, }
@article {pmid36530202, year = {2022}, author = {Bleuzé, A and Mattout, J and Congedo, M}, title = {Tangent space alignment: Transfer learning for Brain-Computer Interface.}, journal = {Frontiers in human neuroscience}, volume = {16}, number = {}, pages = {1049985}, pmid = {36530202}, issn = {1662-5161}, abstract = {Statistical variability of electroencephalography (EEG) between subjects and between sessions is a common problem faced in the field of Brain-Computer Interface (BCI). Such variability prevents the usage of pre-trained machine learning models and requires the use of a calibration for every new session. This paper presents a new transfer learning (TL) method that deals with this variability. This method aims to reduce calibration time and even improve accuracy of BCI systems by aligning EEG data from one subject to the other in the tangent space of the positive definite matrices Riemannian manifold. We tested the method on 18 BCI databases comprising a total of 349 subjects pertaining to three BCI paradigms, namely, event related potentials (ERP), motor imagery (MI), and steady state visually evoked potentials (SSVEP). We employ a support vector classifier for feature classification. The results demonstrate a significant improvement of classification accuracy, as compared to a classical training-test pipeline, in the case of the ERP paradigm, whereas for both the MI and SSVEP paradigm no deterioration of performance is observed. A global 2.7% accuracy improvement is obtained compared to a previously published Riemannian method, Riemannian Procrustes Analysis (RPA). Interestingly, tangent space alignment has an intrinsic ability to deal with transfer learning for sets of data that have different number of channels, naturally applying to inter-dataset transfer learning.}, }
@article {pmid36529022, year = {2022}, author = {Li, H and Zhang, D and Xie, J}, title = {MI-DABAN: A dual-attention-based adversarial network for motor imagery classification.}, journal = {Computers in biology and medicine}, volume = {152}, number = {}, pages = {106420}, doi = {10.1016/j.compbiomed.2022.106420}, pmid = {36529022}, issn = {1879-0534}, abstract = {The brain-computer interface (BCI) based on motor imagery electroencephalography (EEG) is widely used because of its convenience and safety. However, due to the distributional disparity between EEG signals, data from other subjects cannot be used directly to train a subject-specific classifier. For efficient use of the labeled data, domain transfer learning and adversarial learning are gradually applied to BCI classification tasks. While these methods improve classification performance, they only align globally and ignore task-specific class boundaries, which may lead to the blurring of features near the classification boundaries. Simultaneously, they employ fully shared generators to extract features, resulting in the loss of domain-specific information and the destruction of performance. To address these issues, we propose a novel dual-attention-based adversarial network for motor imagery classification (MI-DABAN). Our framework leverages multiple subjects' knowledge to improve a single subject's motor imagery classification performance by cleverly using a novel adversarial learning method and two unshared attention blocks. Specifically, without introducing additional domain discriminators, we iteratively maximize and minimize the output difference between the two classifiers to implement adversarial learning to ensure accurate domain alignment. Among them, maximization is used to identify easily confused samples near the decision boundary, and minimization is used to align the source and target domain distributions. Moreover, for the shallow features from source and target domains, we use two non-shared attention blocks to preserve domain-specific information, which can prevent the negative transfer of domain information and further improve the classification performance on test data. We conduct extensive experiments on two publicly available EEG datasets, namely BCI Competition IV Datasets 2a and 2b. The experiment results demonstrate our method's effectiveness and superiority.}, }
@article {pmid36528312, year = {2022}, author = {Kim, H and Kim, JS and Chung, CK}, title = {Identification of cerebral cortices processing acceleration, velocity, and position during directional reaching movement with deep neural network and explainable AI.}, journal = {NeuroImage}, volume = {}, number = {}, pages = {119783}, doi = {10.1016/j.neuroimage.2022.119783}, pmid = {36528312}, issn = {1095-9572}, abstract = {Cerebral cortical representation of motor kinematics is crucial for understanding human motor behavior, potentially extending to efficient control of the brain-computer interface. Numerous single-neuron studies have found the existence of a relationship between neuronal activity and motor kinematics such as acceleration, velocity, and position. Despite differences between kinematic characteristics, it is hard to distinguish neural representations of these kinematic characteristics with macroscopic functional images such as electroencephalography (EEG) and magnetoencephalography (MEG). The reason might be because cortical signals are not sensitive enough to segregate kinematic characteristics due to their limited spatial and temporal resolution. Considering different roles of each cortical area in producing movement, there might be a specific cortical representation depending on characteristics of acceleration, velocity, and position. Recently, neural network modeling has been actively pursued in the field of decoding. We hypothesized that neural features of each kinematic parameter could be identified with a high-performing model for decoding with an explainable AI method. Time-series deep neural network (DNN) models were used to measure the relationship between cortical activity and motor kinematics during reaching movement. With DNN models, kinematic parameters of reaching movement in a 3D space were decoded based on cortical source activity obtained from MEG data. An explainable artificial intelligence (AI) method was then adopted to extract the map of cortical areas, which strongly contributed to decoding each kinematics from DNN models. We found that there existed differed as well as shared cortical areas for decoding each kinematic attribute. Shared areas included bilateral supramarginal gyri and superior parietal lobules known to be related to the goal of movement and sensory integration. On the other hand, dominant areas for each kinematic parameter (the contralateral motor cortex for acceleration, the contralateral parieto-frontal network for velocity, and bilateral visuomotor areas for position) were mutually exclusive. Regarding the visuomotor reaching movement, the motor cortex was found to control the muscle force, the parieto-frontal network encoded reaching movement from sensory information, and visuomotor areas computed limb and gaze coordination in the action space. To the best of our knowledge, this is the first study to discriminate kinematic cortical areas using DNN models and explainable AI.}, }
@article {pmid36527133, year = {2022}, author = {Sgroi, DC and Treuner, K and Zhang, Y and Piper, T and Salunga, R and Ahmed, I and Doos, L and Thornber, S and Taylor, KJ and Brachtel, E and Pirrie, S and Schnabel, CA and Rea, D and Bartlett, JMS}, title = {Correlative studies of the Breast Cancer Index (HOXB13/IL17BR) and ER, PR, AR, AR/ER ratio and Ki67 for prediction of extended endocrine therapy benefit: a Trans-aTTom study.}, journal = {Breast cancer research : BCR}, volume = {24}, number = {1}, pages = {90}, pmid = {36527133}, issn = {1465-542X}, abstract = {BACKGROUND: Multiple clinical trials demonstrate consistent but modest benefit of adjuvant extended endocrine therapy (EET) in HR + breast cancer patients. Predictive biomarkers to identify patients that benefit from EET are critical to balance modest reductions in risk against potential side effects of EET. This study compares the performance of the Breast Cancer Index, BCI (HOXB13/IL17BR, H/I), with expression of estrogen (ER), progesterone (PR), and androgen receptors (AR), and Ki67, for prediction of EET benefit.<br><br>METHODS: Node-positive (N+) patients from the Trans-aTTom study with available tissue specimen and BCI results (N&#x2009;=&#x2009;789) were included. Expression of ER, PR, AR, and Ki67 was assessed by quantitative immunohistochemistry. BCI&#xa0;(H/I) gene expression analysis was conducted by quantitative RT-PCR. Statistical significance of the treatment by biomarker interaction was evaluated by likelihood ratio tests based on multivariate Cox proportional models, adjusting for age, tumor size, grade, and HER2 status. Pearson's correlation coefficients were calculated to evaluate correlations between BCI (H/I) versus ER, PR, AR, Ki67 and AR/ER ratio.<br><br>RESULTS: EET benefit, measured by the difference in risk of recurrence between patients treated with tamoxifen for 10 versus 5&#xa0;years, is significantly associated with increasing values of BCI&#xa0;(H/I) (interaction P&#x2009;=&#x2009;0.01). In contrast, expression of ER (P&#x2009;=&#x2009;0.83), PR (P&#x2009;=&#x2009;0.66), AR (P&#x2009;=&#x2009;0.78), Ki67 (P&#x2009;=&#x2009;0.87) and AR/ER ratio (P&#x2009;=&#x2009;0.84) exhibited no significant relationship with EET benefit. BCI&#xa0;(H/I) showed a very weak negative correlation with ER (r&#x2009;=&#x2009;-&#x2009;0.18), PR (r&#x2009;=&#x2009;-&#x2009;0.25), and AR (r&#x2009;=&#x2009;-&#x2009;0.14) expression, but no correlation with either Ki67 (r&#x2009;=&#x2009;0.04) or AR/ER ratio (r&#x2009;=&#x2009;0.02).<br><br>CONCLUSION: These findings are consistent with the growing body of evidence that BCI&#xa0;(H/I) is significantly predictive of response to EET and outcome. Results from this direct comparison demonstrate that expression of ER, PR, AR, Ki67 or AR/ER ratio are not predictive of benefit from EET. BCI&#xa0;(H/I) is the only clinically validated biomarker that predicts EET benefit.}, }
@article {pmid36525745, year = {2022}, author = {Kern, K and Vukelić, M and Guggenberger, R and Gharabaghi, A}, title = {Oscillatory neurofeedback networks and poststroke rehabilitative potential in severely impaired stroke patients.}, journal = {NeuroImage. Clinical}, volume = {37}, number = {}, pages = {103289}, doi = {10.1016/j.nicl.2022.103289}, pmid = {36525745}, issn = {2213-1582}, abstract = {Motor restoration after severe stroke is often limited. However, some of the severely impaired stroke patients may still have a rehabilitative potential. Biomarkers that identify these patients are sparse. Eighteen severely impaired chronic stroke patients with a lack of volitional finger extension participated in an EEG study. During sixty-six trials of kinesthetic motor imagery, a brain-machine interface turned event-related beta-band desynchronization of the ipsilesional sensorimotor cortex into opening of the paralyzed hand by a robotic orthosis. A subgroup of eight patients participated in a subsequent four-week rehabilitation training. Changes of the movement extent were captured with sensors which objectively quantified even discrete improvements of wrist movement. Albeit with the same motor impairment level, patients could be differentiated into two groups, i.e., with and without task-related increase of bilateral cortico-cortical phase synchronization between frontal/premotor and parietal areas. This fronto-parietal integration (FPI) was associated with a significantly higher volitional beta modulation range in the ipsilesional sensorimotor cortex. Following the four-week training, patients with FPI showed significantly higher improvement in wrist movement than those without FPI. Moreover, only the former group improved significantly in the upper extremity Fugl-Meyer-Assessment score. Neurofeedback-related long-range oscillatory coherence may differentiate severely impaired stroke patients with regard to their rehabilitative potential, a finding that needs to be confirmed in larger patient cohorts.}, }
@article {pmid36524791, year = {2022}, author = {Sinha, S and Dmochowski, RR and Hashim, H and Finazzi-Agrò, E and Iacovelli, V}, title = {The bladder contractility and bladder outlet obstruction indices in adult women. Results of a global Delphi consensus study.}, journal = {Neurourology and urodynamics}, volume = {}, number = {}, pages = {}, doi = {10.1002/nau.25114}, pmid = {36524791}, issn = {1520-6777}, abstract = {AIMS: This Delphi study was planned to examine global expert consensus with regard to utility, accuracy, and categorization of the bladder contractility index (BCI), bladder outlet obstruction index (BOOI), and the related evidence. This manuscript deals with adult women and follows a previous manuscript reporting on adult men.<br><br>METHODS: Twenty-nine experts were invited to answer the two-round survey including three foundation questions and 12 survey questions. Consensus was defined as &#x2265;75% agreement. The ordinal scale (0-10) in round 1 was classified into "strongly agree," "agree," "neutral," "disagree," and "strongly disagree" for the final round. A systematic search for evidence was conducted for therapeutic studies that have examined outcome stratified by the indices in women.<br><br>RESULTS: Eighteen experts participated in the survey with 100% completion. Consensus was noted with regard to 2 of 12 questions, both in the negative. The experts had a consensus that BOOI was neither accurate nor useful and a similar negative trend was noted with regard to BCI. However, there was support, short of consensus, for the utility on an index of bladder contractility and bladder outflow obstruction. Systematic search yielded eight publications pertaining to stress urinary incontinence (n&#x2009;=&#x2009;6), pelvic organ prolapse (n&#x2009;=&#x2009;1), and intra-sphincteric botulinum toxin (n&#x2009;=&#x2009;1).<br><br>CONCLUSIONS: Experts had significant concerns with regard to the use of the male BCI and BOOI in adult women despite a general recognition of the need for numerical indices of contractility and obstruction. Systematic search showed a striking lack of evidence in this regard.}, }
@article {pmid36523756, year = {2022}, author = {Proverbio, AM and Tacchini, M and Jiang, K}, title = {Event-related brain potential markers of visual and auditory perception: A useful tool for brain computer interface systems.}, journal = {Frontiers in behavioral neuroscience}, volume = {16}, number = {}, pages = {1025870}, pmid = {36523756}, issn = {1662-5153}, abstract = {OBJECTIVE: A majority of BCI systems, enabling communication with patients with locked-in syndrome, are based on electroencephalogram (EEG) frequency analysis (e.g., linked to motor imagery) or P300 detection. Only recently, the use of event-related brain potentials (ERPs) has received much attention, especially for face or music recognition, but neuro-engineering research into this new approach has not been carried out yet. The aim of this study was to provide a variety of reliable ERP markers of visual and auditory perception for the development of new and more complex mind-reading systems for reconstructing the mental content from brain activity.<br><br>METHODS: A total of 30 participants were shown 280 color pictures (adult, infant, and animal faces; human bodies; written words; checkerboards; and objects) and 120 auditory files (speech, music, and affective vocalizations). This paradigm did not involve target selection to avoid artifactual waves linked to decision-making and response preparation (e.g., P300 and motor potentials), masking the neural signature of semantic representation. Overall, 12,000 ERP waveforms &#xd7; 126 electrode channels (1 million 512,000 ERP waveforms) were processed and artifact-rejected.<br><br>RESULTS: Clear and distinct category-dependent markers of perceptual and cognitive processing were identified through statistical analyses, some of which were novel to the literature. Results are discussed from the view of current knowledge of ERP functional properties and with respect to machine learning classification methods previously applied to similar data.<br><br>CONCLUSION: The data showed a high level of accuracy (p &#x2264; 0.01) in the discriminating the perceptual categories eliciting the various electrical potentials by statistical analyses. Therefore, the ERP markers identified in this study could be significant tools for optimizing BCI systems [pattern recognition or artificial intelligence (AI) algorithms] applied to EEG/ERP signals.}, }
@article {pmid36523527, year = {2022}, author = {Kophamel, S and Ward, LC and Konovalov, DA and Mendez, D and Ariel, E and Cassidy, N and Bell, I and Balastegui Martínez, MT and Munns, SL}, title = {Field-based adipose tissue quantification in sea turtles using bioelectrical impedance spectroscopy validated with CT scans and deep learning.}, journal = {Ecology and evolution}, volume = {12}, number = {12}, pages = {e9610}, pmid = {36523527}, issn = {2045-7758}, abstract = {Loss of adipose tissue in vertebrate wildlife species is indicative of decreased nutritional and health status and is linked to environmental stress and diseases. Body condition indices (BCI) are commonly used in ecological studies to estimate adipose tissue mass across wildlife populations. However, these indices have poor predictive power, which poses the need for quantitative methods for improved population assessments. Here, we calibrate bioelectrical impedance spectroscopy (BIS) as an alternative approach for assessing the nutritional status of vertebrate wildlife in ecological studies. BIS is a portable technology that can estimate body composition from measurements of body impedance and is widely used in humans. BIS is a predictive technique that requires calibration using a reference body composition method. Using sea turtles as model organisms, we propose a calibration protocol using computed tomography (CT) scans, with the prediction equation being: adipose tissue mass (kg) = body mass - (-0.03 [intercept] - 0.29 * length[2]/resistance at 50 kHz + 1.07 * body mass - 0.11 * time after capture). CT imaging allows for the quantification of body fat. However, processing the images manually is prohibitive due to the extensive time requirement. Using a form of artificial intelligence (AI), we trained a computer model to identify and quantify nonadipose tissue from the CT images, and adipose tissue was determined by the difference in body mass. This process enabled estimating adipose tissue mass from bioelectrical impedance measurements. The predictive performance of the model was built on 2/3 samples and tested against 1/3 samples. Prediction of adipose tissue percentage had greater accuracy when including impedance parameters (mean bias = 0.11%-0.61%) as predictor variables, compared with using body mass alone (mean bias = 6.35%). Our standardized BIS protocol improves on conventional body composition assessment methods (e.g., BCI) by quantifying adipose tissue mass. The protocol can be applied to other species for the validation of BIS and to provide robust information on the nutritional and health status of wildlife, which, in turn, can be used to inform conservation decisions at the management level.}, }
@article {pmid36523445, year = {2022}, author = {Ferracuti, F and Freddi, A and Iarlori, S and Monteriù, A and Omer, KIM and Porcaro, C}, title = {A human-in-the-loop approach for enhancing mobile robot navigation in presence of obstacles not detected by the sensory set.}, journal = {Frontiers in robotics and AI}, volume = {9}, number = {}, pages = {909971}, pmid = {36523445}, issn = {2296-9144}, abstract = {Human-in-the-loop approaches can greatly enhance the human-robot interaction by making the user an active part of the control loop, who can provide a feedback to the robot in order to augment its capabilities. Such feedback becomes even more important in all those situations where safety is of utmost concern, such as in assistive robotics. This study aims to realize a human-in-the-loop approach, where the human can provide a feedback to a specific robot, namely, a smart wheelchair, to augment its artificial sensory set, extending and improving its capabilities to detect and avoid obstacles. The feedback is provided by both a keyboard and a brain-computer interface: with this scope, the work has also included a protocol design phase to elicit and evoke human brain event-related potentials. The whole architecture has been validated within a simulated robotic environment, with electroencephalography signals acquired from different test subjects.}, }
@article {pmid36522455, year = {2022}, author = {Iwama, S and Yanagisawa, T and Hirose, R and Ushiba, J}, title = {Beta rhythmicity in human motor cortex reflects neural population coupling that modulates subsequent finger coordination stability.}, journal = {Communications biology}, volume = {5}, number = {1}, pages = {1375}, pmid = {36522455}, issn = {2399-3642}, mesh = {Humans ; *Motor Cortex/physiology ; Movement/physiology ; Transcranial Magnetic Stimulation/methods ; Electroencephalography ; Periodicity ; }, abstract = {Human behavior is not performed completely as desired, but is influenced by the inherent rhythmicity of the brain. Here we show that anti-phase bimanual coordination stability is regulated by the dynamics of pre-movement neural oscillations in bi-hemispheric primary motor cortices (M1) and supplementary motor area (SMA). In experiment 1, pre-movement bi-hemispheric M1 phase synchrony in beta-band (M1-M1 phase synchrony) was online estimated from 129-channel scalp electroencephalograms. Anti-phase bimanual tapping preceded by lower M1-M1 phase synchrony exhibited significantly longer duration than tapping preceded by higher M1-M1 phase synchrony. Further, the inter-individual variability of duration was explained by the interaction of pre-movement activities within the motor network; lower M1-M1 phase synchrony and spectral power at SMA were associated with longer duration. The necessity of cortical interaction for anti-phase maintenance was revealed by sham-controlled repetitive transcranial magnetic stimulation over SMA in another experiment. Our results demonstrate that pre-movement cortical oscillatory coupling within the motor network unknowingly influences bimanual coordination performance in humans after consolidation, suggesting the feasibility of augmenting human motor ability by covertly monitoring preparatory neural dynamics.}, }
@article {pmid36515725, year = {2022}, author = {Kruppa, C and Benner, S and Brinkemper, A and Aach, M and Reimertz, C and Schildhauer, TA}, title = {[New technologies and robotics].}, journal = {Unfallchirurgie (Heidelberg, Germany)}, volume = {}, number = {}, pages = {}, pmid = {36515725}, issn = {2731-703X}, abstract = {The development of increasingly more complex computer and electromotor technologies enables the increasing use and expansion of robot-assisted systems in trauma surgery rehabilitation; however, the currently available devices are rarely comprehensively applied but are often used within pilot projects and studies. Different technological approaches, such as exoskeletal systems, functional electrical stimulation, soft robotics, neurorobotics and brain-machine interfaces are used and combined to read and process the communication between, e.g., residual musculature or brain waves, to transfer them to the executing device and to enable the desired execution.Currently, the greatest amount of evidence exists for the use of exoskeletal systems with different modes of action in the context of gait and stance rehabilitation in paraplegic patients; however, their use also plays a role in the rehabilitation of fractures close to the hip joint and endoprosthetic care. So-called single joint systems are also being tested in the rehabilitation of functionally impaired extremities, e.g., after knee prosthesis implantation. At this point, however, the current data situation is still too limited to be able to make a clear statement about the use of these technologies in the trauma surgery "core business" of rehabilitation after fractures and other joint injuries.For rehabilitation after limb amputation, in addition to the further development of myoelectric prostheses, the current development of "sentient" prostheses is of great interest. The use of 3D printing also plays a role in the production of individualized devices.Due to the current progress of artificial intelligence in all fields, ground-breaking further developments and widespread application possibilities in the rehabilitation of trauma patients are to be expected.}, }
@article {pmid36509440, year = {2023}, author = {Shapiro, SB and Llerena, PA and Mowery, TM and Miele, EA and Wackym, PA}, title = {Subtemporalis Muscle Middle Cranial Fossa Bone-Island Craniotomy Technique for Placement of an Active Transcutaneous Bone-Conduction Implant.}, journal = {Otology &amp; neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology}, volume = {44}, number = {1}, pages = {54-60}, pmid = {36509440}, issn = {1537-4505}, mesh = {Humans ; *Hearing Loss, Mixed Conductive-Sensorineural/surgery ; Bone Conduction/physiology ; Cranial Fossa, Middle/surgery ; *Hearing Aids ; Muscles ; Hearing Loss, Conductive/surgery ; Treatment Outcome ; *Speech Perception ; }, abstract = {OBJECTIVE: Placement of an active transcutaneous bone-conduction implant (BCI) requires drilling of a precise bone bed to accommodate the device and allow for fixation points to make appropriate contact with bone, which can be difficult even when lifts are used. We describe a subtemporalis muscle middle cranial fossa bone-island craniotomy technique that simplifies the procedure and obviates the need for lifts in securing the device.<br><br>STUDY DESIGN: Prospective case series.<br><br>SETTING: Tertiary academic medical center.<br><br>PATIENTS: Seventeen patients underwent surgery for placement of 18 transcutaneous BCIs, 14 for conductive or mixed hearing loss, and 4 for single-sided deafness.<br><br>INTERVENTIONS: Surgical placement of a transcutaneous BCI with a bone-island craniotomy technique.<br><br>MAIN OUTCOME MEASURES: Functional gain in air-conduction thresholds, aided air-bone gap, frequency of need for lifts, and minor and major complications.<br><br>RESULTS: For the conductive or mixed hearing loss cohort, with the transcutaneous BCI in place, there was a highly statistically significant mean functional gain of 35.4 dB hearing level (HL) (range, 16.7-50.25 dB HL; standard deviation, 12.4 dB HL) compared with the unaided condition (p < 0.0001; 95% confidence interval, 36.6-51.6 dB HL). Lifts were not needed in any case. There was one minor complication requiring a second procedure in a patient who had previously received radiation and no major complications. There was no device loss or failure.<br><br>CONCLUSIONS: A subtemporalis muscle middle cranial fossa bone-island craniotomy technique eliminates the need for lifts and is a safe and effective method for placement of a transcutaneous BCI.}, }
@article {pmid36507325, year = {2022}, author = {Du, Y and Huang, J and Huang, X and Shi, K and Zhou, N}, title = {Dual attentive fusion for EEG-based brain-computer interfaces.}, journal = {Frontiers in neuroscience}, volume = {16}, number = {}, pages = {1044631}, pmid = {36507325}, issn = {1662-4548}, abstract = {The classification based on Electroencephalogram (EEG) is a challenging task in the brain-computer interface (BCI) field due to data with a low signal-to-noise ratio. Most current deep learning based studies in this challenge focus on designing a desired convolutional neural network (CNN) to learn and classify the raw EEG signals. However, only CNN itself may not capture the highly discriminative patterns of EEG due to a lack of exploration of attentive spatial and temporal dynamics. To improve information utilization, this study proposes a Dual Attentive Fusion Model (DAFM) for the EEG-based BCI. DAFM is employed to capture the spatial and temporal information by modeling the interdependencies between the features from the EEG signals. To our best knowledge, our method is the first to fuse spatial and temporal dimensions in an interactive attention module. This module improves the expression ability of the extracted features. Extensive experiments implemented on four publicly available datasets demonstrate that our method outperforms state-of-the-art methods. Meanwhile, this work also indicates the effectiveness of Dual Attentive Fusion Module.}, }
@article {pmid36507305, year = {2022}, author = {La Fisca, L and Vandenbulcke, V and Wauthia, E and Miceli, A and Simoes Loureiro, I and Ris, L and Lefebvre, L and Gosselin, B and Pernet, CR}, title = {Biases in BCI experiments: Do we really need to balance stimulus properties across categories?.}, journal = {Frontiers in computational neuroscience}, volume = {16}, number = {}, pages = {900571}, pmid = {36507305}, issn = {1662-5188}, abstract = {Brain Computer Interfaces (BCIs) consist of an interaction between humans and computers with a specific mean of communication, such as voice, gestures, or even brain signals that are usually recorded by an Electroencephalogram (EEG). To ensure an optimal interaction, the BCI algorithm typically involves the classification of the input signals into predefined task-specific categories. However, a recurrent problem is that the classifier can easily be biased by uncontrolled experimental conditions, namely covariates, that are unbalanced across the categories. This issue led to the current solution of forcing the balance of these covariates across the different categories which is time consuming and drastically decreases the dataset diversity. The purpose of this research is to evaluate the need for this forced balance in BCI experiments involving EEG data. A typical design of neural BCIs involves repeated experimental trials using visual stimuli to trigger the so-called Event-Related Potential (ERP). The classifier is expected to learn spatio-temporal patterns specific to categories rather than patterns related to uncontrolled stimulus properties, such as psycho-linguistic variables (e.g., phoneme number, familiarity, and age of acquisition) and image properties (e.g., contrast, compactness, and homogeneity). The challenges are then to know how biased the decision is, which features affect the classification the most, which part of the signal is impacted, and what is the probability to perform neural categorization per se. To address these problems, this research has two main objectives: (1) modeling and quantifying the covariate effects to identify spatio-temporal regions of the EEG allowing maximal classification performance while minimizing the biasing effect, and (2) evaluating the need to balance the covariates across categories when studying brain mechanisms. To solve the modeling problem, we propose using a linear parametric analysis applied to some observable and commonly studied covariates to them. The biasing effect is quantified by comparing the regions highly influenced by the covariates with the regions of high categorical contrast, i.e., parts of the ERP allowing a reliable classification. The need to balance the stimulus's inner properties across categories is evaluated by assessing the separability between category-related and covariate-related evoked responses. The procedure is applied to a visual priming experiment where the images represent items belonging to living or non-living entities. The observed covariates are the commonly controlled psycho-linguistic variables and some visual features of the images. As a result, we identified that the category of the stimulus mostly affects the late evoked response. The covariates, when not modeled, have a biasing effect on the classification, essentially in the early evoked response. This effect increases with the diversity of the dataset and the complexity of the algorithm used. As the effects of both psycho-linguistic variables and image features appear outside of the spatio-temporal regions of significant categorical contrast, the proper selection of the region of interest makes the classification reliable. Having proved that the covariate effects can be separated from the categorical effect, our framework can be further used to isolate the category-dependent evoked response from the rest of the EEG to study neural processes involved when seeing living vs. non-living entities.}, }
@article {pmid36507057, year = {2022}, author = {Wang, Y and Liu, S and Wang, H and Zhao, Y and Zhang, XD}, title = {Neuron devices: emerging prospects in neural interfaces and recognition.}, journal = {Microsystems &amp; nanoengineering}, volume = {8}, number = {}, pages = {128}, pmid = {36507057}, issn = {2055-7434}, abstract = {Neuron interface devices can be used to explore the relationships between neuron firing and synaptic transmission, as well as to diagnose and treat neurological disorders, such as epilepsy and Alzheimer's disease. It is crucial to exploit neuron devices with high sensitivity, high biocompatibility, multifunctional integration and high-speed data processing. During the past decades, researchers have made significant progress in neural electrodes, artificial sensory neuron devices, and neuromorphic optic neuron devices. The main part of the review is divided into two sections, providing an overview of recently developed neuron interface devices for recording electrophysiological signals, as well as applications in neuromodulation, simulating the human sensory system, and achieving memory and recognition. We mainly discussed the development, characteristics, functional mechanisms, and applications of neuron devices and elucidated several key points for clinical translation. The present review highlights the advances in neuron devices on brain-computer interfaces and neuroscience research.}, }
@article {pmid36504642, year = {2022}, author = {Ren, Z and Han, X and Wang, B}, title = {The performance evaluation of the state-of-the-art EEG-based seizure prediction models.}, journal = {Frontiers in neurology}, volume = {13}, number = {}, pages = {1016224}, pmid = {36504642}, issn = {1664-2295}, abstract = {The recurrent and unpredictable nature of seizures can lead to unintentional injuries and even death. The rapid development of electroencephalogram (EEG) and Artificial Intelligence (AI) technologies has made it possible to predict seizures in real-time through brain-machine interfaces (BCI), allowing advanced intervention. To date, there is still much room for improvement in predictive seizure models constructed by EEG using machine learning (ML) and deep learning (DL). But, the most critical issue is how to improve the performance and generalization of the model, which involves some confusing conceptual and methodological issues. This review focuses on analyzing several factors affecting the performance of seizure prediction models, focusing on the aspects of post-processing, seizure occurrence period (SOP), seizure prediction horizon (SPH), and algorithms. Furthermore, this study presents some new directions and suggestions for building high-performance prediction models in the future. We aimed to clarify the concept for future research in related fields and improve the performance of prediction models to provide a theoretical basis for future applications of wearable seizure detection devices.}, }
@article {pmid36502931, year = {2023}, author = {Ojeda, A and Wagner, M and Maric, V and Ramanathan, D and Mishra, J}, title = {EEG source derived salience network coupling supports real-world attention switching.}, journal = {Neuropsychologia}, volume = {178}, number = {}, pages = {108445}, doi = {10.1016/j.neuropsychologia.2022.108445}, pmid = {36502931}, issn = {1873-3514}, abstract = {While the brain mechanisms underlying selective attention have been studied in great detail in controlled laboratory settings, it is less clear how these processes function in the context of a real-world self-paced task. Here, we investigated engagement on a real-world computerized task equivalent to a standard academic test that consisted of solving high-school level problems in a self-paced manner. In this task, we used EEG-source derived estimates of effective coupling between brain sources to characterize the neural mechanisms underlying switches of sustained attention from the attentive on-task state to the distracted off-task state. Specifically, since the salience network has been implicated in sustained attention and attention switching, we conducted a hypothesis-driven analysis of effective coupling between the core nodes of the salience network, the anterior insula (AI) and the anterior cingulate cortex (ACC). As per our hypothesis, we found an increase in AI - > ACC effective coupling that occurs during the transitions of attention from on-task focused to off-task distracted state. This research may inform the development of future neural function-targeted brain-computer interfaces to enhance sustained attention.}, }
@article {pmid36502205, year = {2022}, author = {Fernández-Rodríguez, &#xc1; and Darves-Bornoz, A and Velasco-Álvarez, F and Ron-Angevin, R}, title = {Effect of Stimulus Size in a Visual ERP-Based BCI under RSVP.}, journal = {Sensors (Basel, Switzerland)}, volume = {22}, number = {23}, pages = {}, pmid = {36502205}, issn = {1424-8220}, mesh = {Humans ; *Brain-Computer Interfaces ; Evoked Potentials ; Eye Movements ; Electroencephalography/methods ; }, abstract = {Rapid serial visual presentation (RSVP) is currently one of the most suitable paradigms for use with a visual brain-computer interface based on event-related potentials (ERP-BCI) by patients with a lack of ocular motility. However, gaze-independent paradigms have not been studied as closely as gaze-dependent ones, and variables such as the sizes of the stimuli presented have not yet been explored under RSVP. Hence, the aim of the present work is to assess whether stimulus size has an impact on ERP-BCI performance under the RSVP paradigm. Twelve participants tested the ERP-BCI under RSVP using three different stimulus sizes: small (0.1 × 0.1 cm), medium (1.9 × 1.8 cm), and large (20.05 × 19.9 cm) at 60 cm. The results showed significant differences in accuracy between the conditions; the larger the stimulus, the better the accuracy obtained. It was also shown that these differences were not due to incorrect perception of the stimuli since there was no effect from the size in a perceptual discrimination task. The present work therefore shows that stimulus size has an impact on the performance of an ERP-BCI under RSVP. This finding should be considered by future ERP-BCI proposals aimed at users who need gaze-independent systems.}, }
@article {pmid36501860, year = {2022}, author = {Gannouni, S and Belwafi, K and Alangari, N and AboAlsamh, H and Belghith, A}, title = {Classification Strategies for P300-Based BCI-Spellers Adopting the Row Column Paradigm.}, journal = {Sensors (Basel, Switzerland)}, volume = {22}, number = {23}, pages = {}, pmid = {36501860}, issn = {1424-8220}, mesh = {Humans ; *Brain-Computer Interfaces ; Event-Related Potentials, P300 ; Electroencephalography ; Signal Processing, Computer-Assisted ; User-Computer Interface ; }, abstract = {Acknowledging the importance of the ability to communicate with other people, the researcher community has developed a series of BCI-spellers, with the goal of regaining communication and interaction capabilities with the environment for people with disabilities. In order to bridge the gap in the digital divide between the disabled and the non-disabled people, we believe that the development of efficient signal processing algorithms and strategies will go a long way towards achieving novel assistive technologies using new human-computer interfaces. In this paper, we present various classification strategies that would be adopted by P300 spellers adopting the row/column paradigm. The presented strategies have obtained high accuracy rates compared with existent similar research works.}, }
@article {pmid36501753, year = {2022}, author = {Jochumsen, M and Hougaard, BI and Kristensen, MS and Knoche, H}, title = {Implementing Performance Accommodation Mechanisms in Online BCI for Stroke Rehabilitation: A Study on Perceived Control and Frustration.}, journal = {Sensors (Basel, Switzerland)}, volume = {22}, number = {23}, pages = {}, pmid = {36501753}, issn = {1424-8220}, mesh = {Humans ; *Stroke Rehabilitation ; Electroencephalography ; *Brain-Computer Interfaces ; Imagery, Psychotherapy ; Feedback ; *Stroke ; }, abstract = {Brain-computer interfaces (BCIs) are successfully used for stroke rehabilitation, but the training is repetitive and patients can lose the motivation to train. Moreover, controlling the BCI may be difficult, which causes frustration and leads to even worse control. Patients might not adhere to the regimen due to frustration and lack of motivation/engagement. The aim of this study was to implement three performance accommodation mechanisms (PAMs) in an online motor imagery-based BCI to aid people and evaluate their perceived control and frustration. Nineteen healthy participants controlled a fishing game with a BCI in four conditions: (1) no help, (2) augmented success (augmented successful BCI-attempt), (3) mitigated failure (turn unsuccessful BCI-attempt into neutral output), and (4) override input (turn unsuccessful BCI-attempt into successful output). Each condition was followed-up and assessed with Likert-scale questionnaires and a post-experiment interview. Perceived control and frustration were best predicted by the amount of positive feedback the participant received. PAM-help increased perceived control for poor BCI-users but decreased it for good BCI-users. The input override PAM frustrated the users the most, and they differed in how they wanted to be helped. By using PAMs, developers have more freedom to create engaging stroke rehabilitation games.}, }
@article {pmid36495049, year = {2022}, author = {Sorinas, J and Troyano, JCF and Ferrández, JM and Fernandez, E}, title = {Unraveling the Development of an Algorithm for Recognizing Primary Emotions Through Electroencephalography.}, journal = {International journal of neural systems}, volume = {}, number = {}, pages = {2250057}, doi = {10.1142/S0129065722500575}, pmid = {36495049}, issn = {1793-6462}, abstract = {The large range of potential applications, not only for patients but also for healthy people, that could be achieved by affective brain-computer interface (aBCI) makes more latent the necessity of finding a commonly accepted protocol for real-time EEG-based emotion recognition. Based on wavelet package for spectral feature extraction, attending to the nature of the EEG signal, we have specified some of the main parameters needed for the implementation of robust positive and negative emotion classification. Twelve seconds has resulted as the most appropriate sliding window size; from that, a set of 20 target frequency-location variables have been proposed as the most relevant features that carry the emotional information. Lastly, QDA and KNN classifiers and population rating criterion for stimuli labeling have been suggested as the most suitable approaches for EEG-based emotion recognition. The proposed model reached a mean accuracy of 98% (s.d. 1.4) and 98.96% (s.d. 1.28) in a subject-dependent (SD) approach for QDA and KNN classifier, respectively. This new model represents a step forward towards real-time classification. Moreover, new insights regarding subject-independent (SI) approximation have been discussed, although the results were not conclusive.}, }
@article {pmid36494390, year = {2022}, author = {Rouanne, V and Costecalde, T and Benabid, AL and Aksenova, T}, title = {Unsupervised adaptation of an ECoG based brain-computer interface using neural correlates of task performance.}, journal = {Scientific reports}, volume = {12}, number = {1}, pages = {21316}, pmid = {36494390}, issn = {2045-2322}, mesh = {*Brain-Computer Interfaces ; Task Performance and Analysis ; Electrocorticography ; Brain ; Computer Simulation ; Electroencephalography ; }, abstract = {Brain-computer interfaces (BCIs) translate brain signals into commands to external effectors, and mainly target severely disabled users. The usability of BCIs may be improved by reducing their major constraints, such as the necessity for special training sessions to initially calibrate and later keep up to date the neural signal decoders. In this study, we show that it is possible to train and update BCI decoders during free use of motor BCIs. In addition to the neural signal decoder controlling effectors (control decoder), one more classifier is proposed to detect neural correlates of BCI motor task performances (MTP). MTP decoders reveal whether the actions performed by BCI effectors matched the user's intentions. The combined outputs of MTP and control decoders allow forming training datasets to update the control decoder online and in real time during free use of BCIs. The usability of the proposed auto-adaptive BCI (aaBCI) is demonstrated for two principle BCIs paradigms: with discrete outputs (4 classes BCI, virtual 4-limb exoskeleton), and with continuous outputs (cursor 2D control). The proof of concept was performed in an online simulation study using an ECoG dataset collected from a tetraplegic during a BCI clinical trial. The control decoder reached a multiclass area under the ROC curve of 0.7404 using aaBCI, compared to a chance level of 0.5173 and to 0.8187 for supervised training for the multiclass BCI, and a cosine similarity of 0.1211 using aaBCI, compared to a chance level of 0.0036 and to 0.2002 for supervised training for the continuous BCI.}, }
@article {pmid36483633, year = {2022}, author = {de Seta, V and Toppi, J and Colamarino, E and Molle, R and Castellani, F and Cincotti, F and Mattia, D and Pichiorri, F}, title = {Cortico-muscular coupling to control a hybrid brain-computer interface for upper limb motor rehabilitation: A pseudo-online study on stroke patients.}, journal = {Frontiers in human neuroscience}, volume = {16}, number = {}, pages = {1016862}, pmid = {36483633}, issn = {1662-5161}, abstract = {Brain-Computer Interface (BCI) systems for motor rehabilitation after stroke have proven their efficacy to enhance upper limb motor recovery by reinforcing motor related brain activity. Hybrid BCIs (h-BCIs) exploit both central and peripheral activation and are frequently used in assistive BCIs to improve classification performances. However, in a rehabilitative context, brain and muscular features should be extracted to promote a favorable motor outcome, reinforcing not only the volitional control in the central motor system, but also the effective projection of motor commands to target muscles, i.e., central-to-peripheral communication. For this reason, we considered cortico-muscular coupling (CMC) as a feature for a h-BCI devoted to post-stroke upper limb motor rehabilitation. In this study, we performed a pseudo-online analysis on 13 healthy participants (CTRL) and 12 stroke patients (EXP) during executed (CTRL, EXP unaffected arm) and attempted (EXP affected arm) hand grasping and extension to optimize the translation of CMC computation and CMC-based movement detection from offline to online. Results showed that updating the CMC computation every 125 ms (shift of the sliding window) and accumulating two predictions before a final classification decision were the best trade-off between accuracy and speed in movement classification, independently from the movement type. The pseudo-online analysis on stroke participants revealed that both attempted and executed grasping/extension can be classified through a CMC-based movement detection with high performances in terms of classification speed (mean delay between movement detection and EMG onset around 580 ms) and accuracy (hit rate around 85%). The results obtained by means of this analysis will ground the design of a novel non-invasive h-BCI in which the control feature is derived from a combined EEG and EMG connectivity pattern estimated during upper limb movement attempts.}, }
@article {pmid36481698, year = {2022}, author = {Chinchani, AM and Paliwal, S and Ganesh, S and Chandrasekhar, V and Yu, BM and Sridharan, D}, title = {Tracking momentary fluctuations in human attention with a cognitive brain-machine interface.}, journal = {Communications biology}, volume = {5}, number = {1}, pages = {1346}, pmid = {36481698}, issn = {2399-3642}, mesh = {Humans ; *Brain-Computer Interfaces ; Cognition ; }, abstract = {Selective attention produces systematic effects on neural states. It is unclear whether, conversely, momentary fluctuations in neural states have behavioral significance for attention. We investigated this question in the human brain with a cognitive brain-machine interface (cBMI) for tracking electrophysiological steady-state visually evoked potentials (SSVEPs) in real-time. Discrimination accuracy (d') was significantly higher when target stimuli were triggered at high, versus low, SSVEP power states. Target and distractor SSVEP power was uncorrelated across the hemifields, and target d' was unaffected by distractor SSVEP power states. Next, we trained participants on an auditory neurofeedback paradigm to generate biased, cross-hemispheric competitive interactions between target and distractor SSVEPs. The strongest behavioral effects emerged when competitive SSVEP dynamics unfolded at a timescale corresponding to the deployment of endogenous attention. In sum, SSVEP power dynamics provide a reliable readout of attentional state, a result with critical implications for tracking and training human attention.}, }
@article {pmid36481619, year = {2022}, author = {Yuan, TF and Ng, CH and Hu, S}, title = {Addressing the mental health of children in quarantine with COVID-19 during the Omicron variant era.}, journal = {Asian journal of psychiatry}, volume = {80}, number = {}, pages = {103371}, pmid = {36481619}, issn = {1876-2026}, }
@article {pmid36478044, year = {2023}, author = {Zhang, Z and Chen, Y and Zheng, L and Du, J and Wei, S and Zhu, X and Xiong, JW}, title = {A DUSP6 inhibitor suppresses inflammatory cardiac remodeling and improves heart function after myocardial infarction.}, journal = {Disease models &amp; mechanisms}, volume = {16}, number = {5}, pages = {}, doi = {10.1242/dmm.049662}, pmid = {36478044}, issn = {1754-8411}, mesh = {Animals ; Rats ; Dual Specificity Phosphatase 6 ; Fibrosis ; *Myocardial Infarction/complications/drug therapy ; Ventricular Remodeling ; }, abstract = {Acute myocardial infarction (MI) results in loss of cardiomyocytes and abnormal cardiac remodeling with severe inflammation and fibrosis. However, how cardiac repair can be achieved by timely resolution of inflammation and cardiac fibrosis remains incompletely understood. Our previous findings have shown that dual-specificity phosphatase 6 (DUSP6) is a regeneration repressor from zebrafish to rats. In this study, we found that intravenous administration of the DUSP6 inhibitor (E)-2-benzylidene-3-(cyclohexylamino)-2,3-dihydro-1H-inden-1-one (BCI) improved heart function and reduced cardiac fibrosis in MI rats. Mechanistic analysis revealed that BCI attenuated macrophage inflammation through NF-κB and p38 signaling, independent of DUSP6 inhibition, leading to the downregulation of various cytokines and chemokines. In addition, BCI suppressed differentiation-related signaling pathways and decreased bone-marrow cell differentiation into macrophages through inhibiting DUSP6. Furthermore, intramyocardial injection of poly (D, L-lactic-co-glycolic acid)-loaded BCI after MI had a notable effect on cardiac repair. In summary, BCI improves heart function and reduces abnormal cardiac remodeling by inhibiting macrophage formation and inflammation post-MI, thus providing a promising pro-drug candidate for the treatment of MI and related heart diseases. This article has an associated First Person interview with the first author of the paper.}, }
@article {pmid36476879, year = {2022}, author = {Cao, W and Li, JH and Lin, S and Xia, QQ and Du, YL and Yang, Q and Ye, YZ and Zeng, LH and Li, XY and Xu, J and Luo, JH}, title = {NMDA receptor hypofunction underlies deficits in parvalbumin interneurons and social behavior in neuroligin 3 R451C knockin mice.}, journal = {Cell reports}, volume = {41}, number = {10}, pages = {111771}, doi = {10.1016/j.celrep.2022.111771}, pmid = {36476879}, issn = {2211-1247}, mesh = {Animals ; Mice ; *Autism Spectrum Disorder ; *Parvalbumins ; Receptors, N-Methyl-D-Aspartate ; Social Behavior ; }, abstract = {Neuroligins (NLs), a family of postsynaptic cell-adhesion molecules, have been associated with autism spectrum disorder. We have reported that dysfunction of the medial prefrontal cortex (mPFC) leads to social deficits in an NL3 R451C knockin (KI) mouse model of autism. However, the underlying molecular mechanism remains unclear. Here, we find that N-methyl-D-aspartate receptor (NMDAR) function and parvalbumin-positive (PV+) interneuron number and expression are reduced in the mPFC of the KI mice. Selective knockdown of NMDAR subunit GluN1 in the mPFC PV+ interneuron decreases its intrinsic excitability. Restoring NMDAR function by its partial agonist D-cycloserine rescues the PV+ interneuron dysfunction and social deficits in the KI mice. Interestingly, early D-cycloserine administration at adolescence prevents adult KI mice from social deficits. Together, our results suggest that NMDAR hypofunction and the resultant PV+ interneuron dysfunction in the mPFC may constitute a central node in the pathogenesis of social deficits in the KI mice.}, }
@article {pmid36476748, year = {2022}, author = {Asahina, T and Shimba, K and Kotani, K and Jimbo, Y}, title = {Improving the accuracy of decoding monkey brain-machine interface data by estimating the state of unobserved cell assemblies.}, journal = {Journal of neuroscience methods}, volume = {385}, number = {}, pages = {109764}, doi = {10.1016/j.jneumeth.2022.109764}, pmid = {36476748}, issn = {1872-678X}, abstract = {BACKGROUND: The brain-machine interface is a technology that has been used for improving the quality of life of individuals with physical disabilities and also healthy individuals. It is important to improve the methods used for decoding the brain-machine interface data as the accuracy and speed of movements achieved using the existing technology are not comparable to the normal body.<br><br>Decoding of brain-machine interface data using the proposed method resulted in improved decoding accuracy compared to the existing method.<br><br>CONCLUSIONS: The results demonstrated the usefulness of cell assembly state estimation method for decoding the brain-machine interface data.<br><br>NEW METHOD: We incorporated a novel method of estimating cell assembly states using spike trains with the existing decoding method that used only firing rate data. Synaptic connectivity pattern was used as feature values in addition to firing rate. Publicly available monkey brain-machine interface datasets were used in the study.<br><br>RESULTS: As long as the decoding was successful, the root mean square error of the proposed method was significantly smaller than the existing method. Artificial neural netowork-based decoding method resulted in more stable decoding, and also improved the decoding accuracy due to incorporation of synaptic connectivity pattern.}, }
@article {pmid36471144, year = {2022}, author = {Bex, A and Mathon, B}, title = {Advances, technological innovations, and future prospects in stereotactic brain biopsies.}, journal = {Neurosurgical review}, volume = {46}, number = {1}, pages = {5}, pmid = {36471144}, issn = {1437-2320}, mesh = {Humans ; *Brain Neoplasms/diagnosis/surgery/pathology ; Inventions ; Stereotaxic Techniques ; Biopsy/methods ; Brain/surgery/pathology ; }, abstract = {Stereotactic brain biopsy is one of the most frequently performed brain surgeries. This review aimed to expose the latest cutting-edge and updated technologies and innovations available to neurosurgeons to safely perform stereotactic brain biopsy by minimizing the risks of complications and ensuring that the procedure is successful, leading to a histological diagnosis. We also examined methods for improving preoperative, intraoperative, and postoperative workflows. We performed a comprehensive state-of-the-art literature review. Intraoperative histology, fluorescence, and imaging techniques appear as smart tools to improve the diagnostic yield of biopsy. Constant innovations such as optical methods and augmented reality are also being made to increase patient safety. Robotics and integrated imaging techniques provide an enhanced intraoperative workflow. Patients' management algorithms based on early discharge after biopsy optimize the patient's personal experience and make the most efficient possible use of the available hospital resources. Many new trends are emerging, constantly improving patient care and safety, as well as surgical workflow. A parameter that must be considered is the cost-effectiveness of these devices and the possibility of using them on a daily basis. The decision to implement a new instrument in the surgical workflow should also be dependent on the number of procedures per year, the existing stereotactic equipment, and the experience of each center. Research on patients' postbiopsy management is another mandatory approach to enhance the safety profile of stereotactic brain biopsy and patient satisfaction, as well as to reduce healthcare costs.}, }
@article {pmid36471022, year = {2022}, author = {Chen, W and Wu, J and Wei, R and Wu, S and Xia, C and Wang, D and Liu, D and Zheng, L and Zou, T and Li, R and Qi, X and Zhang, X}, title = {Improving the diagnosis of acute ischemic stroke on non-contrast CT using deep learning: a multicenter study.}, journal = {Insights into imaging}, volume = {13}, number = {1}, pages = {184}, pmid = {36471022}, issn = {1869-4101}, abstract = {OBJECTIVE: This study aimed to develop a deep learning (DL) model to improve the diagnostic performance of EIC and ASPECTS in acute ischemic stroke (AIS).<br><br>METHODS: Acute ischemic stroke patients were retrospectively enrolled from 5 hospitals. We proposed a deep learning model to simultaneously segment the infarct and estimate ASPECTS automatically using baseline CT. The model performance of segmentation and ASPECTS scoring was evaluated using dice similarity coefficient (DSC) and ROC, respectively. Four raters participated in the multi-reader and multicenter (MRMC) experiment to fulfill the region-based ASPECTS reading under the assistance of the model or not. At last, sensitivity, specificity, interpretation time and interrater agreement were used to evaluate the raters' reading performance.<br><br>RESULTS: In total, 1391 patients were enrolled for model development and 85 patients for external validation with onset to CT scanning time of 176.4&#x2009;&#xb1;&#x2009;93.6&#xa0;min and NIHSS of 5 (IQR 2-10). The model achieved a DSC of 0.600 and 0.762 and an AUC of 0.876 (CI 0.846-0.907) and 0.729 (CI 0.679-0.779), in the internal and external validation set, respectively. The assistance of the DL model improved the raters' average sensitivities and specificities from 0.254 (CI 0.22-0.26) and 0.896 (CI 0.884-0.907), to 0.333 (CI 0.301-0.345) and 0.915 (CI 0.904-0.926), respectively. The average interpretation time of the raters was reduced from 219.0 to 175.7&#xa0;s (p&#x2009;=&#x2009;0.035). Meanwhile, the interrater agreement increased from 0.741 to 0.980.<br><br>CONCLUSIONS: With the assistance of our proposed DL model, radiologists got better performance in the detection of AIS lesions on NCCT.}, }
@article {pmid36470437, year = {2022}, author = {Wang, H and Xia, H and Xu, Z and Natsuki, T and Ni, QQ}, title = {Effect of surface structure on the antithrombogenicity performance of poly(-caprolactone)-cellulose acetate small-diameter tubular scaffolds.}, journal = {International journal of biological macromolecules}, volume = {226}, number = {}, pages = {132-142}, doi = {10.1016/j.ijbiomac.2022.11.315}, pmid = {36470437}, issn = {1879-0003}, abstract = {Small-diameter artificial blood vessels have always faced the problem of thrombosis. In this research, three types of poly(-caprolactone)-cellulose acetate (PCL-CA) composite nanofiber membranes were prepared by various collectors to make into a tubular scaffold with a 4.5-mm diameter. The collector consisted of two sizes of stainless steel wire mesh large-mesh (LM) and small-mesh (SM), respectively. There is also a random flat (RF) that acts as the third type collector. The nanofiber membrane's surface structure mimicked the collectors' surface morphology, they named LM, SM and RF scaffolds. The water contact angles of RF and LM scaffolds are 126.5° and 105.5°, and the distinct square-groove construction greatly improves the contact angle of LM. The tubular scaffolds' radial mechanical property test demonstrated that the large-mesh (LM) tubular scaffold enhanced the strain and tensile strength; the tensile strength and strain are 30 % and 148 % higher than that of the random-flat (RF) tubular scaffold, respectively. The suture retention strength value of the LM tubular scaffold was 103 % higher than that of the RF tubular scaffold. The cytotoxicity and antithrombogenicity performance were also evaluated, the LM tubular scaffold has 88 % cell viability, and the 5-min blood coagulation index (BCI) value was 89 %, which is much higher than other tubular scaffolds. The findings indicate that changing the tubular scaffold's surface morphology cannot only enhance the mechanical and hydrophilic properties but also increase cell survival and antithrombogenicity performance. Thus, the development of a small-diameter artificial blood vessel will be a big step toward solving the problem on thrombosis. Furthermore, artificial blood vessel is expected to be a candidate material for biomedical applications.}, }
@article {pmid36468060, year = {2022}, author = {Carino-Escobar, RI and Rodríguez-García, ME and Ramirez-Nava, AG and Quinzaños-Fresnedo, J and Ortega-Robles, E and Arias-Carrion, O and Valdés-Cristerna, R and Cantillo-Negrete, J}, title = {A case report: Upper limb recovery from stroke related to SARS-CoV-2 infection during an intervention with a brain-computer interface.}, journal = {Frontiers in neurology}, volume = {13}, number = {}, pages = {1010328}, pmid = {36468060}, issn = {1664-2295}, abstract = {COVID-19 may increase the risk of acute ischemic stroke that can cause a loss of upper limb function, even in patients with low risk factors. However, only individual cases have been reported assessing different degrees of hospitalization outcomes. Therefore, outpatient recovery profiles during rehabilitation interventions are needed to better understand neuroplasticity mechanisms required for upper limb motor recovery. Here, we report the progression of physiological and clinical outcomes during upper limb rehabilitation of a 41-year-old patient, without any stroke risk factors, which presented a stroke on the same day as being diagnosed with COVID-19. The patient, who presented hemiparesis with incomplete motor recovery after conventional treatment, participated in a clinical trial consisting of an experimental brain-computer interface (BCI) therapy focused on upper limb rehabilitation during the chronic stage of stroke. Clinical and physiological features were measured throughout the intervention, including the Fugl-Meyer Assessment for the Upper Extremity (FMA-UE), Action Research Arm Test (ARAT), the Modified Ashworth Scale (MAS), corticospinal excitability using transcranial magnetic stimulation, cortical activity with electroencephalography, and upper limb strength. After the intervention, the patient gained 8 points and 24 points of FMA-UE and ARAT, respectively, along with a reduction of one point of MAS. In addition, grip and pinch strength doubled. Corticospinal excitability of the affected hemisphere increased while it decreased in the unaffected hemisphere. Moreover, cortical activity became more pronounced in the affected hemisphere during movement intention of the paralyzed hand. Recovery was higher compared to that reported in other BCI interventions in stroke and was due to a reengagement of the primary motor cortex of the affected hemisphere during hand motor control. This suggests that patients with stroke related to COVID-19 may benefit from a BCI intervention and highlights the possibility of a significant recovery in these patients, even in the chronic stage of stroke.}, }
@article {pmid36466619, year = {2022}, author = {Mussi, MG and Adams, KD}, title = {EEG hybrid brain-computer interfaces: A scoping review applying an existing hybrid-BCI taxonomy and considerations for pediatric applications.}, journal = {Frontiers in human neuroscience}, volume = {16}, number = {}, pages = {1007136}, pmid = {36466619}, issn = {1662-5161}, abstract = {Most hybrid brain-computer interfaces (hBCI) aim at improving the performance of single-input BCI. Many combinations are possible to configure an hBCI, such as using multiple brain input signals, different stimuli or more than one input system. Multiple studies have been done since 2010 where such interfaces have been tested and analyzed. Results and conclusions are promising but little has been discussed as to what is the best approach for the pediatric population, should they use hBCI as an assistive technology. Children might face greater challenges when using BCI and might benefit from less complex interfaces. Hence, in this scoping review we included 42 papers that developed hBCI systems for the purpose of control of assistive devices or communication software, and we analyzed them through the lenses of potential use in clinical settings and for children. We extracted taxonomic categories proposed in previous studies to describe the types of interfaces that have been developed. We also proposed interface characteristics that could be observed in different hBCI, such as type of target, number of targets and number of steps before selection. Then, we discussed how each of the extracted characteristics could influence the overall complexity of the system and what might be the best options for applications for children. Effectiveness and efficiency were also collected and included in the analysis. We concluded that the least complex hBCI interfaces might involve having a brain inputs and an external input, with a sequential role of operation, and visual stimuli. Those interfaces might also use a minimal number of targets of the strobic type, with one or two steps before the final selection. We hope this review can be used as a guideline for future hBCI developments and as an incentive to the design of interfaces that can also serve children who have motor impairments.}, }
@article {pmid36463881, year = {2022}, author = {Senathirajah, Y and Solomonides, AE}, title = {Best Papers in Human Factors and Sociotechnical Development.}, journal = {Yearbook of medical informatics}, volume = {31}, number = {1}, pages = {221-225}, pmid = {36463881}, issn = {2364-0502}, mesh = {Humans ; *COVID-19 ; *Medical Informatics ; Electronic Health Records ; MEDLINE ; *Social Media ; }, abstract = {OBJECTIVES: To select the best papers that made original and high impact contributions in human factors and organizational issues in biomedical informatics in 2021.<br><br>METHODS: A rigorous extraction process based on queries from Web of Science&#xae; and PubMed/Medline was conducted to identify the scientific contributions published in 2021 that address human factors and organizational issues in biomedical informatics. The screening of papers on titles and abstracts independently by the two section editors led to a total of 3,206 papers. These papers were discussed for a selection of 12 finalist papers, which were then reviewed by the two section editors, two chief editors, and by three external reviewers from internationally renowned research teams.<br><br>RESULTS: The query process resulted in 12 papers that reveal interesting and rigorous methods and important studies in human factors that move the field forward, particularly in clinical informatics and emerging technologies such as brain-computer interfaces and mobile health. This year three papers were clearly outstanding and help advance in the field. They provide examples of examining novel and important topics such as the nature of human-machine interaction behavior and norms, use of social-media based design for an electronic health record, and emerging topics such as brain-computer interfaces. thematic development of electronic health records and usability techniques, and condition-focused patient facing tools. Those concerning the Corona Virus Disease 2019 (COVID-19) were included as part of that section.<br><br>CONCLUSION: The selected papers make important contributions to human factors and organizational issues, expanding and deepening our knowledge of how to apply theory and applications of new technologies in health.}, }
@article {pmid36463200, year = {2022}, author = {Yan, JJ and Ding, XJ and He, T and Chen, AX and Zhang, W and Yu, ZX and Cheng, XY and Wei, CY and Hu, QD and Liu, XY and Zhang, YL and He, M and Xie, ZY and Zha, X and Xu, C and Cao, P and Li, H and Xu, XH}, title = {A circuit from the ventral subiculum to anterior hypothalamic nucleus GABAergic neurons essential for anxiety-like behavioral avoidance.}, journal = {Nature communications}, volume = {13}, number = {1}, pages = {7464}, pmid = {36463200}, issn = {2041-1723}, mesh = {Male ; Animals ; Mice ; *GABAergic Neurons ; *Anterior Hypothalamic Nucleus ; Anxiety Disorders ; Anxiety ; Hippocampus ; }, abstract = {Behavioral observations suggest a connection between anxiety and predator defense, but the underlying neural mechanisms remain unclear. Here we examine the role of the anterior hypothalamic nucleus (AHN), a node in the predator defense network, in anxiety-like behaviors. By in vivo recordings in male mice, we find that activity of AHN GABAergic (AHN[Vgat+]) neurons shows individually stable increases when animals approach unfamiliar objects in an open field (OF) or when they explore the open-arm of an elevated plus-maze (EPM). Moreover, object-evoked AHN activity overlap with predator cue responses and correlate with the object and open-arm avoidance. Crucially, exploration-triggered optogenetic inhibition of AHN[Vgat+] neurons reduces object and open-arm avoidance. Furthermore, retrograde viral tracing identifies the ventral subiculum (vSub) of the hippocampal formation as a significant input to AHN[Vgat+] neurons in driving avoidance behaviors in anxiogenic situations. Thus, convergent activation of AHN[Vgat+] neurons serves as a shared mechanism between anxiety and predator defense to promote behavioral avoidance.}, }
@article {pmid36460220, year = {2022}, author = {Pan, H and Fu, Y and Li, Z and Wen, F and Hu, J and Wu, B}, title = {Images Reconstruction from Functional Magnetic Resonance Imaging Patterns Based on the Improved Deep Generative Multiview Model.}, journal = {Neuroscience}, volume = {509}, number = {}, pages = {103-112}, doi = {10.1016/j.neuroscience.2022.11.021}, pmid = {36460220}, issn = {1873-7544}, abstract = {Reconstructing visual stimulus images from the brain activity signals is an important research task in the field of brain decoding. Many methods of reconstructing visual stimulus images mainly focus on how to use deep learning to classify the brain activities measured by functional magnetic resonance imaging or identify visual stimulus images. Accurate reconstruction of visual stimulus images by using deep learning still remains challenging. This paper proposes an improved deep generative multiview model to further promote the accuracy of reconstructing visual stimulus images. Firstly, an encoder based on residual-in-residual dense blocks is designed to fit the deep and multiview visual features of human natural state, and extract the features of visual stimulus images. Secondly, the structure of original decoder is extended to a deeper network in the deep generative multiview model, which makes the features obtained by each deconvolution layer more distinguishable. Finally, we configure the parameters of the optimizer and compare the performance of various optimizers under different parameter values, and then the one with the best performance is chosen and adopted to the whole model. The performance evaluations conducted on two publicly available datasets demonstrate that the improved model has more accurate reconstruction effectiveness than the original deep generative multiview model.}, }
@article {pmid36456595, year = {2022}, author = {Dimova-Edeleva, V and Ehrlich, SK and Cheng, G}, title = {Brain computer interface to distinguish between self and other related errors in human agent collaboration.}, journal = {Scientific reports}, volume = {12}, number = {1}, pages = {20764}, pmid = {36456595}, issn = {2045-2322}, mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography ; Support Vector Machine ; Movement ; Acclimatization ; }, abstract = {When a human and machine collaborate on a shared task, ambiguous events might occur that could be perceived as an error by the human partner. In such events, spontaneous error-related potentials (ErrPs) are evoked in the human brain. Knowing whom the human perceived as responsible for the error would help a machine in co-adaptation and shared control paradigms to better adapt to human preferences. Therefore, we ask whether self- and agent-related errors evoke different ErrPs. Eleven subjects participated in an electroencephalography human-agent collaboration experiment with a collaborative trajectory-following task on two collaboration levels, where movement errors occurred as trajectory deviations. Independently of the collaboration level, we observed a higher amplitude of the responses on the midline central Cz electrode for self-related errors compared to observed errors made by the agent. On average, Support Vector Machines classified self- and agent-related errors with 72.64% accuracy using subject-specific features. These results demonstrate that ErrPs can tell if a person relates an error to themselves or an external autonomous agent during collaboration. Thus, the collaborative machine will receive more informed feedback for the error attribution that allows appropriate error identification, a possibility for correction, and avoidance in future actions.}, }
@article {pmid36456558, year = {2022}, author = {Tian, X and Chen, Y and Majka, P and Szczupak, D and Perl, YS and Yen, CC and Tong, C and Feng, F and Jiang, H and Glen, D and Deco, G and Rosa, MGP and Silva, AC and Liang, Z and Liu, C}, title = {An integrated resource for functional and structural connectivity of the marmoset brain.}, journal = {Nature communications}, volume = {13}, number = {1}, pages = {7416}, pmid = {36456558}, issn = {2041-1723}, mesh = {Animals ; *Callithrix ; *Brain/diagnostic imaging ; Diffusion Magnetic Resonance Imaging ; Computer Simulation ; }, abstract = {Comprehensive integration of structural and functional connectivity data is required to model brain functions accurately. While resources for studying the structural connectivity of non-human primate brains already exist, their integration with functional connectivity data has remained unavailable. Here we present a comprehensive resource that integrates the most extensive awake marmoset resting-state fMRI data available to date (39 marmoset monkeys, 710 runs, 12117 mins) with previously published cellular-level neuronal tracing data (52 marmoset monkeys, 143 injections) and multi-resolution diffusion MRI datasets. The combination of these data allowed us to (1) map the fine-detailed functional brain networks and cortical parcellations, (2) develop a deep-learning-based parcellation generator that preserves the topographical organization of functional connectivity and reflects individual variabilities, and (3) investigate the structural basis underlying functional connectivity by computational modeling. This resource will enable modeling structure-function relationships and facilitate future comparative and translational studies of primate brains.}, }
@article {pmid36455079, year = {2022}, author = {Bian, R and Wu, H and Liu, B and Wu, D}, title = {Small Data Least-Squares Transformation (sd-LST) for Fast Calibration of SSVEP-based BCIs.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2022.3225878}, pmid = {36455079}, issn = {1558-0210}, abstract = {Steady-state visual evoked potential (SSVEP) is one of the most popular brain-computer interface (BCI) paradigms, with high information transmission rate and signal-to-noise ratio. Many calibration-free and calibration-based approaches have been proposed to improve the performance of SSVEP-based BCIs. This paper considers a quick calibration scenario, where there are plenty of data from multiple source subjects, but only a small number of calibration trials from a subset of stimulus frequencies for the new subject. We propose small data least-squares transformation (sd-LST) to solve this problem. Experiments on three publicly available SSVEP datasets demonstrated that sd-LST outperformed several classical or state-of-the-art approaches, with only about 10 calibration trials for 40-target SSVEP-based BCI spellers.}, }
@article {pmid36452175, year = {2022}, author = {Weisinger, B and Pandey, DP and Saver, JL and Hochberg, A and Bitton, A and Doniger, GM and Lifshitz, A and Vardi, O and Shohami, E and Segal, Y and Reznik Balter, S and Djemal Kay, Y and Alter, A and Prasad, A and Bornstein, NM}, title = {Frequency-tuned electromagnetic field therapy improves post-stroke motor function: A pilot randomized controlled trial.}, journal = {Frontiers in neurology}, volume = {13}, number = {}, pages = {1004677}, pmid = {36452175}, issn = {1664-2295}, abstract = {BACKGROUND AND PURPOSE: Impaired upper extremity (UE) motor function is a common disability after ischemic stroke. Exposure to extremely low frequency and low intensity electromagnetic fields (ELF-EMF) in a frequency-specific manner (Electromagnetic Network Targeting Field therapy; ENTF therapy) is a non-invasive method available to a wide range of patients that may enhance neuroplasticity, potentially facilitating motor recovery. This study seeks to quantify the benefit of the ENTF therapy on UE motor function in a subacute ischemic stroke population.<br><br>METHODS: In a randomized, sham-controlled, double-blind trial, ischemic stroke patients in the subacute phase with moderately to severely impaired UE function were randomly allocated to active or sham treatment with a novel, non-invasive, brain computer interface-based, extremely low frequency and low intensity ENTF therapy (1-100 Hz, < 1 G). Participants received 40 min of active ENTF or sham treatment 5 days/week for 8 weeks; ~three out of the five treatments were accompanied by 10 min of concurrent physical/occupational therapy. Primary efficacy outcome was improvement on the Fugl-Meyer Assessment - Upper Extremity (FMA-UE) from baseline to end of treatment (8 weeks).<br><br>RESULTS: In the per protocol set (13 ENTF and 8 sham participants), mean age was 54.7 years (&#xb1;15.0), 19% were female, baseline FMA-UE score was 23.7 (&#xb1;11.0), and median time from stroke onset to first stimulation was 11 days (interquartile range (IQR) 8-15). Greater improvement on the FMA-UE from baseline to week 4 was seen with ENTF compared to sham stimulation, 23.2 &#xb1; 14.1 vs. 9.6 &#xb1; 9.0, p = 0.007; baseline to week 8 improvement was 31.5 &#xb1; 10.7 vs. 23.1 &#xb1; 14.1. Similar favorable effects at week 8 were observed for other UE and global disability assessments, including the Action Research Arm Test (Pinch, 13.4 &#xb1; 5.6 vs. 5.3 &#xb1; 6.5, p = 0.008), Box and Blocks Test (affected hand, 22.5 &#xb1; 12.4 vs. 8.5 &#xb1; 8.6, p < 0.0001), and modified Rankin Scale (-2.5 &#xb1; 0.7 vs. -1.3 &#xb1; 0.7, p = 0.0005). No treatment-related adverse events were reported.<br><br>CONCLUSIONS: ENTF stimulation in subacute ischemic stroke patients was associated with improved UE motor function and reduced overall disability, and results support its safe use in the indicated population. These results should be confirmed in larger multicenter studies.<br><br>CLINICAL TRIAL REGISTRATION: https://clinicaltrials.gov/ct2/show/NCT04039178, identifier: NCT04039178.}, }
@article {pmid36450968, year = {2022}, author = {Vansteensel, MJ and Klein, E and van Thiel, G and Gaytant, M and Simmons, Z and Wolpaw, JR and Vaughan, TM}, title = {Towards clinical application of implantable brain-computer interfaces for people with late-stage ALS: medical and ethical considerations.}, journal = {Journal of neurology}, volume = {}, number = {}, pages = {}, pmid = {36450968}, issn = {1432-1459}, abstract = {Individuals with amyotrophic lateral sclerosis (ALS) frequently develop speech and communication problems in the course of their disease. Currently available augmentative and alternative communication technologies do not present a solution for many people with advanced ALS, because these devices depend on residual and reliable motor activity. Brain-computer interfaces (BCIs) use neural signals for computer control and may allow people with late-stage ALS to communicate even when conventional technology falls short. Recent years have witnessed fast progression in the development and validation of implanted BCIs, which place neural signal recording electrodes in or on the cortex. Eventual widespread clinical application of implanted BCIs as an assistive communication technology for people with ALS will have significant consequences for their daily life, as well as for the clinical management of the disease, among others because of the potential interaction between the BCI and other procedures people with ALS undergo, such as tracheostomy. This article aims to facilitate responsible real-world implementation of implanted BCIs. We review the state of the art of research on implanted BCIs for communication, as well as the medical and ethical implications of the clinical application of this technology. We conclude that the contribution of all BCI stakeholders, including clinicians of the various ALS-related disciplines, will be needed to develop procedures for, and shape the process of, the responsible clinical application of implanted BCIs.}, }
@article {pmid36450871, year = {2022}, author = {Kaushik, P and Moye, A and Vugt, MV and Roy, PP}, title = {Decoding the cognitive states of attention and distraction in a real-life setting using EEG.}, journal = {Scientific reports}, volume = {12}, number = {1}, pages = {20649}, pmid = {36450871}, issn = {2045-2322}, mesh = {Humans ; Animals ; Electroencephalography ; *Brain-Computer Interfaces ; *Automobile Driving ; *Gastropoda ; Attention ; Cognition ; }, abstract = {Lapses in attention can have serious consequences in situations such as driving a car, hence there is considerable interest in tracking it using neural measures. However, as most of these studies have been done in highly controlled and artificial laboratory settings, we want to explore whether it is also possible to determine attention and distraction using electroencephalogram (EEG) data collected in a natural setting using machine/deep learning. 24 participants volunteered for the study. Data were collected from pairs of participants simultaneously while they engaged in Tibetan Monastic debate, a practice that is interesting because it is a real-life situation that generates substantial variability in attention states. We found that attention was on average associated with increased left frontal alpha, increased left parietal theta, and decreased central delta compared to distraction. In an attempt to predict attention and distraction, we found that a Long Short Term Memory model classified attention and distraction with maximum accuracy of 95.86% and 95.4% corresponding to delta and theta waves respectively. This study demonstrates that EEG data collected in a real-life setting can be used to predict attention states in participants with good accuracy, opening doors for developing Brain-Computer Interfaces that track attention in real-time using data extracted in daily life settings, rendering them much more usable.}, }
@article {pmid36447267, year = {2022}, author = {Li, Y and Qu, T and Li, D and Jing, J and Deng, Q and Wan, X}, title = {Human herpesvirus 7 encephalitis in an immunocompetent adult and a literature review.}, journal = {Virology journal}, volume = {19}, number = {1}, pages = {200}, pmid = {36447267}, issn = {1743-422X}, mesh = {Adult ; Child ; Humans ; *Herpesvirus 7, Human/genetics ; *Encephalitis, Herpes Simplex ; *Roseolovirus Infections/complications/diagnosis ; Electroencephalography ; High-Throughput Nucleotide Sequencing ; }, abstract = {BACKGROUND: Human herpesvirus 7 (HHV-7) is a common virus that infects children early and is accompanied by lifelong latency in cells, which is easy to reactivate in immunodeficient adults, but the underlying pathological mechanism is uncertain in immunocompetent adults without peculiar past medical history. Even though the clinical manifestation of the encephalitis caused by HHV-7 is uncommon in immunocompetent adults, the HHV-7 infection should not be neglected for encephalitis for unknown reasons.<br><br>CASE PRESENTATION: We reported here a case of HHV-7 encephalitis with epileptic seizures. While the brain computer tomography was standard, electroencephalography displayed slow waves in the temporal and bilateral frontal areas, then HHV-7 DNA was detected in the metagenomic next-generation sequencing of cerebrospinal fluid. Fortunately, the patient recovered after treatment and was discharged 2&#xa0;months later. We also collected the related cases and explored a better way to illuminate the underlying mechanism.<br><br>CONCLUSION: The case indicates clinicians should memorize HHV-7 as an unusual etiology of encephalitis to make an early diagnosis and therapy.}, }
@article {pmid36446933, year = {2022}, author = {Yu, XD and Zhu, Y and Sun, QX and Deng, F and Wan, J and Zheng, D and Gong, W and Xie, SZ and Shen, CJ and Fu, JY and Huang, H and Lai, HY and Jin, J and Li, Y and Li, XM}, title = {Distinct serotonergic pathways to the amygdala underlie separate behavioral features of anxiety.}, journal = {Nature neuroscience}, volume = {25}, number = {12}, pages = {1651-1663}, pmid = {36446933}, issn = {1546-1726}, mesh = {Animals ; Mice ; Amygdala ; Anxiety ; *Anxiety Disorders ; *Basolateral Nuclear Complex ; Receptors, GABA-B ; *Serotonin ; }, abstract = {Anxiety-like behaviors in mice include social avoidance and avoidance of bright spaces. Whether these features are distinctly regulated is unclear. We demonstrate that in mice, social and anxiogenic stimuli, respectively, increase and decrease serotonin (5-HT) levels in basal amygdala (BA). In dorsal raphe nucleus (DRN), 5-HT∩vGluT3 neurons projecting to BA parvalbumin (DRN[5-HT∩vGluT3]-BA[PV]) and pyramidal (DRN[5-HT∩vGluT3]-BA[Pyr]) neurons have distinct intrinsic properties and gene expression and respond to anxiogenic and social stimuli, respectively. Activation of DRN[5-HT∩vGluT3]→BA[PV] inhibits 5-HT release via GABAB receptors on serotonergic terminals in BA, inducing social avoidance and avoidance of bright spaces. Activation of DRN[5-HT∩vGluT3]→BA neurons inhibits two subsets of BA[Pyr] neurons via 5-HT1A receptors (HTR1A) and 5-HT1B receptors (HTR1B). Pharmacological inhibition of HTR1A and HTR1B in BA induces avoidance of bright spaces and social avoidance, respectively. These findings highlight the functional significance of heterogenic inputs from DRN to BA subpopulations in the regulation of separate anxiety-related behaviors.}, }
@article {pmid36446797, year = {2022}, author = {Nicolelis, MAL and Alho, EJL and Donati, ARC and Yonamine, S and Aratanha, MA and Bao, G and Campos, DSF and Almeida, S and Fischer, D and Shokur, S}, title = {Training with noninvasive brain-machine interface, tactile feedback, and locomotion to enhance neurological recovery in individuals with complete paraplegia: a randomized pilot study.}, journal = {Scientific reports}, volume = {12}, number = {1}, pages = {20545}, pmid = {36446797}, issn = {2045-2322}, mesh = {Adult ; Male ; Humans ; Feedback ; *Brain-Computer Interfaces ; Pilot Projects ; Brazil ; Paraplegia ; Locomotion ; *Spinal Cord Injuries/therapy ; }, abstract = {In recent years, our group and others have reported multiple cases of consistent neurological recovery in people with spinal cord injury (SCI) following a protocol that integrates locomotion training with brain machine interfaces (BMI). The primary objective of this pilot study was to compare the neurological outcomes (motor, tactile, nociception, proprioception, and vibration) in both an intensive assisted locomotion training (LOC) and a neurorehabilitation protocol integrating assisted locomotion with a noninvasive brain-machine interface (L + BMI), virtual reality, and tactile feedback. We also investigated whether individuals with chronic-complete SCI could learn to perform leg motor imagery. We ran a parallel two-arm randomized pilot study; the experiments took place in São Paulo, Brazil. Eight adults sensorimotor-complete (AIS A) (all male) with chronic (> 6 months) traumatic spinal SCI participated in the protocol that was organized in two blocks of 14 weeks of training and an 8-week follow-up. The participants were allocated to either the LOC group (n = 4) or L + BMI group (n = 4) using block randomization (blinded outcome assessment). We show three important results: (i) locomotion training alone can induce some level of neurological recovery in sensorimotor-complete SCI, and (ii) the recovery rate is enhanced when such locomotion training is associated with BMI and tactile feedback (∆Mean Lower Extremity Motor score improvement for LOC = + 2.5, L + B = + 3.5; ∆Pinprick score: LOC = + 3.75, L + B = + 4.75 and ∆Tactile score LOC = + 4.75, L + B = + 9.5). (iii) Furthermore, we report that the BMI classifier accuracy was significantly above the chance level for all participants in L + B group. Our study shows potential for sensory and motor improvement in individuals with chronic complete SCI following a protocol with BMIs and locomotion therapy. We report no dropouts nor adverse events in both subgroups participating in the study, opening the possibility for a more definitive clinical trial with a larger cohort of people with SCI.Trial registration: http://www.ensaiosclinicos.gov.br/ identifier RBR-2pb8gq.}, }
@article {pmid36444397, year = {2022}, author = {Heubel-Moenen, FCJI and Ansems, LEM and Verhezen, PWM and Wetzels, RJH and van Oerle, RGM and Straat, RJMHE and Megy, K and Downes, K and Henskens, YMC and Beckers, EAM and Joore, MA}, title = {Effectiveness and costs of a stepwise versus an all-in-one approach to diagnose mild bleeding disorders.}, journal = {British journal of haematology}, volume = {}, number = {}, pages = {}, doi = {10.1111/bjh.18570}, pmid = {36444397}, issn = {1365-2141}, abstract = {The diagnostic work-up of patients referred to the haematologist for bleeding evaluation is performed in a stepwise way: bleeding history and results of screening laboratory tests guide further diagnostic evaluation. This can be ineffective, time-consuming and burdensome for patients. To improve this strategy, the initial laboratory investigation can be extended. In a model-based approach, effectiveness and costs of a conventional stepwise versus a newly proposed all-in-one diagnostic approach for bleeding evaluation were evaluated and compared, using data from an observational patient cohort study, including adult patients referred for bleeding evaluation. In the all-in-one approach, specialized platelet function tests, coagulation factors, and fibrinolysis tests were included in the initial investigation. Final diagnosis, hospital resource use and costs and patient burden were compared. A total of 150 patients were included. Compared to the stepwise approach, in the all-in-one approach, 19 additional patients reached a diagnosis and patient burden was lower, but total costs per patient were higher [€359, 95% bootstrapped confidence interval (BCI) 283-518, p = 0.001]. For bleeding evaluation of patients referred to the haematologist, an all-in-one diagnostic approach has a higher diagnostic yield and reduces patient burden, at a higher cost. This raises the question what costs justify the diagnosis of a bleeding disorder and a less burdensome diagnostic strategy.}, }
@article {pmid36441876, year = {2022}, author = {Strypsteen, T and BertrandSenior Member, A}, title = {Bandwidth-efficient distributed neural network architectures with application to neuro-sensor networks.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2022.3225019}, pmid = {36441876}, issn = {2168-2208}, abstract = {In this paper, we describe a conceptual design methodology to design distributed neural network architectures that can perform efficient inference within sensor networks with communication bandwidth constraints. The different sensor channels are distributed across multiple sensor devices, which have to exchange data over bandwidth-limited communication channels to solve a classification task. Our design methodology starts from a user-defined centralized neural network and transforms it into a distributed architecture in which the channels are distributed over different nodes. The distributed network consists of two parallel branches, whose outputs are fused at the fusion center. The first branch collects classification results from local, node-specific classifiers while the second branch compresses each node's signal and then reconstructs the multi-channel time series for classification at the fusion center. We further improve bandwidth gains by dynamically activating the compression path when the local classifications do not suffice. We validate this method on a motor execution task in an emulated EEG sensor network and analyze the resulting bandwidth-accuracy trade-offs. Our experiments show that the proposed framework enables up to a factor 20 in bandwidth reduction and factor 9 in power reduction with minimal loss (up to 2%) in classification accuracy compared to the centralized baseline on the demonstrated task. The proposed method offers a way to smoothly transform a centralized architecture to a distributed, bandwidth-efficient network amenable for low-power sensor networks. While the application focus of this paper is on wearable brain-computer interfaces, the proposed methodology can be applied in other sensor network-like applications as well.}, }
@article {pmid36441469, year = {2022}, author = {Wood, CR and Xi, Y and Yang, WJ and Wang, H}, title = {Insight into Neuroethical Considerations of the Newly Emerging Technologies and Techniques of the Global Brain Initiatives.}, journal = {Neuroscience bulletin}, volume = {}, number = {}, pages = {}, pmid = {36441469}, issn = {1995-8218}, }
@article {pmid36440598, year = {2022}, author = {Kong, X and Shu, X and Wang, J and Liu, D and Ni, Y and Zhao, W and Wang, L and Gao, Z and Chen, J and Yang, B and Guo, X and Wang, Z}, title = {Fine-tuning of mTOR signaling by the UBE4B-KLHL22 E3 ubiquitin ligase cascade in brain development.}, journal = {Development (Cambridge, England)}, volume = {}, number = {}, pages = {}, doi = {10.1242/dev.201286}, pmid = {36440598}, issn = {1477-9129}, abstract = {Spatiotemporal regulation of the mechanistic target of rapamycin (mTOR) pathway is pivotal for establishment of brain architecture. Dysregulation of mTOR signaling is associated with a variety of neurodevelopmental disorders (NDDs). Here, we discover that the UBE4B-KLHL22 E3 ubiquitin ligase cascade regulates mTOR activity in neurodevelopment. In a mouse model with UBE4B conditionally deleted in the nervous system, animals display severe growth defects, spontaneous seizures, and premature death. Loss of UBE4B in the brains of mutant mice results in depletion of neural precursor cells (NPCs) and impairment of neurogenesis. Mechanistically, UBE4B polyubiquitinates and degrades KLHL22, an E3 ligase previously shown to degrade the GATOR1 component DEPDC5. Deletion of UBE4B causes upregulation of KLHL22 and hyperactivation of mTOR, leading to defective proliferation and differentiation of NPCs. Suppression of KLHL22 expression reverses the elevated activity of mTOR caused by acute local deletion of UBE4B. Prenatal treatment with the mTOR inhibitor rapamycin rescues neurogenesis defects in Ube4b mutant mice. Taken together, these findings demonstrate that UBE4B and KLHL22 are essential for maintenance and differentiation of the precursor pool through fine-tuning of mTOR activity.}, }
@article {pmid36438642, year = {2022}, author = {Sisti, HM and Beebe, A and Bishop, M and Gabrielsson, E}, title = {A brief review of motor imagery and bimanual coordination.}, journal = {Frontiers in human neuroscience}, volume = {16}, number = {}, pages = {1037410}, pmid = {36438642}, issn = {1662-5161}, abstract = {Motor imagery is increasingly being used in clinical settings, such as in neurorehabilitation and brain computer interface (BCI). In stroke, patients lose upper limb function and must re-learn bimanual coordination skills necessary for the activities of daily living. Physiotherapists integrate motor imagery with physical rehabilitation to accelerate recovery. In BCIs, users are often asked to imagine a movement, often with sparse instructions. The EEG pattern that coincides with this cognitive task is captured, then used to execute an external command, such as operating a neuroprosthetic device. As such, BCIs are dependent on the efficient and reliable interpretation of motor imagery. While motor imagery improves patient outcome and informs BCI research, the cognitive and neurophysiological mechanisms which underlie it are not clear. Certain types of motor imagery techniques are more effective than others. For instance, focusing on kinesthetic cues and adopting a first-person perspective are more effective than focusing on visual cues and adopting a third-person perspective. As motor imagery becomes more dominant in neurorehabilitation and BCIs, it is important to elucidate what makes these techniques effective. The purpose of this review is to examine the research to date that focuses on both motor imagery and bimanual coordination. An assessment of current research on these two themes may serve as a useful platform for scientists and clinicians seeking to use motor imagery to help improve bimanual coordination, either through augmenting physical therapy or developing more effective BCIs.}, }
@article {pmid36437049, year = {2022}, author = {Robinson, DA and Foster, ME and Bennett, CH and Bhandarkar, A and Webster, ER and Celebi, A and Celebi, N and Fuller, EJ and Stavila, V and Spataru, CD and Ashby, DS and Marinella, MJ and Krishnakumar, R and Allendorf, MD and Talin, AA}, title = {Tunable Intervalence Charge Transfer in Ruthenium Prussian Blue Analogue Enables Stable and Efficient Biocompatible Artificial Synapses.}, journal = {Advanced materials (Deerfield Beach, Fla.)}, volume = {}, number = {}, pages = {e2207595}, doi = {10.1002/adma.202207595}, pmid = {36437049}, issn = {1521-4095}, abstract = {Emerging concepts for neuromorphic computing, bioelectronics, and brain-computer interfacing inspire new research avenues aimed at understanding the relationship between oxidation state and conductivity in unexplored materials. This report expands the materials playground for neuromorphic devices to include a mixed valence inorganic 3D coordination framework, a ruthenium Prussian blue analogue (RuPBA), for flexible and biocompatible artificial synapses that reversibly switch conductance by more than four orders of magnitude based on electrochemically tunable oxidation state. The electrochemically tunable degree of mixed valency and electronic coupling between N-coordinated Ru sites controls the carrier concentration and mobility, as supported by density functional theory (DFT) computations and application of electron transfer theory to in-situ spectroscopy of intervalence charge transfer. Retention of programmed states is improved by nearly two orders of magnitude compared to extensively studied organic polymers, thus reducing the frequency, complexity and energy costs associated with error correction schemes. This report demonstrates dopamine-mediated plasticity of RuPBA synapses and biocompatibility of RuPBA with neuronal cells, evoking prospective application for brain-computer interfacing. This article is protected by copyright. All rights reserved.}, }
@article {pmid36433362, year = {2022}, author = {Wang, H and Zhu, C and Jin, W and Tang, J and Wu, Z and Chen, K and Hong, H}, title = {A Linear-Power-Regulated Wireless Power Transfer Method for Decreasing the Heat Dissipation of Fully Implantable Microsystems.}, journal = {Sensors (Basel, Switzerland)}, volume = {22}, number = {22}, pages = {}, pmid = {36433362}, issn = {1424-8220}, mesh = {*Wireless Technology ; *Hot Temperature ; Prostheses and Implants ; Electric Impedance ; Body Temperature Regulation ; }, abstract = {Magnetic coupling resonance wireless power transfer can efficiently provide energy to intracranial implants under safety constraints, and is the main way to power fully implantable brain-computer interface systems. However, the existing maximum efficiency tracking wireless power transfer system is aimed at optimizing the overall system efficiency, but the efficiency of the secondary side is not optimized. Moreover, the parameters of the transmitter and the receiver change nonlinearly in the power control process, and the efficiency tracking mainly depends on wireless communication. The heat dissipation caused by the unoptimized receiver efficiency and the wireless communication delay in power control will inevitably affect neural activity and even cause damage, thus affecting the results of neuroscience research. Here, a linear-power-regulated wireless power transfer method is proposed to realize the linear change of the received power regulation and optimize the receiver efficiency, and a miniaturized linear-power-regulated wireless power transfer system is developed. With the received power control, the efficiency of the receiver is increased to more than 80%, which can significantly reduce the heating of fully implantable microsystems. The linear change of the received power regulation makes the reflected impedance in the transmitter change linearly, which will help to reduce the dependence on wireless communication and improve biological safety in received power control applications.}, }
@article {pmid36429765, year = {2022}, author = {Chang, D and Xiang, Y and Zhao, J and Qian, Y and Li, F}, title = {Exploration of Brain-Computer Interaction for Supporting Children's Attention Training: A Multimodal Design Based on Attention Network and Gamification Design.}, journal = {International journal of environmental research and public health}, volume = {19}, number = {22}, pages = {}, pmid = {36429765}, issn = {1660-4601}, mesh = {Child ; Humans ; *Brain-Computer Interfaces ; Gamification ; Brain ; Cognition ; Computers ; }, abstract = {Recent developments in brain-computer interface (BCI) technology have shown great potential in terms of estimating users' mental state and supporting children's attention training. However, existing training tasks are relatively simple and lack a reliable task-generation process. Moreover, the training experience has not been deeply studied, and the empirical validation of the training effect is still insufficient. This study thusly proposed a BCI training system for children's attention improvement. In particular, to achieve a systematic training process, the attention network was referred to generate the training games for alerting, orienting and executive attentions, and to improve the training experience and adherence, the gamification design theory was introduced to derive attractive training tasks. A preliminary experiment was conducted to set and modify the training parameters. Subsequently, a series of contrasting user experiments were organized to examine the impact of BCI training. To test the training effect of the proposed system, a hypothesis-testing approach was adopted. The results revealed that the proposed BCI gamification attention training system can significantly improve the participants' attention behaviors and concentration ability. Moreover, an immersive, inspiring and smooth training process can be created, and a pleasant user experience can be achieved. Generally, this work is promising in terms of providing a valuable reference for related practices, especially for how to generate BCI attention training tasks using attention networks and how to improve training adherence by integrating multimodal gamification elements.}, }
@article {pmid36428885, year = {2022}, author = {Shovon, MSH and Islam, MJ and Nabil, MNAK and Molla, MM and Jony, AI and Mridha, MF}, title = {Strategies for Enhancing the Multi-Stage Classification Performances of HER2 Breast Cancer from Hematoxylin and Eosin Images.}, journal = {Diagnostics (Basel, Switzerland)}, volume = {12}, number = {11}, pages = {}, pmid = {36428885}, issn = {2075-4418}, abstract = {Breast cancer is a significant health concern among women. Prompt diagnosis can diminish the mortality rate and direct patients to take steps for cancer treatment. Recently, deep learning has been employed to diagnose breast cancer in the context of digital pathology. To help in this area, a transfer learning-based model called 'HE-HER2Net' has been proposed to diagnose multiple stages of HER2 breast cancer (HER2-0, HER2-1+, HER2-2+, HER2-3+) on H&amp;E (hematoxylin &amp; eosin) images from the BCI dataset. HE-HER2Net is the modified version of the Xception model, which is additionally comprised of global average pooling, several batch normalization layers, dropout layers, and dense layers with a swish activation function. This proposed model exceeds all existing models in terms of accuracy (0.87), precision (0.88), recall (0.86), and AUC score (0.98) immensely. In addition, our proposed model has been explained through a class-discriminative localization technique using Grad-CAM to build trust and to make the model more transparent. Finally, nuclei segmentation has been performed through the StarDist method.}, }
@article {pmid36428289, year = {2022}, author = {Si, C and Qin, H and Chuanzhuang, Y and Wei, T and Lin, X}, title = {Study of event-related potentials by withdrawal friction on the fingertip.}, journal = {Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI)}, volume = {}, number = {}, pages = {e13232}, doi = {10.1111/srt.13232}, pmid = {36428289}, issn = {1600-0846}, abstract = {OBJECTIVES: The lack of understanding about the brain's reaction processes in perceiving touch and separation between skin and object surfaces is a barrier to the development of existing brain-computer interface technologies and virtual haptics. These technologies are limited in their ability to advance. It leaves prosthesis users with a limited amount of tactile information that they can feel. This study aims to determine whether distinct surface aspects of various items trigger different reactions from the brain when friction is removed from the surface.<br><br>METHODS: When friction is suddenly removed from the surface of an item, a technique called event-related potential, (ERP) is used to study the features of people's EEGs. It is done after the subject has actively explored the object's surface. A 64-channels EEG collecting system was utilized to acquire EEG data from the individuals. [Corrections added on 5 December 2022, after first online publication: The preceding sentence has been updated.] The event-related potentials for friction removal were generated using the Oddball paradigm, and the samples consisted of sandpaper with three distinct degrees of roughness. We utilized a total of 20 participants, 10 of whom were male, and 10 of whom were female, with a mean age of 21 years.<br><br>RESULTS: It was discovered that the P3 component of event-related potentials, which is essential for cognition, was noticeably absent in the friction withdrawal response for various roughnesses. It was the case regardless of whether the surface was smooth or rough. Moreover, there was no statistically significant difference between the P1 andP2 components, which suggests that the brain could not recognize the surface properties of objects with varying roughness as the friction withdrawal was being performed.<br><br>CONCLUSIONS: It has been demonstrated that tactile recognition does not occur after friction withdrawal. The findings of this paper could have significant repercussions for future research involving the study of haptic perception and brain-computer interaction in prosthetic hands. It is a step toward future research on the mechanisms underlying human tactile perception, so think of it as preparation.}, }
@article {pmid36427669, year = {2023}, author = {Deng, X and Wang, Z and Liu, K and Xiang, X}, title = {A GAN model encoded by CapsEEGNet for visual EEG encoding and image reproduction.}, journal = {Journal of neuroscience methods}, volume = {384}, number = {}, pages = {109747}, doi = {10.1016/j.jneumeth.2022.109747}, pmid = {36427669}, issn = {1872-678X}, abstract = {In last few decades, reading the human mind is an innovative topic in scientific research. Recent studies in neuroscience indicate that it is possible to decode the signals of the human brain based on the neuroimaging data. The work in this paper explores the possibility of building an end-to-end BCI system to learn and visualize the brain thoughts evoked by the stimulating images. To achieve this goal, it designs an experiment to collect the EEG signals evoked by randomly presented images. Based on these data, this work analyzes and compares the classification abilities by several improved methods, including the Transformer, CapsNet and the ensemble strategies. After obtaining the optimal method to be the encoder, this paper proposes a distribution-to-distribution mapping network to transform an encoded latent feature vector into a prior image feature vector. To visualize the brain thoughts, a pretrained IC-GAN model is used to receive these image feature vectors and generate images. Extensive experiments are carried out and the results show that the proposed method can effectively deal with the small sample data original from the less electrode channels. By examining the generated images coming from the EEG signals, it verifies that the proposed model is capable of reproducing the images seen by human eyes to some extent.}, }
@article {pmid36426541, year = {2022}, author = {Colucci, A and Vermehren, M and Cavallo, A and Angerhöfer, C and Peekhaus, N and Zollo, L and Kim, WS and Paik, NJ and Soekadar, SR}, title = {Brain-Computer Interface-Controlled Exoskeletons in Clinical Neurorehabilitation: Ready or Not?.}, journal = {Neurorehabilitation and neural repair}, volume = {36}, number = {12}, pages = {747-756}, pmid = {36426541}, issn = {1552-6844}, mesh = {Humans ; *Exoskeleton Device ; *Brain-Computer Interfaces ; *Neurological Rehabilitation ; Brain ; *Robotics ; }, abstract = {The development of brain-computer interface-controlled exoskeletons promises new treatment strategies for neurorehabilitation after stroke or spinal cord injury. By converting brain/neural activity into control signals of wearable actuators, brain/neural exoskeletons (B/NEs) enable the execution of movements despite impaired motor function. Beyond the use as assistive devices, it was shown that-upon repeated use over several weeks-B/NEs can trigger motor recovery, even in chronic paralysis. Recent development of lightweight robotic actuators, comfortable and portable real-world brain recordings, as well as reliable brain/neural control strategies have paved the way for B/NEs to enter clinical care. Although B/NEs are now technically ready for broader clinical use, their promotion will critically depend on early adopters, for example, research-oriented physiotherapists or clinicians who are open for innovation. Data collected by early adopters will further elucidate the underlying mechanisms of B/NE-triggered motor recovery and play a key role in increasing efficacy of personalized treatment strategies. Moreover, early adopters will provide indispensable feedback to the manufacturers necessary to further improve robustness, applicability, and adoption of B/NEs into existing therapy plans.}, }
@article {pmid36423320, year = {2022}, author = {Chen, YF and Fu, R and Wu, J and Song, J and Ma, R and Jiang, YC and Zhang, M}, title = {Continuous Bimanual Trajectory Decoding of Coordinated Movement from EEG Signals.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2022.3224506}, pmid = {36423320}, issn = {2168-2208}, abstract = {While many voluntary movements involve bimanual coordination, few attempts have been made to simultaneously decode the trajectory of bimanual movements from electroencephalogram (EEG) signals. In this study, we proposed a novel bimanual brain-computer interface (BCI) paradigm to reconstruct the continuous trajectory of both hands during coordinated movements from EEG. The protocol required human subjects to complete a bimanual reaching task to the left, middle, or right target while EEG data were collected. A multi-task deep learning model combining the EEGNet and long short-term memory network (LSTM) was proposed to decode bimanual trajectories, including position and velocity. Decoding performance was evaluated in terms of the correlation coefficient (CC) and normalized root mean square error (NRMSE) between decoded and real trajectories. Experimental results from 13 human subjects showed that the grand-averaged combined CC values achieved 0.54 and 0.42 for position and velocity decoding, respectively. The corresponding combined NRMSE values were 0.22 and 0.23. Both CC and NRMSE were significantly superior to the chance level (p<0.05). Comparative experiments also indicated that the proposed model significantly outperformed some other commonly-used methods in terms of CC and NRMSE for continuous trajectory decoding. These findings demonstrated the feasibility of simultaneously decoding bimanual trajectory from EEG, indicating the potential of bimanual control for coordinated tasks.}, }
@article {pmid36422533, year = {2022}, author = {Diao, X and Luo, D and Wang, D and Lai, J and Li, Q and Zhang, P and Huang, H and Wu, L and Lu, S and Hu, S}, title = {Lurasidone versus Quetiapine for Cognitive Impairments in Young Patients with Bipolar Depression: A Randomized, Controlled Study.}, journal = {Pharmaceuticals (Basel, Switzerland)}, volume = {15}, number = {11}, pages = {}, pmid = {36422533}, issn = {1424-8247}, abstract = {The clinical efficacy of lurasidone and quetiapine, two commonly prescribed atypical antipsychotics for bipolar depression, has been inadequately studied in young patients. In this randomized and controlled study, we aimed to compare the effects of these two drugs on cognitive function, emotional status, and metabolic profiles in children and adolescents with bipolar depression. We recruited young participants (aged 10-17 years old) with a DSM-5 diagnosis of bipolar disorder during a depressive episode, who were then randomly assigned to two groups and treated with flexible doses of lurasidone (60 to 120 mg/day) or quetiapine (300 to 600 mg/day) for consecutive 8 weeks, respectively. All the participants were clinically evaluated on cognitive function using the THINC-it instrument at baseline and week 8, and emotional status was assessed at baseline and the end of week 2, 4, and 8. Additionally, the changes in weight and serum metabolic profiles (triglyceride, cholesterol, and fasting blood glucose) during the trial were also analyzed. In results, a total of 71 patients were randomly assigned to the lurasidone group (n = 35) or the quetiapine group (n = 36), of which 31 patients completed the whole treatment course. After an 8-week follow-up, participants in the lurasidone group showed better performance in the Symbol Check Reaction and Accuracy Tests, when compared to those in the quetiapine group. No inter-group difference was observed in the depression scores, response rate, or remission rate throughout the trial. In addition, there was no significant difference in serum metabolic profiles between the lurasidone group and the quetiapine group, including triglyceride level, cholesterol level, and fasting blood glucose level. However, the quetiapine group presented a more apparent change in body weight than the lurasidone group. In conclusion, the present study provided preliminary evidence that quetiapine and lurasidone had an equivalent anti-depressive effect, and lurasidone appeared to be superior to quetiapine in improving the cognitive function of young patients with bipolar depression.}, }
@article {pmid36421880, year = {2022}, author = {Li, J and Huang, B and Wang, F and Xie, Q and Xu, C and Huang, H and Pan, J}, title = {A Potential Prognosis Indicator Based on P300 Brain-Computer Interface for Patients with Disorder of Consciousness.}, journal = {Brain sciences}, volume = {12}, number = {11}, pages = {}, pmid = {36421880}, issn = {2076-3425}, abstract = {For patients with disorders of consciousness, such as unresponsive wakefulness syndrome (UWS) patients and minimally conscious state (MCS) patients, their long treatment cycle and high cost commonly put a heavy burden on the patient's family and society. Therefore, it is vital to accurately diagnose and predict consciousness recovery for such patients. In this paper, we explored the role of the P300 signal based on an audiovisual BCI in the classification and prognosis prediction of patients with disorders of consciousness. This experiment included 18 patients: 10 UWS patients and 8 MCS- patients. At the three-month follow-up, we defined patients with an improved prognosis (from UWS to MCS-, from UWS to MCS+, or from MCS- to MCS+) as "improved patients" and those who stayed in UWS/MCS as "not improved patients". First, we compared and analyzed different types of patients, and the results showed that the P300 detection accuracy rate of "improved" patients was significantly higher than that of "not improved" patients. Furthermore, the P300 detection accuracy of traumatic brain injury (TBI) patients was significantly higher than that of non-traumatic brain injury (NTBI, including acquired brain injury and cerebrovascular disease) patients. We also found that there was a positive linear correlation between P300 detection accuracy and CRS-R score, and patients with higher P300 detection accuracy were likely to achieve higher CRS-R scores. In addition, we found that the patients with higher P300 detection accuracies tend to have better prognosis in this audiovisual BCI. These findings indicate that the detection accuracy of P300 is significantly correlated with the level of consciousness, etiology, and prognosis of patients. P300 can be used to represent the preservation level of consciousness in clinical neurophysiology and predict the possibility of recovery in patients with disorders of consciousness.}, }
@article {pmid36421877, year = {2022}, author = {Zavala Hernández, JG and Barbosa-Santillán, LI}, title = {Virtual Intelligence: A Systematic Review of the Development of Neural Networks in Brain Simulation Units.}, journal = {Brain sciences}, volume = {12}, number = {11}, pages = {}, pmid = {36421877}, issn = {2076-3425}, abstract = {The functioning of the brain has been a complex and enigmatic phenomenon. From the first approaches made by Descartes about this organism as the vehicle of the mind to contemporary studies that consider the brain as an organism with emergent activities of primary and higher order, this organism has been the object of continuous exploration. It has been possible to develop a more profound study of brain functions through imaging techniques, the implementation of digital platforms or simulators through different programming languages and the use of multiple processors to emulate the speed at which synaptic processes are executed in the brain. The use of various computational architectures raises innumerable questions about the possible scope of disciplines such as computational neurosciences in the study of the brain and the possibility of deep knowledge into different devices with the support that information technology (IT) brings. One of the main interests of cognitive science is the opportunity to develop human intelligence in a system or mechanism. This paper takes the principal articles of three databases oriented to computational sciences (EbscoHost Web, IEEE Xplore and Compendex Engineering Village) to understand the current objectives of neural networks in studying the brain. The possible use of this kind of technology is to develop artificial intelligence (AI) systems that can replicate more complex human brain tasks (such as those involving consciousness). The results show the principal findings in research and topics in developing studies about neural networks in computational neurosciences. One of the principal developments is the use of neural networks as the basis of much computational architecture using multiple techniques such as computational neuromorphic chips, MRI images and brain-computer interfaces (BCI) to enhance the capacity to simulate brain activities. This article aims to review and analyze those studies carried out on the development of different computational architectures that focus on affecting various brain activities through neural networks. The aim is to determine the orientation and the main lines of research on this topic and work in routes that allow interdisciplinary collaboration.}, }
@article {pmid36419166, year = {2022}, author = {Jervis-Rademeyer, H and Ong, K and Djuric, A and Munce, S and Musselman, KE and Marquez-Chin, C}, title = {Therapists' perspectives on using brain-computer interface-triggered functional electrical stimulation therapy for individuals living with upper extremity paralysis: a qualitative case series study.}, journal = {Journal of neuroengineering and rehabilitation}, volume = {19}, number = {1}, pages = {127}, pmid = {36419166}, issn = {1743-0003}, support = {//CIHR/Canada ; }, mesh = {Humans ; *Brain-Computer Interfaces ; *Electric Stimulation Therapy ; Paralysis ; Qualitative Research ; Upper Extremity ; }, abstract = {BACKGROUND: Brain computer interface-triggered functional electrical stimulation therapy (BCI-FEST) has shown promise as a therapy to improve upper extremity function for individuals who have had a stroke or spinal cord injury. The next step is to determine whether BCI-FEST could be used clinically as part of broader therapy practice. To do this, we need to understand therapists' opinions on using the BCI-FEST and what limitations potentially exist. Therefore, we conducted a qualitative exploratory study to understand the perspectives of therapists on their experiences delivering BCI-FEST and the feasibility of large-scale clinical implementation.<br><br>METHODS: Semi-structured interviews were conducted with physical therapists (PTs) and occupational therapists (OTs) who have delivered BCI-FEST. Interview questions were developed using the COM-B (Capability, Opportunity, Motivation-Behaviour) model of behaviour change. COM-B components were used to inform deductive content analysis while other subthemes were detected using an inductive approach.<br><br>RESULTS: We interviewed PTs (n&#x2009;=&#x2009;3) and OTs (n&#x2009;=&#x2009;3), with 360 combined hours of experience delivering BCI-FEST. Components and subcomponents of the COM-B determined deductively included: (1) Capability (physical, psychological), (2) Opportunity (physical, social), and (3) Motivation (automatic, reflective). Under each deductive subcomponent, one to two inductive subthemes were identified (n&#x2009;=&#x2009;8). Capability and Motivation were perceived as strengths, and therefore supported therapists' decisions to use BCI-FEST. Under Opportunity, for both subcomponents (physical, social), therapists recognized the need for more support to clinically implement BCI-FEST.<br><br>CONCLUSIONS: We identified facilitating and limiting factors to BCI-FEST delivery in a clinical setting according to clinicians. These factors implied that education, training, a support network or mentors, and restructuring the physical environment (e.g., scheduling) should be targeted as interventions. The results of this study may help to inform future development of new technologies and interventions.}, }
@article {pmid36418525, year = {2022}, author = {Baudry, AS and Vanlemmens, L and Congard, A and Untas, A and Segura-Djezzar, C and Lefeuvre-Plesse, C and Coussy, F and Guiu, S and Frenel, JS and Sauterey, B and Yakimova, S and Christophe, V}, title = {Emotional processes in partners' quality of life at various stages of breast cancer pathway: a longitudinal study.}, journal = {Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation}, volume = {}, number = {}, pages = {}, pmid = {36418525}, issn = {1573-2649}, abstract = {INTRODUCTION: Several studies have shown that emotional competence (EC) impacts cancer adjustment via anxiety and depression symptoms. The objective was to test this model for the quality of life (QoL) of partners: first, the direct effect of partners' EC on their QoL, anxiety and depression symptoms after cancer diagnosis (T1), after chemotherapy (T2) and after radiotherapy (T3); Second, the indirect effects of partners' EC at T1 on their QoL at T2 and T3 through anxiety and depression symptoms.<br><br>METHODS: 192 partners of women with breast cancer completed a questionnaire at T1, T2 and T3 to assess their EC (PEC), anxiety and depression symptoms (HADS) and QoL (Partner-YW-BCI). Partial correlations and regression analyses were performed to test direct and indirect effects of EC on issues.<br><br>RESULTS: EC at T1 predicted fewer anxiety and depression symptoms at each time and all dimensions of QoL, except for career management and financial difficulties. EC showed different significant indirect effects (i.e. via anxiety or depression symptoms) on all sub-dimensions of QoL, except for financial difficulties, according to the step of care pathway (T2 and T3). Anxiety and depression played a different role in the psychological processes that influence QoL.<br><br>CONCLUSION: Findings confirm the importance of taking emotional processes into account in the adjustment of partners, especially regarding their QoL and the support they may provide to patients. It, thus, seems important to integrate EC in future health models and psychosocial interventions focused on partners or caregivers.}, }
@article {pmid36408731, year = {2022}, author = {Kucewicz, MT and Worrell, GA and Axmacher, N}, title = {Direct electrical brain stimulation of human memory: lessons learnt and future perspectives.}, journal = {Brain : a journal of neurology}, volume = {}, number = {}, pages = {}, doi = {10.1093/brain/awac435}, pmid = {36408731}, issn = {1460-2156}, abstract = {Modulation of cognitive functions supporting human declarative memory is one of the grand challenges of neuroscience, and of vast importance for a variety of neuropsychiatric, neurodegenerative and neurodevelopmental diseases. Despite a recent surge of successful attempts at improving performance in a range of memory tasks, the optimal approaches and parameters for memory enhancement have yet to be determined. On a more fundamental level, it remains elusive how delivering electrical current in a given brain area leads to enhanced memory processing. Starting from the local and distal physiological effects on neural populations, the mechanisms of enhanced memory encoding, maintenance, consolidation, or recall in response to direct electrical stimulation are only now being unraveled. With the advent of innovative neurotechnologies for concurrent recording and stimulation intracranially in the human brain, it becomes possible to study both acute and chronic effects of stimulation on memory performance and the underlying neural activities. In this review, we summarize the effects of various invasive stimulation approaches for modulating memory functions. We first outline the challenges that were faced in the initial studies of memory enhancement and the lessons learned. Electrophysiological biomarkers are then reviewed as more objective measures of the stimulation effects than behavioral outcomes. Finally, we classify the various stimulation approaches into continuous and phasic modulation with open or closed loop for responsive stimulation based on analysis of the recorded neural activities. Although the potential advantage of closed-loop responsive stimulation over the classic open-loop approaches is inconclusive, we foresee the emerging results from ongoing longitudinal studies and clinical trials to shed light on both the mechanisms and optimal strategies for improving declarative memory. Adaptive stimulation based on the biomarker analysis over extended periods of time is proposed as a future direction for obtaining lasting effects on memory functions. Chronic tracking and modulation of neural activities intracranially through adaptive stimulation opens tantalizing new avenues to continually monitor and treat memory and cognitive deficits in a range of brain disorders. Brain co-processors created with machine-learning tools and wireless bi-directional connectivity to seamlessly integrate implanted devices with smartphones and cloud computing are poised to enable real-time automated analysis of large data volumes and adaptively tune electrical stimulation based on electrophysiological biomarkers of behavioral states. Next generation implantable devices for high-density recording and stimulation of electrophysiological activities, and technologies for distributed brain-computer interfaces are presented as selected future perspectives for modulating human memory and associated mental processes.}, }
@article {pmid36408095, year = {2022}, author = {Hu, J and Zou, J and Wan, Y and Yao, Q and Dong, P and Li, G and Wu, X and Zhang, L and Liang, D and Zeng, Q and Huang, G}, title = {Rehabilitation of motor function after stroke: A bibliometric analysis of global research from 2004 to 2022.}, journal = {Frontiers in aging neuroscience}, volume = {14}, number = {}, pages = {1024163}, pmid = {36408095}, issn = {1663-4365}, abstract = {BACKGROUND AND AIMS: The mortality rate of stroke has been increasing worldwide. Poststroke somatic dysfunctions are common. Motor function rehabilitation of patients with such somatic dysfunctions enhances the quality of life and has long been the primary practice to achieve functional recovery. In this regard, we aimed to delineate the new trends and frontiers in stroke motor function rehabilitation literature published from 2004 to 2022 using a bibliometric software.<br><br>METHODS: All documents related to stroke rehabilitation and published from 2004 to 2022 were retrieved from the Web of Science Core Collection. Publication output, research categories, countries/institutions, authors/cocited authors, journals/cocited journals, cocited references, and keywords were assessed using VOSviewer v.1.6.15.0 and CiteSpace version 5.8. The cocitation map was plotted according to the analysis results to intuitively observe the research hotspots.<br><br>RESULTS: Overall, 3,302 articles were retrieved from 78 countries or regions and 564 institutions. Over time, the publication outputs increased annually. In terms of national contribution, the United States published the most papers, followed by China, Japan, South Korea, and Canada. Yeungnam University had the most articles among all institutions, followed by Emory University, Fudan University, and National Taiwan University. Jang Sung Ho and Wolf S.L. were the most productive (56 published articles) and influential (cited 1,121 times) authors, respectively. "Effect of constraint-induced movement therapy on upper extremity function 3-9&#x2009;months after stroke: the Extremity Constraint Induced Therapy Evaluation randomized clinical trial" was the most frequently cited reference. Analysis of keywords showed that upper limbs, Fugl-Meyer assessment, electromyography, virtual reality, telerehabilitation, exoskeleton, and brain-computer interface were the research development trends and focus areas for this topic.<br><br>CONCLUSION: Publications regarding motor function rehabilitation following stroke are likely to continuously increase. Research on virtual reality, telemedicine, electroacupuncture, the brain-computer interface, and rehabilitation robots has attracted increasing attention, with these topics becoming the hotspots of present research and the trends of future research.}, }
@article {pmid36408074, year = {2022}, author = {Cui, Z and Li, Y and Huang, S and Wu, X and Fu, X and Liu, F and Wan, X and Wang, X and Zhang, Y and Qiu, H and Chen, F and Yang, P and Zhu, S and Li, J and Chen, W}, title = {BCI system with lower-limb robot improves rehabilitation in spinal cord injury patients through short-term training: a pilot study.}, journal = {Cognitive neurodynamics}, volume = {16}, number = {6}, pages = {1283-1301}, pmid = {36408074}, issn = {1871-4080}, abstract = {UNLABELLED: In the recent years, the increasing applications of brain-computer interface (BCI) in rehabilitation programs have enhanced the chances of functional recovery for patients with neurological disorders. We presented and validated a BCI system with a lower-limb robot for short-term training of patients with spinal cord injury (SCI). The cores of this system included: (1) electroencephalogram (EEG) features related to motor intention reported through experiments and used to drive the robot; (2) a decision tree to determine the training mode provided for patients with different degrees of injuries. Seven SCI patients (one American Spinal Injury Association Impairment Scale (AIS) A, three AIS B, and three AIS C) participated in the short-term training with this system. All patients could learn to use the system rapidly and maintained a high intensity during the training program. The strength of the lower limb key muscles of the patients was improved. Four AIS A/B patients were elevated to AIS C. The cumulative results indicate that clinical application of the BCI system with lower-limb robot is feasible and safe, and has potentially positive effects on SCI patients.<br><br>SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-022-09801-6.}, }
@article {pmid36405787, year = {2022}, author = {Jaipriya, D and Sriharipriya, KC}, title = {A comparative analysis of masking empirical mode decomposition and a neural network with feed-forward and back propagation along with masking empirical mode decomposition to improve the classification performance for a reliable brain-computer interface.}, journal = {Frontiers in computational neuroscience}, volume = {16}, number = {}, pages = {1010770}, pmid = {36405787}, issn = {1662-5188}, abstract = {In general, extraction and classification are used in various fields like image processing, pattern recognition, signal processing, and so on. Extracting effective characteristics from raw electroencephalogram (EEG) signals is a crucial role of the brain-computer interface for motor imagery. Recently, there has been a great deal of focus on motor imagery in the EEG signals since they encode a person's intent to do an action. Researchers have been using MI signals to assist paralyzed people and even move them on their own with certain equipment, like wheelchairs. As a result, proper decoding is an important step required for the interconnection of the brain and the computer. EEG decoding is a challenging process because of poor SNR, complexity, and other reasons. However, choosing an appropriate method to extract the features to improve the performance of motor imagery recognition is still a research hotspot. To extract the features of the EEG signal in the classification task, this paper proposes a Masking Empirical Mode Decomposition (MEMD) based Feed Forward Back Propagation Neural Network (MEMD-FFBPNN). The dataset consists of EEG signals which are first normalized using the minimax method and given as input to the MEMD to extract the features and then given to the FFBPNN to classify the tasks. The accuracy of the proposed method MEMD-FFBPNN has been measured using the confusion matrix, mean square error and which has been recorded up to 99.9%. Thus, the proposed method gives better accuracy than the other conventional methods.}, }
@article {pmid36403238, year = {2022}, author = {Zhang, F and Zhang, L and Xia, J and Zhao, W and Dong, S and Ye, Z and Pan, G and Luo, J and Zhang, S}, title = {Multimodal Electrocorticogram Active Electrode Array Based on Zinc Oxide-Thin Film Transistors.}, journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)}, volume = {}, number = {}, pages = {e2204467}, doi = {10.1002/advs.202204467}, pmid = {36403238}, issn = {2198-3844}, abstract = {Active electrocorticogram (ECoG) electrodes can amplify weak electrophysiological signals and improve anti-interference ability; however, traditional active electrodes are opaque and cannot realize photoelectric collaborative observation. In this study, an active and fully transparent ECoG array based on zinc oxide thin-film transistors (ZnO TFTs) is developed as a local neural signal amplifier for electrophysiological monitoring. The transparency of the proposed ECoG array is up to 85%, which is superior to that of the previously reported active electrode arrays. Various electrical characterizations have demonstrated its ability to record electrophysiological signals with a higher signal-to-noise ratio of 19.9 dB compared to the Au grid (13.2 dB). The high transparency of the ZnO-TFT electrode array allows the concurrent collection of high-quality electrophysiological signals (32.2 dB) under direct optical stimulation of the optogenetic mice brain. The ECoG array can also work under 7-Tesla magnetic resonance imaging to record local brain signals without affecting brain tissue imaging. As the most transparent active ECoG array to date, it provides a powerful multimodal tool for brain observation, including recording brain activity under synchronized optical modulation and 7-Tesla magnetic resonance imaging.}, }
@article {pmid36403143, year = {2022}, author = {Klein, E and Kinsella, M and Stevens, I and Fried-Oken, M}, title = {Ethical issues raised by incorporating personalized language models into brain-computer interface communication technologies: a qualitative study of individuals with neurological disease.}, journal = {Disability and rehabilitation. Assistive technology}, volume = {}, number = {}, pages = {1-11}, doi = {10.1080/17483107.2022.2146217}, pmid = {36403143}, issn = {1748-3115}, abstract = {PURPOSE: To examine the views of individuals with neurodegenerative diseases about ethical issues related to incorporating personalized language models into brain-computer interface (BCI) communication technologies.<br><br>METHODS: Fifteen semi-structured interviews and 51 online free response surveys were completed with individuals diagnosed with neurodegenerative disease that could lead to loss of speech and motor skills. Each participant responded to questions after six hypothetical ethics vignettes were presented that address the possibility of building language models with personal words and phrases in BCI communication technologies. Data were analyzed with consensus coding, using modified grounded theory.<br><br>RESULTS: Four themes were identified. (1) The experience of a neurodegenerative disease shapes preferences for personalized language models. (2) An individual's identity will be affected by the ability to personalize the language model. (3) The motivation for personalization is tied to how relationships can be helped or harmed. (4) Privacy is important to people who may need BCI communication technologies. Responses suggest that the inclusion of personal lexica raises ethical issues. Stakeholders want their values to be considered during development of BCI communication technologies.<br><br>CONCLUSIONS: With the rapid development of BCI communication technologies, it is critical to incorporate feedback from individuals regarding their ethical concerns about the storage and use of personalized language models. Stakeholder values and preferences about disability, privacy, identity and relationships should drive design, innovation and implementation.IMPLICATIONS FOR REHABILITATIONIndividuals with neurodegenerative diseases are important stakeholders to consider in development of natural language processing within brain-computer interface (BCI) communication technologies.The incorporation of personalized language models raises issues related to disability, identity, relationships, and privacy.People who may one day rely on BCI communication technologies care not just about usability of communication technology but about technology that supports their values and priorities.Qualitative ethics-focused research is a valuable tool for exploring stakeholder perspectives on new capabilities of BCI communication technologies, such as the storage and use of personalized language models.}, }
@article {pmid36400152, year = {2022}, author = {Xi, C and Li, A and Lai, J and Huang, X and Zhang, P and Yan, S and Jiao, M and Huang, H and Hu, S}, title = {Brain-gut microbiota multimodal predictive model in patients with bipolar depression.}, journal = {Journal of affective disorders}, volume = {323}, number = {}, pages = {140-152}, doi = {10.1016/j.jad.2022.11.026}, pmid = {36400152}, issn = {1573-2517}, abstract = {BACKGROUND: The "microbiota-gut-brain axis" which bridges the brain and gut microbiota is involved in the pathological mechanisms of bipolar disorder (BD), but rare is known about the exact association patterns and the potential for clinical diagnosis and treatment outcome prediction.<br><br>METHODS: At baseline, fecal samples and resting-state MRI data were collected from 103 BD depression patients and 39 healthy controls (HCs) for metagenomic sequencing and network-based functional connectivity (FC), grey matter volume (GMV) analyses. All patients then received 4-weeks quetiapine treatment and were further classified as responders and non-responders. Based on pre-treatment datasets, the correlation networks were established between gut microbiota and neuroimaging measures and the multimodal kernal combination support vector machine (SVM) classifiers were constructed to distinguish BD patients from HCs, and quetiapine responders from non-responders.<br><br>RESULTS: The multi-modal pre-treatment characteristics of quetiapine responders, were closer to the HCs compared to non-responders. And the correlation network analyses found the substantial correlations existed in HC between the Anaerotruncus_ unclassified&#xff0c;Porphyromonas_asaccharolytica&#xff0c;Actinomyces_graevenitzii et al. and the functional connectomes involved default mode network (DMN),somatomotor (SM), visual, limbic and basal ganglia networks were disrupted in BD. Moreover, in terms of the multimodal classifier, it reached optimized area under curve (AUC-ROC) at 0.9517 when classified BD from HC, and also acquired 0.8292 discriminating quetiapine responders from non-responders, which consistently better than even using the best unique modality.<br><br>LIMITATIONS: Lack post-treatment and external validation datasets; size of HCs is modest.<br><br>CONCLUSIONS: Multi-modalities of combining pre-treatment gut microbiota with neuroimaging endophenotypes might be a superior approach for accurate diagnosis and quetiapine efficacy prediction in BD.}, }
@article {pmid36398685, year = {2022}, author = {Bhuvaneshwari, M and Grace Mary Kanaga, E and George, ST}, title = {Classification of SSVEP-EEG signals using CNN and Red Fox Optimization for BCI applications.}, journal = {Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine}, volume = {}, number = {}, pages = {9544119221135714}, doi = {10.1177/09544119221135714}, pmid = {36398685}, issn = {2041-3033}, abstract = {Classification of electroencephalography (EEG) signals associated with Steady-state visually evoked potential (SSVEP) is prominent because of its potential in restoring the communication and controlling capability of paralytic people. However, SSVEP signals classification is a challenging task for researchers because of its low signal-to-noise ratio, non-stationary and high dimensional properties. A proficient technique has to be evolved to classify the SSVEP-based EEG data. In recent times, convolutional neural network (CNN) has reached a quantum leap in EEG signal classification. Therefore, the proposed system employs CNN to classify the SSVEP-based EEG signals. Though CNN has proved its proficiency in handling EEG signal classification problems, the calibration of hyperparameters is required to enhance the performance of the model. The calibration of a hyperparameter is a time-consuming task, hence proposed an automated hyperparameter optimization technique using the Red Fox Optimization Algorithm (RFO). The effectiveness of the algorithm is evaluated by comparing it with the performance of Harris Hawk Optimization (HHO), Flower Pollination Algorithm (FPA), Grey Wolf Optimization Algorithm (GWO) and Whale Optimization Algorithm (WOA) based hyperparameter optimized CNN applied to the SSVEP based EEG signals multiclass dataset. The experimental results infer that the proposed algorithm can achieve a testing accuracy of 88.91% which is higher than other comparative algorithms like HHO, FPA, GWO and WOA. The above-mentioned values clearly show that the proposed algorithm achieved competitive performance when compared to the other reported algorithm.}, }
@article {pmid36398508, year = {2022}, author = {Rainey, S and Dague, KO and Crisp, R}, title = {Brain-State Transitions, Responsibility, and Personal Identity.}, journal = {Cambridge quarterly of healthcare ethics : CQ : the international journal of healthcare ethics committees}, volume = {31}, number = {4}, pages = {453-463}, doi = {10.1017/S096318012100092X}, pmid = {36398508}, issn = {1469-2147}, mesh = {Humans ; *Self Concept ; *Brain-Computer Interfaces ; Morals ; Brain ; }, abstract = {This article examines the emerging possibility of "brain-state transitioning," in which one brain state is prompted through manipulating the dynamics of the active brain. The technique, still in its infancy, is intended to provide the basis for novel treatments for brain-based disorders. Although a detailed literature exists covering topics around brain-machine interfaces, where targets of brain-based activity include artificial limbs, hardware, and software, there is less concentration on the brain itself as a target for instrumental intervention. This article examines some of the science behind brain-state transitioning, before extending beyond current possibilities in order to explore philosophical and ethical questions about how transitions could be seen to impact on assessment of responsibility and personal identity. It concludes with some thoughts on how best to pursue this nascent approach while accounting for the philosophical and ethical issues.}, }
@article {pmid36398434, year = {2022}, author = {Zhang, J and Wang, L and Xue, Y and Lei, IM and Chen, X and Zhang, P and Cai, C and Liang, X and Lu, Y and Liu, J}, title = {Engineering Electrodes with Robust Conducting Hydrogel Coating for Neural Recording and Modulation.}, journal = {Advanced materials (Deerfield Beach, Fla.)}, volume = {}, number = {}, pages = {e2209324}, doi = {10.1002/adma.202209324}, pmid = {36398434}, issn = {1521-4095}, abstract = {Coating conventional metallic electrodes with conducting polymers has enabled the essential characteristics required for bioelectronics, such as biocompatibility, electrical conductivity, mechanical compliance, and the capacity for structural and chemical functionalization of the bioelectrodes. However, the fragile interface between the conducting polymer and the electrode in wet physiological environment greatly limits their utility and reliability. Here, a general yet reliable strategy to seamlessly interface conventional electrodes with conducting hydrogel coatings is established, featuring tissue-like modulus, highly-desirable electrochemical properties, robust interface, and long-term reliability. Numerical modeling reveals the role of toughening mechanism, synergy of covalent anchorage of long-chain polymers, and chemical cross-linking, in improving the long-term robustness of the interface. Through in vivo implantation in freely-moving mouse models, it is shown that stable electrophysiological recording can be achieved, while the conducting hydrogel-electrode interface remains robust during the long-term low-voltage electrical stimulation. This simple yet versatile design strategy addresses the long-standing technical challenges in functional bioelectrode engineering, and opens up new avenues for the next-generation diagnostic brain-machine interfaces.}, }
@article {pmid36395140, year = {2022}, author = {Faes, A and Camarrone, F and Hulle, MMV}, title = {Single Finger Trajectory Prediction From Intracranial Brain Activity Using Block-Term Tensor Regression With Fast and Automatic Component Extraction.}, journal = {IEEE transactions on neural networks and learning systems}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNNLS.2022.3216589}, pmid = {36395140}, issn = {2162-2388}, abstract = {Multiway-or tensor-based decoding techniques for brain-computer interfaces (BCIs) are believed to better account for the multilinear structure of brain signals than conventional vector-or matrix-based ones. However, despite their outlook on significant performance gains, the used parameter optimization approach is often too computationally demanding so that conventional techniques are still preferred. We propose two novel tensor factorizations which we integrate into our block-term tensor regression (BTTR) algorithm and further introduce a marginalization procedure that guarantees robust predictions while reducing the risk of overfitting (generalized regression). BTTR accounts for the underlying (hidden) data structure in a fully automatic and computationally efficient manner, leading to a significant performance gain over conventional vector-or matrix-based techniques in a challenging real-world application. As a challenging real-world application, we apply BTTR to accurately predict single finger movement trajectories from intracranial recordings in human subjects. We compare the obtained performance with that of the state-of-the-art.}, }
@article {pmid36394044, year = {2022}, author = {Insausti-Delgado, A and López-Larraz, E and Nishimura, Y and Ziemann, U and Ramos-Murguialday, A}, title = {Non-invasive brain-spine interface: Continuous control of trans-spinal magnetic stimulation using EEG.}, journal = {Frontiers in bioengineering and biotechnology}, volume = {10}, number = {}, pages = {975037}, pmid = {36394044}, issn = {2296-4185}, abstract = {Brain-controlled neuromodulation has emerged as a promising tool to promote functional recovery in patients with motor disorders. Brain-machine interfaces exploit this neuromodulatory strategy and could be used for restoring voluntary control of lower limbs. In this work, we propose a non-invasive brain-spine interface (BSI) that processes electroencephalographic (EEG) activity to volitionally control trans-spinal magnetic stimulation (ts-MS), as an approach for lower-limb neurorehabilitation. This novel platform allows to contingently connect motor cortical activation during leg motor imagery with the activation of leg muscles via ts-MS. We tested this closed-loop system in 10 healthy participants using different stimulation conditions. This BSI efficiently removed stimulation artifacts from EEG regardless of ts-MS intensity used, allowing continuous monitoring of cortical activity and real-time closed-loop control of ts-MS. Our BSI induced afferent and efferent evoked responses, being this activation ts-MS intensity-dependent. We demonstrated the feasibility, safety and usability of this non-invasive BSI. The presented system represents a novel non-invasive means of brain-controlled neuromodulation and opens the door towards its integration as a therapeutic tool for lower-limb rehabilitation.}, }
@article {pmid36389253, year = {2022}, author = {Li, P and Su, J and Belkacem, AN and Cheng, L and Chen, C}, title = {Corrigendum: Multi-person feature fusion transfer learning-based convolutional neural network for SSVEP-based collaborative BCI.}, journal = {Frontiers in neuroscience}, volume = {16}, number = {}, pages = {1024150}, doi = {10.3389/fnins.2022.1024150}, pmid = {36389253}, issn = {1662-4548}, abstract = {[This corrects the article DOI: 10.3389/fnins.2022.971039.].}, }
@article {pmid36389231, year = {2022}, author = {Hou, X and Zhao, J and Zhang, H}, title = {Reconstruction of perceived face images from brain activities based on multi-attribute constraints.}, journal = {Frontiers in neuroscience}, volume = {16}, number = {}, pages = {1015752}, pmid = {36389231}, issn = {1662-4548}, abstract = {Reconstruction of perceived faces from brain signals is a hot topic in brain decoding and an important application in the field of brain-computer interfaces. Existing methods do not fully consider the multiple facial attributes represented in face images, and their different activity patterns at multiple brain regions are often ignored, which causes the reconstruction performance very poor. In the current study, we propose an algorithmic framework that efficiently combines multiple face-selective brain regions for precise multi-attribute perceived face reconstruction. Our framework consists of three modules: a multi-task deep learning network (MTDLN), which is developed to simultaneously extract the multi-dimensional face features attributed to facial expression, identity and gender from one single face image, a set of linear regressions (LR), which is built to map the relationship between the multi-dimensional face features and the brain signals from multiple brain regions, and a multi-conditional generative adversarial network (mcGAN), which is used to generate the perceived face images constrained by the predicted multi-dimensional face features. We conduct extensive fMRI experiments to evaluate the reconstruction performance of our framework both subjectively and objectively. The results show that, compared with the traditional methods, our proposed framework better characterizes the multi-attribute face features in a face image, better predicts the face features from brain signals, and achieves better reconstruction performance of both seen and unseen face images in both visual effects and quantitative assessment. Moreover, besides the state-of-the-art intra-subject reconstruction performance, our proposed framework can also realize inter-subject face reconstruction to a certain extent.}, }
@article {pmid36388974, year = {2022}, author = {Keogh, C and FitzGerald, JJ}, title = {Decomposition into dynamic features reveals a conserved temporal structure in hand kinematics.}, journal = {iScience}, volume = {25}, number = {11}, pages = {105428}, pmid = {36388974}, issn = {2589-0042}, abstract = {The human hand is a unique and highly complex effector. The ability to describe hand kinematics with a small number of features suggests that complex hand movements are composed of combinations of simpler movements. This would greatly simplify the neural control of hand movements. If such movement primitives exist, a dimensionality reduction approach designed to exploit these features should outperform existing methods. We developed a deep neural network to capture the temporal dynamics of movements and demonstrate that the features learned allow accurate representation of functional hand movements using lower-dimensional representations than previously reported. We show that these temporal features are highly conserved across individuals and can interpolate previously unseen movements, indicating that they capture the intrinsic structure of hand movements. These results indicate that functional hand movements are defined by a low-dimensional basis set of movement primitives with important temporal dynamics and that these features are common across individuals.}, }
@article {pmid36387766, year = {2022}, author = {Bak, S and Yeu, M and Jeong, J}, title = {Forecasting Unplanned Purchase Behavior under Buy-One Get-One-Free Promotions Using Functional Near-Infrared Spectroscopy.}, journal = {Computational intelligence and neuroscience}, volume = {2022}, number = {}, pages = {1034983}, pmid = {36387766}, issn = {1687-5273}, mesh = {Humans ; *Spectroscopy, Near-Infrared/methods ; *Brain-Computer Interfaces ; Support Vector Machine ; Prefrontal Cortex ; Brain ; }, abstract = {It is very important for consumers to recognize their wrong shopping habits such as unplanned purchase behavior (UPB). The traditional methods used for measuring the UPB in qualitative and quantitative studies have some drawbacks because of human perception and memory. We proposed a UPB identification methodology applied with the brain-computer interface technique using a support vector machine (SVM) along with a functional near-infrared spectroscopy (fNIRS). Hemodynamic signals and behavioral data were collected from 33 subjects by performing Task 1 which included the Buy-One-Get-One-Free (BOGOF) and Task 2 which excluded the BOGOF condition. The acquired data were calculated with 6 time-domain features and then classified them using SVM with 10-cross validations. Thereafter, we evaluated whether the results were reliable using the area under the receiver operating characteristic curve (AUC). As a result, we achieved average accuracy greater than 94%, which is reliable because of the AUC values above 0.97. We found that the UPB brain activity was more relevant to Task 1 with the BOGOF condition than with Task 2 in the prefrontal cortex. UPBs were sufficiently derived from self-reported measurement, indicating that the subjects perceived increased impulsivity in the BOGOF condition. Therefore, this study improves the detection and understanding of UPB as a path for a computer-aided detection perspective for rating the severity of UPBs.}, }
@article {pmid36387584, year = {2022}, author = {Cui, X and Wu, Y and Wu, J and You, Z and Xiahou, J and Ouyang, M}, title = {A review: Music-emotion recognition and analysis based on EEG signals.}, journal = {Frontiers in neuroinformatics}, volume = {16}, number = {}, pages = {997282}, pmid = {36387584}, issn = {1662-5196}, abstract = {Music plays an essential role in human life and can act as an expression to evoke human emotions. The diversity of music makes the listener's experience of music appear diverse. Different music can induce various emotions, and the same theme can also generate other feelings related to the listener's current psychological state. Music emotion recognition (MER) has recently attracted widespread attention in academics and industry. With the development of brain science, MER has been widely used in different fields, e.g., recommendation systems, automatic music composing, psychotherapy, and music visualization. Especially with the rapid development of artificial intelligence, deep learning-based music emotion recognition is gradually becoming mainstream. Besides, electroencephalography (EEG) enables external devices to sense neurophysiological signals in the brain without surgery. This non-invasive brain-computer signal has been used to explore emotions. This paper surveys EEG music emotional analysis, involving the analysis process focused on the music emotion analysis method, e.g., data processing, emotion model, and feature extraction. Then, challenging problems and development trends of EEG-based music emotion recognition is proposed. Finally, the whole paper is summarized.}, }
@article {pmid36381629, year = {2022}, author = {Lee, SH and Thunemann, M and Lee, K and Cleary, DR and Tonsfeldt, KJ and Oh, H and Azzazy, F and Tchoe, Y and Bourhis, AM and Hossain, L and Ro, YG and Tanaka, A and Kılıç, K and Devor, A and Dayeh, SA}, title = {Scalable Thousand Channel Penetrating Microneedle Arrays on Flex for Multimodal and Large Area Coverage BrainMachine Interfaces.}, journal = {Advanced functional materials}, volume = {32}, number = {25}, pages = {}, pmid = {36381629}, issn = {1616-301X}, support = {F32 MH120886/MH/NIMH NIH HHS/United States ; R01 DA050159/DA/NIDA NIH HHS/United States ; UG3 NS123723/NS/NINDS NIH HHS/United States ; R01 NS123655/NS/NINDS NIH HHS/United States ; DP2 EB029757/EB/NIBIB NIH HHS/United States ; R01 MH111359/MH/NIMH NIH HHS/United States ; }, abstract = {The Utah array powers cutting-edge projects for restoration of neurological function, such as BrainGate, but the underlying electrode technology has itself advanced little in the last three decades. Here, advanced dual-side lithographic microfabrication processes is exploited to demonstrate a 1024-channel penetrating silicon microneedle array (SiMNA) that is scalable in its recording capabilities and cortical coverage and is suitable for clinical translation. The SiMNA is the first penetrating microneedle array with a flexible backing that affords compliancy to brain movements. In addition, the SiMNA is optically transparent permitting simultaneous optical and electrophysiological interrogation of neuronal activity. The SiMNA is used to demonstrate reliable recordings of spontaneous and evoked field potentials and of single unit activity in chronically implanted mice for up to 196 days in response to optogenetic and to whisker air-puff stimuli. Significantly, the 1024-channel SiMNA establishes detailed spatiotemporal mapping of broadband brain activity in rats. This novel scalable and biocompatible SiMNA with its multimodal capability and sensitivity to broadband brain activity will accelerate the progress in fundamental neurophysiological investigations and establishes a new milestone for penetrating and large area coverage microelectrode arrays for brain-machine interfaces.}, }
@article {pmid36379711, year = {2022}, author = {Heusser, MR and Bourrelly, C and Gandhi, NJ}, title = {Decoding the Time Course of Spatial Information from Spiking and Local Field Potential Activities in the Superior Colliculus.}, journal = {eNeuro}, volume = {9}, number = {6}, pages = {}, pmid = {36379711}, issn = {2373-2822}, mesh = {Animals ; *Superior Colliculi/physiology ; Macaca mulatta ; *Saccades ; Eye Movements ; Neurons/physiology ; }, abstract = {Place code representation is ubiquitous in circuits that encode spatial parameters. For visually guided eye movements, neurons in many brain regions emit spikes when a stimulus is presented in their receptive fields and/or when a movement is directed into their movement fields. Crucially, individual neurons respond for a broad range of directions or eccentricities away from the optimal vector, making it difficult to decode the stimulus location or the saccade vector from each cell's activity. We investigated whether it is possible to decode the spatial parameter with a population-level analysis, even when the optimal vectors are similar across neurons. Spiking activity and local field potentials (LFPs) in the superior colliculus (SC) were recorded with a laminar probe as monkeys performed a delayed saccade task to one of eight targets radially equidistant in direction. A classifier was applied offline to decode the spatial configuration as the trial progresses from sensation to action. For spiking activity, decoding performance across all eight directions was highest during the visual and motor epochs and lower but well above chance during the delay period. Classification performance followed a similar pattern for LFP activity too, except the performance during the delay period was limited mostly to the preferred direction. Increasing the number of neurons in the population consistently increased classifier performance for both modalities. Overall, this study demonstrates the power of population activity for decoding spatial information not possible from individual neurons.}, }
@article {pmid36376487, year = {2022}, author = {Jia, Y and Xu, S and Han, G and Wang, B and Wang, Z and Lan, C and Zhao, P and Gao, M and Zhang, Y and Jiang, W and Qiu, B and Liu, R and Hsu, YC and Sun, Y and Liu, C and Liu, Y and Bai, R}, title = {Transmembrane water-efflux rate measured by magnetic resonance imaging as a biomarker of the expression of aquaporin-4 in gliomas.}, journal = {Nature biomedical engineering}, volume = {}, number = {}, pages = {}, pmid = {36376487}, issn = {2157-846X}, abstract = {The water-selective channel protein aquaporin-4 (AQP4) contributes to the migration and proliferation of gliomas, and to their resistance to therapy. Here we show, in glioma cell cultures, in subcutaneous and orthotopic gliomas in rats, and in glioma tumours in patients, that transmembrane water-efflux rate is a sensitive biomarker of AQP4 expression and can be measured via conventional dynamic-contrast-enhanced magnetic resonance imaging. Water-efflux rates correlated with stages of glioma proliferation as well as with changes in the heterogeneity of intra-tumoural and inter-tumoural AQP4 in rodent and human gliomas following treatment with temozolomide and with the AQP4 inhibitor TGN020. Regions with low water-efflux rates contained higher fractions of stem-like slow-cycling cells and therapy-resistant cells, suggesting that maps of water-efflux rates could be used to identify gliomas that are resistant to therapies.}, }
@article {pmid36376067, year = {2022}, author = {Iwama, S and Zhang, Y and Ushiba, J}, title = {De Novo Brain-Computer Interfacing Deforms Manifold of Populational Neural Activity Patterns in Human Cerebral Cortex.}, journal = {eNeuro}, volume = {9}, number = {6}, pages = {}, pmid = {36376067}, issn = {2373-2822}, mesh = {Humans ; Male ; Female ; *Electroencephalography/methods ; *Cerebral Cortex ; Brain/physiology ; Algorithms ; Computers ; }, abstract = {Human brains are capable of modulating innate activities to adapt to novel environments and tasks; for sensorimotor neural system this means acquisition of a rich repertoire of activity patterns that improve behavioral performance. To directly map the process of acquiring the neural repertoire during tasks onto performance improvement, we analyzed net neural populational activity during the learning of its voluntary modulation by brain-computer interface (BCI) operation in female and male humans. The recorded whole-head high-density scalp electroencephalograms (EEGs) were subjected to dimensionality reduction algorithm to capture changes in cortical activity patterns represented by the synchronization of neuronal oscillations during adaptation. Although the preserved variance of targeted features in the reduced dimensions was 20%, we found systematic interactions between the activity patterns and BCI classifiers that detected motor attempt; the neural manifold derived in the embedded space was stretched along with motor-related features of EEG by model-based fixed classifiers but not with adaptive classifiers that were constantly recalibrated to user activity. Moreover, the manifold was deformed to be orthogonal to the boundary by de novo classifiers with a fixed decision boundary based on biologically unnatural features. Collectively, the flexibility of human cortical signaling patterns (i.e., neural plasticity) is only induced by operation of a BCI whose classifier required fixed activities, and the adaptation could be induced even the requirement is not consistent with biologically natural responses. These principles of neural adaptation at a macroscopic level may underlie the ability of humans to learn wide-ranging behavioral repertoires and adapt to novel environments.}, }
@article {pmid36373709, year = {2022}, author = {Lyu, C and Yu, C and Sun, G and Zhao, Y and Cai, R and Sun, H and Wang, X and Jia, G and Fan, L and Chen, X and Zhou, L and Shen, Y and Gao, L and Li, X}, title = {Deconstruction of Vermal Cerebellum in Ramp Locomotion in Mice.}, journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)}, volume = {}, number = {}, pages = {e2203665}, doi = {10.1002/advs.202203665}, pmid = {36373709}, issn = {2198-3844}, abstract = {The cerebellum is involved in encoding balance, posture, speed, and gravity during locomotion. However, most studies are carried out on flat surfaces, and little is known about cerebellar activity during free ambulation on slopes. Here, it has been imaged the neuronal activity of cerebellar molecular interneurons (MLIs) and Purkinje cells (PCs) using a miniaturized microscope while a mouse is walking on a slope. It has been found that the neuronal activity of vermal MLIs specifically enhanced during uphill and downhill locomotion. In addition, a subset of MLIs is activated during entire uphill or downhill positions on the slope and is modulated by the slope inclines. In contrast, PCs showed counter-balanced neuronal activity to MLIs, which reduced activity at the ramp peak. So, PCs may represent the ramp environment at the population level. In addition, chemogenetic inactivation of lobule V of the vermis impaired uphill locomotion. These results revealed a novel micro-circuit in the vermal cerebellum that regulates ambulatory behavior in 3D terrains.}, }
@article {pmid36371498, year = {2022}, author = {Willsey, MS and Nason-Tomaszewski, SR and Ensel, SR and Temmar, H and Mender, MJ and Costello, JT and Patil, PG and Chestek, CA}, title = {Real-time brain-machine interface in non-human primates achieves high-velocity prosthetic finger movements using a shallow feedforward neural network decoder.}, journal = {Nature communications}, volume = {13}, number = {1}, pages = {6899}, pmid = {36371498}, issn = {2041-1723}, mesh = {Animals ; Male ; *Brain-Computer Interfaces ; Macaca mulatta ; Neural Networks, Computer ; Movement ; Algorithms ; }, abstract = {Despite the rapid progress and interest in brain-machine interfaces that restore motor function, the performance of prosthetic fingers and limbs has yet to mimic native function. The algorithm that converts brain signals to a control signal for the prosthetic device is one of the limitations in achieving rapid and realistic finger movements. To achieve more realistic finger movements, we developed a shallow feed-forward neural network to decode real-time two-degree-of-freedom finger movements in two adult male rhesus macaques. Using a two-step training method, a recalibrated feedback intention-trained (ReFIT) neural network is introduced to further improve performance. In 7 days of testing across two animals, neural network decoders, with higher-velocity and more natural appearing finger movements, achieved a 36% increase in throughput over the ReFIT Kalman filter, which represents the current standard. The neural network decoders introduced herein demonstrate real-time decoding of continuous movements at a level superior to the current state-of-the-art and could provide a starting point to using neural networks for the development of more naturalistic brain-controlled prostheses.}, }
@article {pmid36368035, year = {2022}, author = {Rommel, C and Paillard, J and Moreau, T and Gramfort, A}, title = {Data augmentation for learning predictive models on EEG: a systematic comparison.}, journal = {Journal of neural engineering}, volume = {19}, number = {6}, pages = {}, doi = {10.1088/1741-2552/aca220}, pmid = {36368035}, issn = {1741-2552}, mesh = {*Electroencephalography/methods ; *Brain-Computer Interfaces ; Imagery, Psychotherapy ; Sleep Stages ; Sleep ; }, abstract = {Objective.The use of deep learning for electroencephalography (EEG) classification tasks has been rapidly growing in the last years, yet its application has been limited by the relatively small size of EEG datasets. Data augmentation, which consists in artificially increasing the size of the dataset during training, can be employed to alleviate this problem. While a few augmentation transformations for EEG data have been proposed in the literature, their positive impact on performance is often evaluated on a single dataset and compared to one or two competing augmentation methods. This work proposes to better validate the existing data augmentation approaches through a unified and exhaustive analysis.Approach.We compare quantitatively 13 different augmentations with two different predictive tasks, datasets and models, using three different types of experiments.Main results.We demonstrate that employing the adequate data augmentations can bring up to 45% accuracy improvements in low data regimes compared to the same model trained without any augmentation. Our experiments also show that there is no single best augmentation strategy, as the good augmentations differ on each task.Significance.Our results highlight the best data augmentations to consider for sleep stage classification and motor imagery brain-computer interfaces. More broadly, it demonstrates that EEG classification tasks benefit from adequate data augmentation.}, }
@article {pmid36366726, year = {2022}, author = {Wu, L and Ren, K and Chen, G and Wang, H and Li, H and Xu, L}, title = {Hemostatic effect and safety evaluation of the absorbable macroporous polysaccharides composite hemostatic material prepared by a green fabrication approach.}, journal = {Journal of biomaterials applications}, volume = {}, number = {}, pages = {8853282221139026}, doi = {10.1177/08853282221139026}, pmid = {36366726}, issn = {1530-8022}, abstract = {Carboxymethyl chitosan is widely used in the medical field such as wound healing and other medical fields. We previously fabricated the absorbable macroporous polysaccharides composite hemostatics (AMPCs) mainly composed of carboxymethyl chitosan which possess excellent hemostatic effect. To further elucidate the impact of CMCTs on the hemostatic effect and biosafety of AMPCs, carboxymethyl chitosan with different properties were used to prepare AMPCs. By comparing the physical and chemical properties, AMPCs performed high water absorption ability, especially Group 1 (swelling ratio reached 5792%), which facilitated the rapid formation of blood clots. It was confirmed by blood clotting index (BCI) and blood coagulation tests in vitro that Group 1 showed a slightly higher coagulation capacity than groups 2 and 3, which may be due to the positive charge on the surface of the cations in the salts attaches to the negative charge on the surface of the red blood cells, an electrostatic neutralization reaction occurs. The biosafety was a preliminary evaluation by implanted AMPCs into the back of Sprague-Dawley rats and the tissue was harvested after feeding for 28 days. The AMPCs exhibited good biosafety for whole blood and major organs during the degradation in vivo: during the degradation of AMPCs, excluding changes in some serum indicators, no tissue necrosis or inflammatory cell infiltration was observed in these organs, either by gross observation or histological analysis. These findings demonstrate that expecting to develop a highly functional and safe hemostatic agent based on Group 1 for rapid hemostasis applications in emergencies.}, }
@article {pmid36366225, year = {2022}, author = {Hu, H and Pu, Z and Li, H and Liu, Z and Wang, P}, title = {Learning Optimal Time-Frequency-Spatial Features by the CiSSA-CSP Method for Motor Imagery EEG Classification.}, journal = {Sensors (Basel, Switzerland)}, volume = {22}, number = {21}, pages = {}, pmid = {36366225}, issn = {1424-8220}, mesh = {*Brain-Computer Interfaces ; Imagination ; Signal Processing, Computer-Assisted ; Brain ; Electroencephalography/methods ; Algorithms ; }, abstract = {The common spatial pattern (CSP) is a popular method in feature extraction for motor imagery (MI) electroencephalogram (EEG) classification in brain-computer interface (BCI) systems. However, combining temporal and spectral information in the CSP-based spatial features is still a challenging issue, which greatly affects the performance of MI-based BCI systems. Here, we propose a novel circulant singular spectrum analysis embedded CSP (CiSSA-CSP) method for learning the optimal time-frequency-spatial features to improve the MI classification accuracy. Specifically, raw EEG data are first segmented into multiple time segments and spectrum-specific sub-bands are further derived by CiSSA from each time segment in a set of non-overlapping filter bands. CSP features extracted from all time-frequency segments contain more sufficient time-frequency-spatial information. An experimental study was implemented on the publicly available EEG dataset (BCI Competition III dataset IVa) and a self-collected experimental EEG dataset to validate the effectiveness of the CiSSA-CSP method. Experimental results demonstrate that discriminative and robust features are extracted effectively. Compared with several state-of-the-art methods, the proposed method exhibited optimal accuracies of 96.6% and 95.2% on the public and experimental datasets, respectively, which confirms that it is a promising method for improving the performance of MI-based BCIs.}, }
@article {pmid36366001, year = {2022}, author = {Chuang, CC and Lee, CC and So, EC and Yeng, CH and Chen, YJ}, title = {Multi-Task Learning-Based Deep Neural Network for Steady-State Visual Evoked Potential-Based Brain-Computer Interfaces.}, journal = {Sensors (Basel, Switzerland)}, volume = {22}, number = {21}, pages = {}, pmid = {36366001}, issn = {1424-8220}, mesh = {Humans ; *Brain-Computer Interfaces ; Evoked Potentials, Visual ; *Amyotrophic Lateral Sclerosis ; Neural Networks, Computer ; Electroencephalography/methods ; Photic Stimulation ; Algorithms ; }, abstract = {Amyotrophic lateral sclerosis (ALS) causes people to have difficulty communicating with others or devices. In this paper, multi-task learning with denoising and classification tasks is used to develop a robust steady-state visual evoked potential-based brain-computer interface (SSVEP-based BCI), which can help people communicate with others. To ease the operation of the input interface, a single channel-based SSVEP-based BCI is selected. To increase the practicality of SSVEP-based BCI, multi-task learning is adopted to develop the neural network-based intelligent system, which can suppress the noise components and obtain a high level of accuracy of classification. Thus, denoising and classification tasks are selected in multi-task learning. The experimental results show that the proposed multi-task learning can effectively integrate the advantages of denoising and discriminative characteristics and outperform other approaches. Therefore, multi-task learning with denoising and classification tasks is very suitable for developing an SSVEP-based BCI for practical applications. In the future, an augmentative and alternative communication interface can be implemented and examined for helping people with ALS communicate with others in their daily lives.}, }
@article {pmid36365948, year = {2022}, author = {Emsawas, T and Morita, T and Kimura, T and Fukui, KI and Numao, M}, title = {Multi-Kernel Temporal and Spatial Convolution for EEG-Based Emotion Classification.}, journal = {Sensors (Basel, Switzerland)}, volume = {22}, number = {21}, pages = {}, pmid = {36365948}, issn = {1424-8220}, mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Neural Networks, Computer ; Emotions ; Palliative Care ; }, abstract = {Deep learning using an end-to-end convolutional neural network (ConvNet) has been applied to several electroencephalography (EEG)-based brain-computer interface tasks to extract feature maps and classify the target output. However, the EEG analysis remains challenging since it requires consideration of various architectural design components that influence the representational ability of extracted features. This study proposes an EEG-based emotion classification model called the multi-kernel temporal and spatial convolution network (MultiT-S ConvNet). The multi-scale kernel is used in the model to learn various time resolutions, and separable convolutions are applied to find related spatial patterns. In addition, we enhanced both the temporal and spatial filters with a lightweight gating mechanism. To validate the performance and classification accuracy of MultiT-S ConvNet, we conduct subject-dependent and subject-independent experiments on EEG-based emotion datasets: DEAP and SEED. Compared with existing methods, MultiT-S ConvNet outperforms with higher accuracy results and a few trainable parameters. Moreover, the proposed multi-scale module in temporal filtering enables extracting a wide range of EEG representations, covering short- to long-wavelength components. This module could be further implemented in any model of EEG-based convolution networks, and its ability potentially improves the model's learning capacity.}, }
@article {pmid36365824, year = {2022}, author = {Khare, SK and Gaikwad, N and Bokde, ND}, title = {An Intelligent Motor Imagery Detection System Using Electroencephalography with Adaptive Wavelets.}, journal = {Sensors (Basel, Switzerland)}, volume = {22}, number = {21}, pages = {}, pmid = {36365824}, issn = {1424-8220}, mesh = {Humans ; *Electroencephalography/methods ; Wavelet Analysis ; *Brain-Computer Interfaces ; Imagery, Psychotherapy ; Algorithms ; Support Vector Machine ; Signal Processing, Computer-Assisted ; }, abstract = {Classification of motor imagery (MI) tasks provides a robust solution for specially-abled people to connect with the milieu for brain-computer interface. Precise selection of uniform tuning parameters of tunable Q wavelet transform (TQWT) for electroencephalography (EEG) signals is arduous. Therefore, this paper proposes robust TQWT for automatically selecting optimum tuning parameters to decompose non-stationary EEG signals accurately. Three evolutionary optimization algorithms are explored for automating the tuning parameters of robust TQWT. The fitness function of the mean square error of decomposition is used. This paper also exploits channel selection using a Laplacian score for dominant channel selection. Important features elicited from sub-bands of robust TQWT are classified using different kernels of the least square support vector machine classifier. The radial basis function kernel has provided the highest accuracy of 99.78%, proving that the proposed method is superior to other state-of-the-art using the same database.}, }
@article {pmid36364633, year = {2022}, author = {Frenzel, J and Kupferer, A and Zink, M and Mayr, SG}, title = {Laminin Adsorption and Adhesion of Neurons and Glial Cells on Carbon Implanted Titania Nanotube Scaffolds for Neural Implant Applications.}, journal = {Nanomaterials (Basel, Switzerland)}, volume = {12}, number = {21}, pages = {}, pmid = {36364633}, issn = {2079-4991}, abstract = {Interfacing neurons persistently to conductive matter constitutes one of the key challenges when designing brain-machine interfaces such as neuroelectrodes or retinal implants. Novel materials approaches that prevent occurrence of loss of long-term adhesion, rejection reactions, and glial scarring are highly desirable. Ion doped titania nanotube scaffolds are a promising material to fulfill all these requirements while revealing sufficient electrical conductivity, and are scrutinized in the present study regarding their neuron-material interface. Adsorption of laminin, an essential extracellular matrix protein of the brain, is comprehensively analyzed. The implantation-dependent decline in laminin adsorption is revealed by employing surface characteristics such as nanotube diameter, ζ-potential, and surface free energy. Moreover, the viability of U87-MG glial cells and SH-SY5Y neurons after one and four days are investigated, as well as the material's cytotoxicity. The higher conductivity related to carbon implantation does not affect the viability of neurons, although it impedes glial cell proliferation. This gives rise to novel titania nanotube based implant materials with long-term stability, and could reduce undesirable glial scarring.}, }
@article {pmid36359646, year = {2022}, author = {Wang, K and Tian, F and Xu, M and Zhang, S and Xu, L and Ming, D}, title = {Resting-State EEG in Alpha Rhythm May Be Indicative of the Performance of Motor Imagery-Based Brain-Computer Interface.}, journal = {Entropy (Basel, Switzerland)}, volume = {24}, number = {11}, pages = {}, pmid = {36359646}, issn = {1099-4300}, abstract = {Motor imagery-based brain-computer interfaces (MI-BCIs) have great application prospects in motor enhancement and rehabilitation. However, the capacity to control a MI-BCI varies among persons. Predicting the MI ability of a user remains challenging in BCI studies. We first calculated the relative power level (RPL), power spectral entropy (PSE) and Lempel-Ziv complexity (LZC) of the resting-state open and closed-eye EEG of different frequency bands and investigated their correlations with the upper and lower limbs MI performance (left hand, right hand, both hands and feet MI tasks) on as many as 105 subjects. Then, the most significant related features were used to construct a classifier to separate the high MI performance group from the low MI performance group. The results showed that the features of open-eye resting alpha-band EEG had the strongest significant correlations with MI performance. The PSE performed the best among all features for the screening of the MI performance, with the classification accuracy of 85.24%. These findings demonstrated that the alpha bands might offer information related to the user's MI ability, which could be used to explore more effective and general neural markers to screen subjects and design individual MI training strategies.}, }
@article {pmid36359454, year = {2022}, author = {Syrov, N and Yakovlev, L and Nikolaeva, V and Kaplan, A and Lebedev, M}, title = {Mental Strategies in a P300-BCI: Visuomotor Transformation Is an Option.}, journal = {Diagnostics (Basel, Switzerland)}, volume = {12}, number = {11}, pages = {}, pmid = {36359454}, issn = {2075-4418}, abstract = {Currently, P300-BCIs are mostly used for spelling tasks, where the number of commands is equal to the number of stimuli that evoke event-related potentials (ERPs). Increasing this number slows down the BCI operation because each stimulus has to be presented several times for better classification. Furthermore, P300 spellers typically do not utilize potentially useful imagery-based approaches, such as the motor imagery successfully practiced in motor rehabilitation. Here, we tested a P300-BCI with a motor-imagery component. In this BCI, the number of commands was increased by adding mental strategies instead of increasing the number of targets. Our BCI had only two stimuli and four commands. The subjects either counted target appearances mentally or imagined hand movements toward the targets. In this design, the motor-imagery paradigm enacted a visuomotor transformation known to engage cortical and subcortical networks participating in motor control. The operation of these networks suffers in neurological conditions such as stroke, so we view this BCI as a potential tool for the rehabilitation of patients. As an initial step toward the development of this clinical method, sixteen healthy participants were tested. Consistent with our expectation that mental strategies would result in distinct EEG activities, ERPs were different depending on whether subjects counted stimuli or imagined movements. These differences were especially clear in the late ERP components localized in the frontal and centro-parietal regions. We conclude that (1) the P300 paradigm is suitable for enacting visuomotor transformations and (2) P300-based BCIs with multiple mental strategies could be used in applications where the number of possible outputs needs to be increased while keeping the number of targets constant. As such, our approach adds to both the development of versatile BCIs and clinical approaches to rehabilitation.}, }
@article {pmid36358440, year = {2022}, author = {Adhia, DB and Mani, R and Turner, PR and Vanneste, S and De Ridder, D}, title = {Infraslow Neurofeedback Training Alters Effective Connectivity in Individuals with Chronic Low Back Pain: A Secondary Analysis of a Pilot Randomized Placebo-Controlled Study.}, journal = {Brain sciences}, volume = {12}, number = {11}, pages = {}, pmid = {36358440}, issn = {2076-3425}, abstract = {This study explored the effect of electroencephalographic infraslow neurofeedback (EEG ISF-NF) training on effective connectivity and tested whether such effective connectivity changes are correlated with changes in pain and disability in people with chronic low back pain. This involved secondary analysis of a pilot double-blinded randomised placebo-controlled study. Participants (n = 60) were randomised to receive ISF-NF targeting either the pregenual anterior cingulate cortex (pgACC), dorsal anterior cingulate and somatosensory cortex (dACC + S1), ratio of pgACC*2/dACC + S1, or Sham-NF. Resting-state EEG and clinical outcomes were assessed at baseline, immediately after intervention, and at one-week and one-month follow-up. Kruskal-Wallis tests demonstrated significant between-group differences in effective connectivity from pgACC to S1L at one-month follow up and marginal significant changes from S1L to pgACC at one-week and one-month follow up. Mann-Whitney U tests demonstrated significant increases in effective connectivity in the ISF-NF up-training pgACC group when compared to the Sham-NF group (pgACC to S1L at one-month (p = 0.013), and S1L to pgACC at one-week (p = 0.008) and one-month follow up (p = 0.016)). Correlational analyses demonstrated a significant negative correlation (ρ = -0.630, p = 0.038) between effective connectivity changes from pgACC to S1L and changes in pain severity at one-month follow-up. The ISF-NF training pgACC can reduce pain via influencing effective connectivity between pgACC and S1L.}, }
@article {pmid36358428, year = {2022}, author = {Cao, L and Wu, H and Chen, S and Dong, Y and Zhu, C and Jia, J and Fan, C}, title = {A Novel Deep Learning Method Based on an Overlapping Time Window Strategy for Brain-Computer Interface-Based Stroke Rehabilitation.}, journal = {Brain sciences}, volume = {12}, number = {11}, pages = {}, pmid = {36358428}, issn = {2076-3425}, abstract = {Globally, stroke is a leading cause of death and disability. The classification of motor intentions using brain activity is an important task in the rehabilitation of stroke patients using brain-computer interfaces (BCIs). This paper presents a new method for model training in EEG-based BCI rehabilitation by using overlapping time windows. For this aim, three different models, a convolutional neural network (CNN), graph isomorphism network (GIN), and long short-term memory (LSTM), are used for performing the classification task of motor attempt (MA). We conducted several experiments with different time window lengths, and the results showed that the deep learning approach based on overlapping time windows achieved improvements in classification accuracy, with the LSTM combined vote-counting strategy (VS) having achieved the highest average classification accuracy of 90.3% when the window size was 70. The results verified that the overlapping time window strategy is useful for increasing the efficiency of BCI rehabilitation.}, }
@article {pmid36356391, year = {2022}, author = {Wan, Z and Yang, R and Huang, M and Alsaadi, FE and Sheikh, MM and Wang, Z}, title = {Segment alignment based cross-subject motor imagery classification under fading data.}, journal = {Computers in biology and medicine}, volume = {151}, number = {Pt A}, pages = {106267}, doi = {10.1016/j.compbiomed.2022.106267}, pmid = {36356391}, issn = {1879-0534}, mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Signal Processing, Computer-Assisted ; Imagination ; Algorithms ; }, abstract = {Motor imagery (MI) aims to use brain imagination without actual body activities to support motor learning, and machine learning algorithms such as common spatial patterns (CSP) are proven effective in the analysis of MI signals. In the conventional machine learning-based approaches, there are two main difficulties in feature extraction and recognition of MI signals: high personalization and data fading. The high personalization problem is due to the multi-subject nature when collecting MI signals, and the data fading problem as a recurring issue in MI signal quality is first raised by us but is not widely discussed at present. Aiming to solve the above two mentioned problems, a cross-subject fading data classification approach with segment alignment is proposed to classify the fading data of one single target with the model trained with the normal data of multiple sources in this paper. he effectiveness of proposed method is verified via two experiments: a dataset-based experiment with the dataset from BCI Competition and a lab-based experiment designed and conducted by us. The experimental results obtained from both experiments show that the proposed method can obtain optimal classification performance effectively under different fading levels with data from different subjects.}, }
@article {pmid36356309, year = {2022}, author = {Petrosyan, A and Voskoboinikov, A and Sukhinin, D and Makarova, A and Skalnaya, A and Arkhipova, N and Sinkin, M and Ossadtchi, A}, title = {Speech decoding from a small set of spatially segregated minimally invasive intracranial EEG electrodes with a compact and interpretable neural network.}, journal = {Journal of neural engineering}, volume = {19}, number = {6}, pages = {}, doi = {10.1088/1741-2552/aca1e1}, pmid = {36356309}, issn = {1741-2552}, mesh = {Humans ; *Electrocorticography/methods ; Speech/physiology ; Electroencephalography/methods ; *Brain-Computer Interfaces ; Neural Networks, Computer ; Electrodes ; }, abstract = {Objective. Speech decoding, one of the most intriguing brain-computer interface applications, opens up plentiful opportunities from rehabilitation of patients to direct and seamless communication between human species. Typical solutions rely on invasive recordings with a large number of distributed electrodes implanted through craniotomy. Here we explored the possibility of creating speech prosthesis in a minimally invasive setting with a small number of spatially segregated intracranial electrodes.Approach. We collected one hour of data (from two sessions) in two patients implanted with invasive electrodes. We then used only the contacts that pertained to a single stereotactic electroencephalographic (sEEG) shaft or an electrocorticographic (ECoG) stripe to decode neural activity into 26 words and one silence class. We employed a compact convolutional network-based architecture whose spatial and temporal filter weights allow for a physiologically plausible interpretation.Mainresults. We achieved on average 55% accuracy using only six channels of data recorded with a single minimally invasive sEEG electrode in the first patient and 70% accuracy using only eight channels of data recorded for a single ECoG strip in the second patient in classifying 26+1 overtly pronounced words. Our compact architecture did not require the use of pre-engineered features, learned fast and resulted in a stable, interpretable and physiologically meaningful decision rule successfully operating over a contiguous dataset collected during a different time interval than that used for training. Spatial characteristics of the pivotal neuronal populations corroborate with active and passive speech mapping results and exhibit the inverse space-frequency relationship characteristic of neural activity. Compared to other architectures our compact solution performed on par or better than those recently featured in neural speech decoding literature.Significance. We showcase the possibility of building a speech prosthesis with a small number of electrodes and based on a compact feature engineering free decoder derived from a small amount of training data.}, }
@article {pmid36355738, year = {2022}, author = {Chen, R and Xu, G and Jia, Y and Zhou, C and Wang, Z and Pei, J and Han, C and Wang, Y and Zhang, S}, title = {Enhancement of time-frequency energy for the classification of motor imagery electroencephalogram based on an improved FitzHugh-Nagumo neuron system.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2022.3219450}, pmid = {36355738}, issn = {1558-0210}, abstract = {Brain-computer interface (BCI) based on motor imagery (MI) electroencephalogram (EEG) has become an essential way for rehabilitation, because of the activation and interaction of motor neurons between the brain and rehabilitation devices in recent years. However, due to the discrepancies between individuals, the frequency ranges can be different even for the same rhythm component of EEG recordings, which brings difficulties to the extraction of features for MI classification. Typical algorithms for MI classification such as common spatial patterns (CSP) require multi-channel analysis and lack frequency information. With the development of BCI, the single-channel BCI system has become indispensable for simplicity of use. However, the currently available single-channel detection methods have low classification accuracy. To address this issue, two novel frameworks based on an improved two-dimensional nonlinear FitzHugh-Nagumo (FHN) neuron system are proposed to extract features of the single-channel MI. To evaluate the effectiveness of the proposed methods, this research utilized an open-access database (BCI competition IV dataset 2a), an offline database, and a 10-fold cross-validation procedure. Experimental results showed that the improved nonlinear FHN system can transfer the energy of noise into MI, thereby effectively enhancing the time-frequency energy. Compared with the traditional methods, the proposed methods can achieve higher classification accuracy and robustness.}, }
@article {pmid36354027, year = {2022}, author = {Johnson, KA and Worbe, Y and Foote, KD and Butson, CR and Gunduz, A and Okun, MS}, title = {Tourette syndrome: clinical features, pathophysiology, and treatment.}, journal = {The Lancet. Neurology}, volume = {}, number = {}, pages = {}, doi = {10.1016/S1474-4422(22)00303-9}, pmid = {36354027}, issn = {1474-4465}, abstract = {Tourette syndrome is a chronic neurodevelopmental disorder characterised by motor and phonic tics that can substantially diminish the quality of life of affected individuals. Evaluating and treating Tourette syndrome is complex, in part due to the heterogeneity of symptoms and comorbidities between individuals. The underlying pathophysiology of Tourette syndrome is not fully understood, but recent research in the past 5 years has brought new insights into the genetic variations and the alterations in neurophysiology and brain networks contributing to its pathogenesis. Treatment options for Tourette syndrome are expanding with novel pharmacological therapies and increased use of deep brain stimulation for patients with symptoms that are refractory to pharmacological or behavioural treatments. Potential predictors of patient responses to therapies for Tourette syndrome, such as specific networks modulated during deep brain stimulation, can guide clinical decisions. Multicentre data sharing initiatives have enabled several advances in our understanding of the genetics and pathophysiology of Tourette syndrome and will be crucial for future large-scale research and in refining effective treatments.}, }
@article {pmid36351832, year = {2022}, author = {Fischer, L and Mojica Soto-Albors, R and Tang, VD and Bicknell, B and Grienberger, C and Francioni, V and Naud, R and Palmer, LM and Takahashi, N}, title = {Dendritic Mechanisms for In Vivo Neural Computations and Behavior.}, journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience}, volume = {42}, number = {45}, pages = {8460-8467}, pmid = {36351832}, issn = {1529-2401}, mesh = {*Dendrites/physiology ; *Pyramidal Cells/physiology ; Neurons/physiology ; Hippocampus ; Learning ; Models, Neurological ; Action Potentials/physiology ; }, abstract = {Dendrites receive the vast majority of a single neuron's inputs, and coordinate the transformation of these signals into neuronal output. Ex vivo and theoretical evidence has shown that dendrites possess powerful processing capabilities, yet little is known about how these mechanisms are engaged in the intact brain or how they influence circuit dynamics. New experimental and computational technologies have led to a surge in interest to unravel and harness their computational potential. This review highlights recent and emerging work that combines established and cutting-edge technologies to identify the role of dendrites in brain function. We discuss active dendritic mediation of sensory perception and learning in neocortical and hippocampal pyramidal neurons. Complementing these physiological findings, we present theoretical work that provides new insights into the underlying computations of single neurons and networks by using biologically plausible implementations of dendritic processes. Finally, we present a novel brain-computer interface task, which assays somatodendritic coupling to study the mechanisms of biological credit assignment. Together, these findings present exciting progress in understanding how dendrites are critical for in vivo learning and behavior, and highlight how subcellular processes can contribute to our understanding of both biological and artificial neural computation.}, }
@article {pmid36351413, year = {2022}, author = {McNamara, CG and Rothwell, M and Sharott, A}, title = {Stable, interactive modulation of neuronal oscillations produced through brain-machine equilibrium.}, journal = {Cell reports}, volume = {41}, number = {6}, pages = {111616}, doi = {10.1016/j.celrep.2022.111616}, pmid = {36351413}, issn = {2211-1247}, support = {MC_UU_12024/1/MRC_/Medical Research Council/United Kingdom ; MC_UU_00003/6/MRC_/Medical Research Council/United Kingdom ; 209120/Z/17/Z/WT_/Wellcome Trust/United Kingdom ; }, mesh = {Animals ; Rats ; *Deep Brain Stimulation ; *Parkinson Disease/therapy ; Basal Ganglia/physiology ; Neurons/physiology ; Brain ; }, abstract = {Closed-loop interaction has the potential to regulate ongoing brain activity by continuously binding an external stimulation to specific dynamics of a neural circuit. Achieving interactive modulation requires a stable brain-machine feedback loop. Here, we demonstrate that it is possible to maintain oscillatory brain activity in a desired state by delivering stimulation accurately aligned with the timing of each cycle. We develop a fast algorithm that responds on a cycle-by-cycle basis to stimulate basal ganglia nuclei at predetermined phases of successive cortical beta cycles in parkinsonian rats. Using this approach, an equilibrium emerges between the modified brain signal and feedback-dependent stimulation pattern, leading to sustained amplification or suppression of the oscillation depending on the phase targeted. Beta amplification slows movement speed by biasing the animal's mode of locomotion. Together, these findings show that highly responsive, phase-dependent stimulation can achieve a stable brain-machine interaction that leads to robust modulation of ongoing behavior.}, }
@article {pmid36350872, year = {2022}, author = {Zhang, M and Wu, J and Song, J and Fu, R and Ma, R and Jiang, YC and Chen, YF}, title = {Decoding Coordinated Directions of Bimanual Movements from EEG Signals.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2022.3220884}, pmid = {36350872}, issn = {1558-0210}, abstract = {Bimanual coordination is common in human daily life, whereas current research focused mainly on decoding unimanual movement from electroencephalogram (EEG) signals. Here we developed a brain-computer interface (BCI) paradigm of task-oriented bimanual movements to decode coordinated directions from movement-related cortical potentials (MRCPs) of EEG. Eight healthy subjects participated in the target-reaching task, including (1) performing leftward, midward, and rightward bimanual movements, and (2) performing leftward and rightward unimanual movements. A combined deep learning model of convolution neural network and bidirectional long short-term memory network was proposed to classify movement directions from EEG. Results showed that the average peak classification accuracy for three coordinated directions of bimanual movements reached 73.39 ± 6.35%. The binary classification accuracies achieved 80.24 ± 6.25, 82.62 ± 7.82, and 86.28 ± 5.50% for leftward versus midward, rightward versus midward and leftward versus rightward, respectively. We also compared the binary classification (leftward versus rightward) of bimanual, left-hand, and right-hand movements, and accuracies achieved 86.28 ± 5.50%, 75.67 ± 7.18%, and 77.79 ± 5.65%, respectively. The results indicated the feasibility of decoding human coordinated directions of task-oriented bimanual movements from EEG.}, }
@article {pmid36350815, year = {2022}, author = {Sattler, S and Pietralla, D}, title = {Public attitudes towards neurotechnology: Findings from two experiments concerning Brain Stimulation Devices (BSDs) and Brain-Computer Interfaces (BCIs).}, journal = {PloS one}, volume = {17}, number = {11}, pages = {e0275454}, pmid = {36350815}, issn = {1932-6203}, mesh = {*Brain-Computer Interfaces ; Public Opinion ; Stereotaxic Techniques ; Morals ; Brain/physiology ; }, abstract = {This study contributes to the emerging literature on public perceptions of neurotechnological devices (NTDs) in their medical and non-medical applications, depending on their invasiveness, framing effects, and interindividual differences related to personal needs and values. We conducted two web-based between-subject experiments (2×2×2) using a representative, nation-wide sample of the adult population in Germany. Using vignettes describing how two NTDs, brain stimulation devices (BSDs; NExperiment 1 = 1,090) and brain-computer interfaces (BCIs; NExperiment 2 = 1,089), function, we randomly varied the purpose (treatment vs. enhancement) and invasiveness (noninvasive vs. invasive) of the NTD, and assessed framing effects (variable order of assessing moral acceptability first vs. willingness to use first). We found a moderate moral acceptance and willingness to use BSDs and BCIs. Respondents preferred treatment over enhancement purposes and noninvasive over invasive devices. We also found a framing effect and explored the role of personal characteristics as indicators of personal needs and values (e.g., stress, religiosity, and gender). Our results suggest that the future demand for BSDs or BCIs may depend on the purpose, invasiveness, and personal needs and values. These insights can inform technology developers about the public's needs and concerns, and enrich legal and ethical debates.}, }
@article {pmid36349931, year = {2022}, author = {Zwerus, EL and van Deurzen, DFP and van den Bekerom, MPJ and The, B and Eygendaal, D}, title = {Distal Biceps Tendon Ruptures: Diagnostic Strategy Through Physical Examination.}, journal = {The American journal of sports medicine}, volume = {50}, number = {14}, pages = {3956-3962}, pmid = {36349931}, issn = {1552-3365}, mesh = {Humans ; Cohort Studies ; Reproducibility of Results ; *Physical Examination ; }, abstract = {BACKGROUND: Distinguishing a complete from a partial distal biceps tendon rupture is essential, as a complete rupture may require repair on short notice to restore function, whereas partial ruptures can be treated nonsurgically in most cases. Reliability of physical examination is crucial to determine the right workup and treatment in patients with a distal biceps tendon rupture.<br><br>PURPOSES: The primary aim of this study was to find a (combination of) test(s) that serves best to diagnose a complete rupture with certainty in the acute phase (&#x2264;1 month) without missing any complete ruptures. The secondary aims were to determine the best (combination of) test(s) to identify a chronic (>1 month) rupture of the distal biceps tendon and indicate additional imaging in case partial ruptures or tendinitis are suspected.<br><br>STUDY DESIGN: Cohort study (Diagnosis); Level of evidence, 2.<br><br>METHODS: A total of 86 patients with anterior elbow complaints or suspected distal biceps injury underwent standardized physical examination, including the Hook test, passive forearm pronation test, biceps crease interval (BCI), and biceps crease ratio. Diagnosis was confirmed intraoperatively (68 cases), by magnetic resonance imaging (13 cases), or by ultrasound (5 cases).<br><br>RESULTS: A combination of the Hook test and BCI (ie, both tests are positive) was most accurate for both acute and chronic ruptures but with a different purpose. For acute complete ruptures, sensitivity was 94% and specificity was 100%. In chronic cases, specificity was also 100%. Weakness on active supination and palpation of the tendon footprint provided excellent sensitivity of 100% for chronic complete ruptures and partial ruptures, respectively.<br><br>CONCLUSION: The combination of a positive Hook test and BCI serves best to accurately diagnose acute complete ruptures of the distal biceps tendon. Weakness on active supination and pain on palpation of the tendon footprint provide excellent sensitivity for chronic complete ruptures and partial ruptures. Using these tests in all suspected distal biceps ruptures allows a physician to refrain from imaging for a diagnostic purpose in certain cases, to limit treatment delay and thereby provide better treatment outcome, and to avoid hospital and social costs.}, }
@article {pmid36349662, year = {2023}, author = {Peterson, V and Merk, T and Bush, A and Nikulin, V and Kühn, AA and Neumann, WJ and Richardson, RM}, title = {Movement decoding using spatio-spectral features of cortical and subcortical local field potentials.}, journal = {Experimental neurology}, volume = {359}, number = {}, pages = {114261}, doi = {10.1016/j.expneurol.2022.114261}, pmid = {36349662}, issn = {1090-2430}, mesh = {Humans ; *Brain-Computer Interfaces ; Movement/physiology ; Electrocorticography ; Brain/physiology ; *Parkinson Disease/therapy ; }, abstract = {The first commercially sensing enabled deep brain stimulation (DBS) devices for the treatment of movement disorders have recently become available. In the future, such devices could leverage machine learning based brain signal decoding strategies to individualize and adapt therapy in real-time. As multi-channel recordings become available, spatial information may provide an additional advantage for informing machine learning models. To investigate this concept, we compared decoding performances from single channels vs. spatial filtering techniques using intracerebral multitarget electrophysiology in Parkinson's disease patients undergoing DBS implantation. We investigated the feasibility of spatial filtering in invasive neurophysiology and the putative utility of combined cortical ECoG and subthalamic local field potential signals for decoding grip-force, a well-defined and continuous motor readout. We found that adding spatial information to the model can improve decoding (6% gain in decoding), but the spatial patterns and additional benefit was highly individual. Beyond decoding performance results, spatial filters and patterns can be used to obtain meaningful neurophysiological information about the brain networks involved in target behavior. Our results highlight the importance of individualized approaches for brain signal decoding, for which multielectrode recordings and spatial filtering can improve precision medicine approaches for clinical brain computer interfaces.}, }
@article {pmid36349568, year = {2023}, author = {Khademi, Z and Ebrahimi, F and Kordy, HM}, title = {A review of critical challenges in MI-BCI: From conventional to deep learning methods.}, journal = {Journal of neuroscience methods}, volume = {383}, number = {}, pages = {109736}, doi = {10.1016/j.jneumeth.2022.109736}, pmid = {36349568}, issn = {1872-678X}, mesh = {Humans ; *Brain-Computer Interfaces ; *Deep Learning ; Electroencephalography/methods ; Signal Processing, Computer-Assisted ; Algorithms ; Imagination ; }, abstract = {Brain-computer interfaces (BCIs) have achieved significant success in controlling external devices through the Electroencephalogram (EEG) signal processing. BCI-based Motor Imagery (MI) system bridges brain and external devices as communication tools to control, for example, wheelchair for people with disabilities, robotic control, and exoskeleton control. This success largely depends on the machine learning (ML) approaches like deep learning (DL) models. DL algorithms provide effective and powerful models to analyze compact and complex EEG data optimally for MI-BCI applications. DL models with CNN network have revolutionized computer vision through end-to-end learning from raw data. Meanwhile, RNN networks have been able to decode EEG signals by processing sequences of time series data. However, many challenges in the MI-BCI field have affected the performance of DL models. A major challenge is the individual differences in the EEG signal of different subjects. Therefore, the model must be retrained from the scratch for each new subject, which leads to computational costs. Analyzing the EEG signals is challenging due to its low signal to noise ratio and non-stationary nature. Additionally, limited size of existence datasets can lead to overfitting which can be prevented by using transfer learning (TF) approaches. The main contributions of this study are discovering major challenges in the MI-BCI field by reviewing the state of art machine learning models and then suggesting solutions to address these challenges by focusing on feature selection, feature extraction and classification methods.}, }
@article {pmid36346867, year = {2022}, author = {Fu, K and Du, C and Wang, S and He, H}, title = {Multi-View Multi-Label Fine-Grained Emotion Decoding From Human Brain Activity.}, journal = {IEEE transactions on neural networks and learning systems}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNNLS.2022.3217767}, pmid = {36346867}, issn = {2162-2388}, abstract = {Decoding emotional states from human brain activity play an important role in the brain-computer interfaces. Existing emotion decoding methods still have two main limitations: one is only decoding a single emotion category from a brain activity pattern and the decoded emotion categories are coarse-grained, which is inconsistent with the complex emotional expression of humans; the other is ignoring the discrepancy of emotion expression between the left and right hemispheres of the human brain. In this article, we propose a novel multi-view multi-label hybrid model for fine-grained emotion decoding (up to 80 emotion categories) which can learn the expressive neural representations and predict multiple emotional states simultaneously. Specifically, the generative component of our hybrid model is parameterized by a multi-view variational autoencoder, in which we regard the brain activity of left and right hemispheres and their difference as three distinct views and use the product of expert mechanism in its inference network. The discriminative component of our hybrid model is implemented by a multi-label classification network with an asymmetric focal loss. For more accurate emotion decoding, we first adopt a label-aware module for emotion-specific neural representation learning and then model the dependency of emotional states by a masked self-attention mechanism. Extensive experiments on two visually evoked emotional datasets show the superiority of our method.}, }
@article {pmid36346232, year = {2022}, author = {Sake, SM and Kosch, C and Blockus, S and Haid, S and Gunesch, AP and Zhang, X and Friesland, M and Trummer, SB and Grethe, C and Kühnel, A and Rückert, J and Duprex, WP and Huang, J and Rameix-Welti, MA and Empting, M and Fischer, N and Hirsch, AKH and Schulz, TF and Pietschmann, T}, title = {Respiratory Syncytial Virus Two-Step Infection Screen Reveals Inhibitors of Early and Late Life Cycle Stages.}, journal = {Antimicrobial agents and chemotherapy}, volume = {}, number = {}, pages = {e0103222}, doi = {10.1128/aac.01032-22}, pmid = {36346232}, issn = {1098-6596}, abstract = {Human respiratory syncytial virus (hRSV) infection is a leading cause of severe respiratory tract infections. Effective, directly acting antivirals against hRSV are not available. We aimed to discover new and chemically diverse candidates to enrich the hRSV drug development pipeline. We used a two-step screen that interrogates compound efficacy after primary infection and a consecutive virus passaging. We resynthesized selected hit molecules and profiled their activities with hRSV lentiviral pseudotype cell entry, replicon, and time-of-addition assays. The breadth of antiviral activity was tested against recent RSV clinical strains and human coronavirus (hCoV-229E), and in pseudotype-based entry assays with non-RSV viruses. Screening 6,048 molecules, we identified 23 primary candidates, of which 13 preferentially scored in the first and 10 in the second rounds of infection, respectively. Two of these molecules inhibited hRSV cell entry and selected for F protein resistance within the fusion peptide. One molecule inhibited transcription/replication in hRSV replicon assays, did not select for phenotypic hRSV resistance and was active against non-hRSV viruses, including hCoV-229E. One compound, identified in the second round of infection, did not measurably inhibit hRSV cell entry or replication/transcription. It selected for two coding mutations in the G protein and was highly active in differentiated BCi-NS1.1 lung cells. In conclusion, we identified four new hRSV inhibitor candidates with different modes of action. Our findings build an interesting platform for medicinal chemistry-guided derivatization approaches followed by deeper phenotypical characterization in vitro and in vivo with the aim of developing highly potent hRSV drugs.}, }
@article {pmid36343405, year = {2022}, author = {Pu, X and Yi, P and Chen, K and Ma, Z and Zhao, D and Ren, Y}, title = {EEGDnet: Fusing non-local and local self-similarity for EEG signal denoising with transformer.}, journal = {Computers in biology and medicine}, volume = {151}, number = {Pt A}, pages = {106248}, doi = {10.1016/j.compbiomed.2022.106248}, pmid = {36343405}, issn = {1879-0534}, mesh = {*Electroencephalography/methods ; Artifacts ; *Brain-Computer Interfaces ; Neural Networks, Computer ; Muscles ; Algorithms ; }, abstract = {Electroencephalogram (EEG) has shown a useful approach to produce a brain-computer interface (BCI). One-dimensional (1-D) EEG signal is yet easily disturbed by certain artifacts (a.k.a. noise) due to the high temporal resolution. Thus, it is crucial to remove the noise in received EEG signal. Recently, deep learning-based EEG signal denoising approaches have achieved impressive performance compared with traditional ones. It is well known that the characteristics of self-similarity (including non-local and local ones) of data (e.g., natural images and time-domain signals) are widely leveraged for denoising. However, existing deep learning-based EEG signal denoising methods ignore either the non-local self-similarity (e.g., 1-D convolutional neural network) or local one (e.g., fully connected network and recurrent neural network). To address this issue, we propose a novel 1-D EEG signal denoising network with 2-D transformer, namely EEGDnet. Specifically, we comprehensively take into account the non-local and local self-similarity of EEG signal through the transformer module. By fusing non-local self-similarity in self-attention blocks and local self-similarity in feed forward blocks, the negative impact caused by noises and outliers can be reduced significantly. Extensive experiments show that, compared with other state-of-the-art models, EEGDnet achieves much better performance in terms of both quantitative and qualitative metrics. Specifically, EEGDnet can achieve 18% and 11% improvements in correlation coefficients when removing ocular artifacts and muscle artifacts, respectively.}, }
@article {pmid36343004, year = {2022}, author = {Zhang, D and Liu, S and Zhang, J and Li, G and Suo, D and Liu, T and Luo, J and Ming, Z and Wu, J and Yan, T}, title = {Brain-Controlled 2D Navigation Robot Based on a Spatial Gradient Controller and Predictive Environmental Coordinator.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2022.3219812}, pmid = {36343004}, issn = {2168-2208}, abstract = {OBJECTIVE: Brain-computer interfaces (BCIs) have been used in two-dimensional (2D) navigation robotic devices, such as brain-controlled wheelchairs and brain-controlled vehicles. However, contemporary BCI systems are driven by binary selective control. On the one hand, only directional information can be transferred from humans to machines, such as "turn left" or "turn right", which means that the quantified value, such as the radius of gyration, cannot be controlled. In this study, we proposed a spatial gradient BCI controller and corresponding environment coordinator, by which the quantified value of brain commands can be transferred in the form of a 2D vector, improving the flexibility, stability and efficiency of BCIs.<br><br>METHODS: A horizontal array of steady-state visual stimulation was arranged to excite subject (EEG) signals. Covariance arrays between subjects' electroencephalogram (EEG) and stimulation features were mapped into quantified 2-dimensional vectors. The generated vectors were then inputted into the predictive controller and fused with virtual forces generated by the robot's predictive environment coordinator in the form of vector calculation. The resultant vector was then interpreted into the driving force for the robot, and real-time speed feedback was generated.<br><br>RESULTS: The proposed SGC controller generated a faster (27.4 s vs. 34.9 s) response for the single-obstacle avoidance task than the selective control approach. In practical multiobstacle tasks, the proposed robot executed 39% faster in the target-reaching tasks than the selective controller and had better robustness in multiobstacle avoidance tasks (average failures significantly dropped from 27% to 4%).<br><br>SIGNIFICANCE: This research proposes a new form of brain-machine shared control strategy that quantifies brain commands in the form of a 2-D control vector stream rather than selective constant values. Combined with a predictive environment coordinator, the brain-controlled strategy of the robot is optimized and provided with higher flexibility. The proposed controller can be used in brain-controlled 2D navigation devices, such as brain-controlled wheelchairs and vehicles.}, }
@article {pmid36340769, year = {2022}, author = {Lee, HS and Schreiner, L and Jo, SH and Sieghartsleitner, S and Jordan, M and Pretl, H and Guger, C and Park, HS}, title = {Individual finger movement decoding using a novel ultra-high-density electroencephalography-based brain-computer interface system.}, journal = {Frontiers in neuroscience}, volume = {16}, number = {}, pages = {1009878}, pmid = {36340769}, issn = {1662-4548}, abstract = {Brain-Computer Interface (BCI) technology enables users to operate external devices without physical movement. Electroencephalography (EEG) based BCI systems are being actively studied due to their high temporal resolution, convenient usage, and portability. However, fewer studies have been conducted to investigate the impact of high spatial resolution of EEG on decoding precise body motions, such as finger movements, which are essential in activities of daily living. Low spatial sensor resolution, as found in common EEG systems, can be improved by omitting the conventional standard of EEG electrode distribution (the international 10-20 system) and ordinary mounting structures (e.g., flexible caps). In this study, we used newly proposed flexible electrode grids attached directly to the scalp, which provided ultra-high-density EEG (uHD EEG). We explored the performance of the novel system by decoding individual finger movements using a total of 256 channels distributed over the contralateral sensorimotor cortex. Dense distribution and small-sized electrodes result in an inter-electrode distance of 8.6 mm (uHD EEG), while that of conventional EEG is 60 to 65 mm on average. Five healthy subjects participated in the experiment, performed single finger extensions according to a visual cue, and received avatar feedback. This study exploits mu (8-12 Hz) and beta (13-25 Hz) band power features for classification and topography plots. 3D ERD/S activation plots for each frequency band were generated using the MNI-152 template head. A linear support vector machine (SVM) was used for pairwise finger classification. The topography plots showed regular and focal post-cue activation, especially in subjects with optimal signal quality. The average classification accuracy over subjects was 64.8 (6.3)%, with the middle versus ring finger resulting in the highest average accuracy of 70.6 (9.4)%. Further studies are required using the uHD EEG system with real-time feedback and motor imagery tasks to enhance classification performance and establish the basis for BCI finger movement control of external devices.}, }
@article {pmid36340754, year = {2022}, author = {Tang, C and Gao, T and Li, Y and Chen, B}, title = {EEG channel selection based on sequential backward floating search for motor imagery classification.}, journal = {Frontiers in neuroscience}, volume = {16}, number = {}, pages = {1045851}, pmid = {36340754}, issn = {1662-4548}, abstract = {Brain-computer interfaces (BCIs) based on motor imagery (MI) utilizing multi-channel electroencephalogram (EEG) data are commonly used to improve motor function of people with motor disabilities. EEG channel selection can enhance MI classification accuracy by selecting informative channels, accordingly reducing redundant information. The sequential backward floating search (SBFS) approach has been considered as one of the best feature selection methods. In this paper, SBFS is first implemented to select the optimal EEG channels in MI-BCI. Further, to reduce the time complexity of SBFS, the modified SBFS is proposed and applied to left and right hand MI tasks. In the modified SBFS, based on the map of EEG channels at the scalp, the symmetrical channels are selected as channel pairs and acceleration is thus realized by removing or adding multiple channels in each iteration. Extensive experiments were conducted on four public BCI datasets. Experimental results show that the SBFS achieves significantly higher classification accuracy (p < 0.001) than using all channels and conventional MI channels (i.e., C3, C4, and Cz). Moreover, the proposed method outperforms the state-of-the-art selection methods.}, }
@article {pmid36337859, year = {2022}, author = {He, C and Du, Y and Zhao, X}, title = {A separable convolutional neural network-based fast recognition method for AR-P300.}, journal = {Frontiers in human neuroscience}, volume = {16}, number = {}, pages = {986928}, pmid = {36337859}, issn = {1662-5161}, abstract = {Augmented reality-based brain-computer interface (AR-BCI) has a low signal-to-noise ratio (SNR) and high real-time requirements. Classical machine learning algorithms that improve the recognition accuracy through multiple averaging significantly affect the information transfer rate (ITR) of the AR-SSVEP system. In this study, a fast recognition method based on a separable convolutional neural network (SepCNN) was developed for an AR-based P300 component (AR-P300). SepCNN achieved single extraction of AR-P300 features and improved the recognition speed. A nine-target AR-P300 single-stimulus paradigm was designed to be administered with AR holographic glasses to verify the effectiveness of SepCNN. Compared with four classical algorithms, SepCNN significantly improved the average target recognition accuracy (81.1%) and information transmission rate (57.90 bits/min) of AR-P300 single extraction. SepCNN with single extraction also attained better results than classical algorithms with multiple averaging.}, }
@article {pmid36337857, year = {2022}, author = {Floreani, ED and Rowley, D and Kelly, D and Kinney-Lang, E and Kirton, A}, title = {On the feasibility of simple brain-computer interface systems for enabling children with severe physical disabilities to explore independent movement.}, journal = {Frontiers in human neuroscience}, volume = {16}, number = {}, pages = {1007199}, pmid = {36337857}, issn = {1662-5161}, abstract = {INTRODUCTION: Children with severe physical disabilities are denied their fundamental right to move, restricting their development, independence, and participation in life. Brain-computer interfaces (BCIs) could enable children with complex physical needs to access power mobility (PM) devices, which could help them move safely and independently. BCIs have been studied for PM control for adults but remain unexamined in children. In this study, we explored the feasibility of BCI-enabled PM control for children with severe physical disabilities, assessing BCI performance, standard PM skills and tolerability of BCI.<br><br>MATERIALS AND METHODS: Patient-oriented pilot trial. Eight children with quadriplegic cerebral palsy attended two sessions where they used a simple, commercial-grade BCI system to activate a PM trainer device. Performance was assessed through controlled activation trials (holding the PM device still or activating it upon verbal and visual cueing), and basic PM skills (driving time, number of activations, stopping) were assessed through distance trials. Setup and calibration times, headset tolerability, workload, and patient/caregiver experience were also evaluated.<br><br>RESULTS: All participants completed the study with favorable tolerability and no serious adverse events or technological challenges. Average control accuracy was 78.3 &#xb1; 12.1%, participants were more reliably able to activate (95.7 &#xb1; 11.3%) the device than hold still (62.1 &#xb1; 23.7%). Positive trends were observed between performance and prior BCI experience and age. Participants were able to drive the PM device continuously an average of 1.5 meters for 3.0 s. They were able to stop at a target 53.1 &#xb1; 23.3% of the time, with significant variability. Participants tolerated the headset well, experienced mild-to-moderate workload and setup/calibration times were found to be practical. Participants were proud of their performance and both participants and families were eager to participate in future power mobility sessions.<br><br>DISCUSSION: BCI-enabled PM access appears feasible in disabled children based on evaluations of performance, tolerability, workload, and setup/calibration. Performance was comparable to existing pediatric BCI literature and surpasses established cut-off thresholds (70%) of "effective" BCI use. Participants exhibited PM skills that would categorize them as "emerging operational learners." Continued exploration of BCI-enabled PM for children with severe physical disabilities is justified.}, }
@article {pmid36337363, year = {2022}, author = {Li, L}, title = {Preface to special topic on brain-machine interface.}, journal = {National science review}, volume = {9}, number = {10}, pages = {nwac211}, doi = {10.1093/nsr/nwac211}, pmid = {36337363}, issn = {2053-714X}, }
@article {pmid36337269, year = {2022}, author = {Song, X and Zeng, Y and Tong, L and Shu, J and Yang, Q and Kou, J and Sun, M and Yan, B}, title = {Corrigendum to "A Collaborative Brain-Computer Interface Framework for Enhancing Group Detection Performance of Dynamic Visual Targets".}, journal = {Computational intelligence and neuroscience}, volume = {2022}, number = {}, pages = {9819804}, pmid = {36337269}, issn = {1687-5273}, abstract = {[This corrects the article DOI: 10.1155/2022/4752450.].}, }
@article {pmid36335198, year = {2022}, author = {Smrdel, A}, title = {Use of common spatial patterns for early detection of Parkinson's disease.}, journal = {Scientific reports}, volume = {12}, number = {1}, pages = {18793}, pmid = {36335198}, issn = {2045-2322}, mesh = {Humans ; *Parkinson Disease/diagnosis ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Brain ; Early Diagnosis ; }, abstract = {One of the most common diseases that affects human brain is Parkinson's disease. Detection of Parkinson's disease (PD) poses a serious challenge. Robust methods for feature extraction allowing separation between the electroencephalograms (EEG) of healthy subjects and PD patients are required. We used the EEG records of healthy subjects and PD patients which were subject to auditory tasks. We used the common spatial patterns (CSP) and Laplacian mask as methods to allow robust selection and extraction of features. We used the derived CSP whitening matrix to determine those channels that are the most promising in the terms of differentiating between EEGs of healthy controls and of PD patients. Using the selection of features calculated using the CSP we managed to obtain the classification accuracy of 85% when classifying EEG records belonging to groups of controls or PD patients. Using the features calculated using the Laplacian operator we obtained the classification accuracy of 90%. Diagnosing the PD in early stages using EEG is possible. The CSP proved to be a promising technique to detect informative channels and to separate between the groups. Use of the combination of features calculated using the Laplacian offers good separability between the two groups.}, }
@article {pmid36335102, year = {2022}, author = {Zhao, LH and Lin, J and Ji, SY and Zhou, XE and Mao, C and Shen, DD and He, X and Xiao, P and Sun, J and Melcher, K and Zhang, Y and Yu, X and Xu, HE}, title = {Structure insights into selective coupling of G protein subtypes by a class B G protein-coupled receptor.}, journal = {Nature communications}, volume = {13}, number = {1}, pages = {6670}, pmid = {36335102}, issn = {2041-1723}, mesh = {*Heterotrimeric GTP-Binding Proteins/metabolism ; Urocortins/metabolism ; }, abstract = {The ability to couple with multiple G protein subtypes, such as Gs, Gi/o, or Gq/11, by a given G protein-coupled receptor (GPCR) is critical for many physiological processes. Over the past few years, the cryo-EM structures for all 15 members of the medically important class B GPCRs, all in complex with Gs protein, have been determined. However, no structure of class B GPCRs with Gq/11 has been solved to date, limiting our understanding of the precise mechanisms of G protein coupling selectivity. Here we report the structures of corticotropin releasing factor receptor 2 (CRF2R) bound to Urocortin 1 (UCN1), coupled with different classes of heterotrimeric G proteins, G11 and Go. We compare these structures with the structure of CRF2R in complex with Gs to uncover the structural differences that determine the selective coupling of G protein subtypes by CRF2R. These results provide important insights into the structural basis for the ability of CRF2R to couple with multiple G protein subtypes.}, }
@article {pmid36333482, year = {2022}, author = {Gao, Z and Pang, Z and Chen, Y and Lei, G and Zhu, S and Li, G and Shen, Y and Xu, W}, title = {Restoring After Central Nervous System Injuries: Neural Mechanisms and Translational Applications of Motor Recovery.}, journal = {Neuroscience bulletin}, volume = {38}, number = {12}, pages = {1569-1587}, pmid = {36333482}, issn = {1995-8218}, mesh = {Animals ; *Spinal Cord Injuries/therapy ; Motor Neurons/physiology ; Brain ; *Stroke ; Recovery of Function/physiology ; }, abstract = {Central nervous system (CNS) injuries, including stroke, traumatic brain injury, and spinal cord injury, are leading causes of long-term disability. It is estimated that more than half of the survivors of severe unilateral injury are unable to use the denervated limb. Previous studies have focused on neuroprotective interventions in the affected hemisphere to limit brain lesions and neurorepair measures to promote recovery. However, the ability to increase plasticity in the injured brain is restricted and difficult to improve. Therefore, over several decades, researchers have been prompted to enhance the compensation by the unaffected hemisphere. Animal experiments have revealed that regrowth of ipsilateral descending fibers from the unaffected hemisphere to denervated motor neurons plays a significant role in the restoration of motor function. In addition, several clinical treatments have been designed to restore ipsilateral motor control, including brain stimulation, nerve transfer surgery, and brain-computer interface systems. Here, we comprehensively review the neural mechanisms as well as translational applications of ipsilateral motor control upon rehabilitation after CNS injuries.}, }
@article {pmid36332422, year = {2022}, author = {Zhan, Q and Wang, L and Ren, L and Huang, X}, title = {A novel heterogeneous transfer learning method based on data stitching for the sequential coding brain computer interface.}, journal = {Computers in biology and medicine}, volume = {151}, number = {Pt A}, pages = {106220}, doi = {10.1016/j.compbiomed.2022.106220}, pmid = {36332422}, issn = {1879-0534}, mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Algorithms ; Machine Learning ; Imagination ; }, abstract = {OBJECTIVE: For the brain computer interface (BCI), it is necessary to collect enough electroencephalography (EEG) signals to train the classification model. When the operation dimension of BCI is large, it will bring great burden to data acquisition. Fortunately, this problem can be solved by our proposed transfer learning method.<br><br>METHOD: For the sequential coding experimental paradigm, the multi-band data stitching with label alignment and tangent space mapping (MDSLATSM) algorithm is proposed as a novel heterogeneous transfer learning method. After filtering by multi-band filtering, the artificial signals can be obtained by data stitching from the source domain, which build a bridge between the source domain and target domain. To make the distribution of two domains closer, their covariance matrices are aligned by label alignment. After mapping to the tangent space, the features are extracted from the Riemannian manifold. Finally, the classification results are obtained with feature selection and classification.<br><br>RESULTS: Our data set includes the EEG signals from 16 subjects. For the heterogeneous transfer learning of cross-label, the average classification accuracy is 78.28%. MDSLATSM is also tested for cross-subject, and the average classification accuracy is 64.01%, which is better than existing methods.<br><br>SIGNIFICANCE: Combining multi-band filtering, data stitching, label alignment and tangent space mapping, a novel heterogeneous transfer learning method can be achieved with superior performance, which promotes the practical application of the BCI systems.}, }
@article {pmid36331650, year = {2022}, author = {Zhang, S and Wu, L and Yu, S and Shi, E and Qiang, N and Gao, H and Zhao, J and Zhao, S}, title = {An Explainable and Generalizable Recurrent Neural Network Approach for Differentiating Human Brain States on EEG Dataset.}, journal = {IEEE transactions on neural networks and learning systems}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNNLS.2022.3214225}, pmid = {36331650}, issn = {2162-2388}, abstract = {Electroencephalogram (EEG) is one of the most widely used brain computer interface (BCI) approaches. Despite the success of existing EEG approaches in brain state recognition studies, it is still challenging to differentiate brain states via explainable and generalizable deep learning approaches. In other words, how to explore meaningful and distinguishing features and how to overcome the huge variability and overfitting problem still need to be further studied. To alleviate these challenges, in this work, a multiple random fragment search-based multilayer recurrent neural network (MRFS-MRNN) is proposed to improve the differentiating performance and explore meaningful patterns. Specifically, an explainable MRNN module is proposed to capture the temporal dependences preserved in EEG time series. Besides, a MRFS module is designed to cut multiple random fragments from the entire EEG signal time course to improve the effectiveness of brain state differentiating ability. MRFS-MRNN is concatenatedto effectively overcome the huge variabilities and overfitting problems. Experiment results demonstrate that the proposed MRFS-MRNN model not only has excellent differentiating performance, but also has good explanation and generalization ability. The classification accuracies reach as high as 95.18% for binary classification and 89.19% for four-category classification on the individual level. Similarly, 95.53% and 85.84% classification accuracies are obtained for the binary and four-category classification on the group level. What's more, 94.28% and 85.43% classification accuracies of binary and four-category classifications are achieved for predicting brand new subjects. The experiment results showed that the proposed method outperformed other state-of-the-art (SOTA) models on the same underlying data and improved the explanation and generalization ability.}, }
@article {pmid36331633, year = {2022}, author = {Wang, R and Liu, Y and Shi, J and Peng, B and Fei, W and Bi, L}, title = {Sound Target Detection under Noisy Environment Using Brain-Computer Interface.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2022.3219595}, pmid = {36331633}, issn = {1558-0210}, abstract = {As an important means of environmental reconnaissance and regional security protection, sound target detection (STD) has been widely studied in the field of machine learning for a long time. Considering the shortcomings of the robustness and generalization performance of existing methods based on machine learning, we proposed a target detection method by an auditory brain-computer interface (BCI). We designed the experimental paradigm according to the actual application scenarios of STD, recorded the changes in Electroencephalogram (EEG) signals during the process of detecting target sound, and further extracted the features used to decode EEG signals through the analysis of neural representations, including Event-Related Potential (ERP) and Event-Related Spectral Perturbation (ERSP). Experimental results showed that the proposed method achieved good detection performance under noisy environment. As the first study of BCI applied to STD, this study shows the feasibility of this scheme in BCI and can serve as the foundation for future related applications.}, }
@article {pmid36329083, year = {2022}, author = {Mencel, J and Marusiak, J and Jaskólska, A and Kamiński, &#x141; and Kurzyński, M and Wołczowski, A and Jaskólski, A and Kisiel-Sajewicz, K}, title = {Motor imagery training of goal-directed reaching in relation to imagery of reaching and grasping in healthy people.}, journal = {Scientific reports}, volume = {12}, number = {1}, pages = {18610}, pmid = {36329083}, issn = {2045-2322}, mesh = {Humans ; Brain-Computer Interfaces ; Electroencephalography ; Goals ; *Hand Strength/physiology ; Imagination/physiology ; *Motor Skills/physiology ; Evoked Potentials/physiology ; *Imagery, Psychotherapy/education ; }, abstract = {The study aimed to determine whether four weeks of motor imagery training (MIT) of goal-directed reaching (reaching to grasp task) would affect the cortical activity during motor imagery of reaching (MIR) and grasping (MIG) in the same way. We examined cortical activity regarding event-related potentials (ERPs) in healthy young participants. Our study also evaluated the subjective vividness of the imagery. Furthermore, we aimed to determine the relationship between the subjective assessment of motor imagery (MI) ability to reach and grasp and the cortical activity during those tasks before and after training to understand the underlying neuroplasticity mechanisms. Twenty-seven volunteers participated in MIT of goal-directed reaching and two measurement sessions before and after MIT. During the sessions 128-channel electroencephalography (EEG) was recorded during MIR and MIG. Also, participants assessed the vividness of the MI tasks using a visual analog scale (VAS). The vividness of imagination improved significantly (P < .05) after MIT. A repeated measures ANOVA showed that the task (MIR/MIG) and the location of electrodes had a significant effect on the ERP's amplitude (P < .05). The interaction between the task, location, and session (before/after MIT) also had a significant effect on the ERP's amplitude (P < .05). Finally, the location of electrodes and the interaction between location and session had a significant effect on the ERP's latency (P < .05). We found that MIT influenced the EEG signal associated with reaching differently than grasping. The effect was more pronounced for MIR than for MIG. Correlation analysis showed that changes in the assessed parameters due to MIT reduced the relationship between the subjective evaluation of imagining and the EEG signal. This finding means that the subjective evaluation of imagining cannot be a simple, functional insight into the bioelectrical activity of the cerebral cortex expressed by the ERPs in mental training. The changes we noted in ERPs after MIT may benefit the use of non-invasive EEG in the brain-computer interface (BCI) context.Trial registration: NCT04048083.}, }
@article {pmid36327603, year = {2022}, author = {Fu, Y and Zhu, Y and Zhang, Y and Hu, S}, title = {Is AlphaFold a perfect experimental assistant of psychiatric drug discovery in precision psychiatry era?.}, journal = {Asian journal of psychiatry}, volume = {78}, number = {}, pages = {103305}, doi = {10.1016/j.ajp.2022.103305}, pmid = {36327603}, issn = {1876-2026}, mesh = {Humans ; *Psychiatry ; *Mental Disorders/drug therapy ; Drug Discovery ; }, }
@article {pmid36323230, year = {2022}, author = {Al-Sheikh, U and Kang, L}, title = {Kir2.1 channel: Macrophage plasticity in tumor microenvironment.}, journal = {Cell metabolism}, volume = {34}, number = {11}, pages = {1613-1615}, doi = {10.1016/j.cmet.2022.10.009}, pmid = {36323230}, issn = {1932-7420}, mesh = {Humans ; *Tumor Microenvironment ; Macrophages/metabolism ; *Neoplasms/metabolism ; }, abstract = {Diverse ion channels have dysregulated functional expression in the tumor microenvironment (TME). In this issue of Cell Metabolism, Chen et al. reveal that high intratumoral K[+] ions restrict the plasticity of tumor-associated macrophages (TAMs). Inhibition of the Kir2.1 potassium channel induced metabolic reprogramming and repolarization of pro-tumor M2-TAMs to tumoricidal M1-like states.}, }
@article {pmid36318565, year = {2022}, author = {Bian, Y and Zhao, L and Li, J and Guo, T and Fu, X and Qi, H}, title = {Improvements in classification of left and right foot motor intention using modulated steady-state somatosensory evoked potential induced by electrical stimulation and motor imagery.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2022.3218682}, pmid = {36318565}, issn = {1558-0210}, abstract = {In recent years, motor imagery-based brain-computer interface (MI-BCI) has been applied to motor rehabilitation in patients with motor dysfunction. However, traditional MI-BCI is rarely used for foot motor intention recognition because the motor cortex regions of both feet are anatomically close to each other, and traditional event-related desynchronization (ERD) patterns for MI-BCI have insufficient spatial discrimination. This study introduced steady-state somatosensory evoked potentials (SSSEPs) by synchronous bilateral feet electrical stimulation at different frequencies, which were used as carrier signals modulated by unilateral foot motor intention. Fifteen subjects participated in MI and MI-SSSEP tasks. A Riemannian geometry classifier with a task-related component analysis (TRCA) spatial filter was proposed to demodulate the variation in SSSEP features and discriminate the left and right foot motor intentions. The feature outcomes indicated that the amplitude and phase synchronization of the SSSEPs could be well modulated by unilateral foot MI tasks under the MI-SSSEP paradigm. The classification results revealed that the modulated SSSEP features played an important role in the recognition of left-right foot discrimination. Moreover, the proposed TRCA-based method outperformed the other three methods and improved the foot average classification accuracy to 81.07±13.29%, with the highest accuracy attained at 97.00%. Compared with the traditional MI paradigm, the foot motor intention recognition accuracy of the MI-SSSEP paradigm was significantly improved, from nearly 60% to more than 80%. This work provides a practical method for left-right foot motor intention recognition and expands the application of MI-BCI in the field of lower-extremity motor function rehabilitation.}, }
@article {pmid36318386, year = {2022}, author = {Gorur, K and Eraslan, B}, title = {The single-channel dry electrode SSVEP-based biometric approach: data augmentation techniques against overfitting for RNN-based deep models.}, journal = {Physical and engineering sciences in medicine}, volume = {45}, number = {4}, pages = {1219-1240}, pmid = {36318386}, issn = {2662-4737}, mesh = {Humans ; *Brain-Computer Interfaces ; Evoked Potentials/physiology ; Neural Networks, Computer ; Electrodes ; Biometry ; }, abstract = {Biometric studies based on electroencephalography (EEG) have received increasing attention because each individual has a dynamic and unique pattern. However, classic EEG-based biometrics have significant deficiencies, including noise-prone signals, gel-based electrodes, and the need for multi-training/multi-channel acquisition and high mental effort. In contrast, steady-state visually evoked potential (SSVEP)-based biometrics have the important advantages of high signal-to-noise ratio and untrained usage. Dynamic brain potential responses are a natural subconscious activity and can be elicited by flickering lights having distinct frequencies, such as cell phone flashes, without extra physical or mental effort. Few studies involving multi-channel/multi-trial SSVEP-based biometric research are available in the current literature. Moreover, there is a lack of research comparing them to the single-channel single-trial dry electrode-implemented SSVEP-based biometric approach using Recurrent Neural Networks (RNN). Furthermore, to the best of our knowledge, no prior work has proposed an SSVEP-based biometric comparison of the RNNs using data augmentation strategies against overfitting. It was observed that the biometric recognition results were promising, achieving up to 100% accuracy and > 97% sensitivity and specificity scores for 11 subjects. F-scores were also yielded as > 97% values. This single-channel SSVEP-based biometric approach using RNN deep models may offer low-cost, user-friendly, and reliable individual identification authentication, leading to significant application domains.}, }
@article {pmid36317357, year = {2022}, author = {Ogino, M and Hamada, N and Mitsukura, Y}, title = {Simultaneous multiple-stimulus auditory brain-computer interface with semi-supervised learning and prior probability distribution tuning.}, journal = {Journal of neural engineering}, volume = {19}, number = {6}, pages = {}, doi = {10.1088/1741-2552/ac9edd}, pmid = {36317357}, issn = {1741-2552}, mesh = {*Brain-Computer Interfaces ; Acoustic Stimulation/methods ; Evoked Potentials ; Supervised Machine Learning ; Probability ; Electroencephalography/methods ; Event-Related Potentials, P300 ; }, abstract = {Objective.Auditory brain-computer interfaces (BCIs) enable users to select commands based on the brain activity elicited by auditory stimuli. However, existing auditory BCI paradigms cannot increase the number of available commands without decreasing the selection speed, because each stimulus needs to be presented independently and sequentially under the standard oddball paradigm. To solve this problem, we propose a double-stimulus paradigm that simultaneously presents multiple auditory stimuli.Approach.For addition to an existing auditory BCI paradigm, the best discriminable sound was chosen following a subjective assessment. The new sound was located on the right-hand side and presented simultaneously with an existing sound from the left-hand side. A total of six sounds were used for implementing the auditory BCI with a 6 × 6 letter matrix. We employ semi-supervised learning (SSL) and prior probability distribution tuning to improve the accuracy of the paradigm. The SSL method involved updating of the classifier weights, and their prior probability distributions were adjusted using the following three types of distributions: uniform, empirical, and extended empirical (e-empirical). The performance was evaluated based on the BCI accuracy and information transfer rate (ITR).Main results.The double-stimulus paradigm resulted in a BCI accuracy of 67.89 ± 11.46% and an ITR of 2.67 ± 1.09 bits min[-1], in the absence of SSL and with uniform distribution. The proposed combination of SSL with e-empirical distribution improved the BCI accuracy and ITR to 74.59 ± 12.12% and 3.37 ± 1.27 bits min[-1], respectively. The event-related potential analysis revealed that contralateral and right-hemispheric dominances contributed to the BCI performance improvement.Significance.Our study demonstrated that a BCI based on multiple simultaneous auditory stimuli, incorporating SSL and e-empirical prior distribution, can increase the number of commands without sacrificing typing speed beyond the acceptable level of accuracy.}, }
@article {pmid36317288, year = {2022}, author = {Jia, H and Sun, Z and Duan, F and Zhang, Y and Caiafa, CF and Solé-Casals, J}, title = {Improving pre-movement pattern detection with filter bank selection.}, journal = {Journal of neural engineering}, volume = {19}, number = {6}, pages = {}, doi = {10.1088/1741-2552/ac9e75}, pmid = {36317288}, issn = {1741-2552}, mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Support Vector Machine ; Movement ; Upper Extremity ; Algorithms ; Imagination ; }, abstract = {Objective. Pre-movement decoding plays an important role in detecting the onsets of actions using low-frequency electroencephalography (EEG) signals before the movement of an upper limb. In this work, a binary classification method is proposed between two different states.Approach. The proposed method, referred to as filter bank standard task-related component analysis (FBTRCA), is to incorporate filter bank selection into the standard task-related component analysis (STRCA) method. In FBTRCA, the EEG signals are first divided into multiple sub-bands which start at specific fixed frequencies and end frequencies that follow in an arithmetic sequence. The STRCA method is then applied to the EEG signals in these bands to extract CCPs. The minimum redundancy maximum relevance feature selection method is used to select essential features from these correlation patterns in all sub-bands. Finally, the selected features are classified using the binary support vector machine classifier. A convolutional neural network (CNN) is an alternative approach to select canonical correlation patterns.Main Results. Three methods were evaluated using EEG signals in the time window from 2 s before the movement onset to 1 s after the movement onset. In the binary classification between a movement state and the resting state, the FBTRCA achieved an average accuracy of 0.8968 ± 0.0847 while the accuracies of STRCA and CNN were 0.8228 ± 0.1149 and 0.8828 ± 0.0917, respectively. In the binary classification between two actions, the accuracies of STRCA, CNN, and FBTRCA were 0.6611 ± 0.1432, 0.6993 ± 0.1271, 0.7178 ± 0.1274, respectively. Feature selection using filter banks, as in FBTRCA, produces comparable results to STRCA.Significance. The proposed method provides a way to select filter banks in pre-movement decoding, and thus it improves the classification performance. The improved pre-movement decoding of single upper limb movements is expected to provide people with severe motor disabilities with a more natural, non-invasive control of their external devices.}, }
@article {pmid36317255, year = {2022}, author = {Guo, Z and Chen, F}, title = {Decoding lexical tones and vowels in imagined tonal monosyllables using fNIRS signals.}, journal = {Journal of neural engineering}, volume = {19}, number = {6}, pages = {}, doi = {10.1088/1741-2552/ac9e1d}, pmid = {36317255}, issn = {1741-2552}, mesh = {Humans ; *Speech ; Language ; *Speech Perception ; Imagery, Psychotherapy ; }, abstract = {Objective.Speech is a common way of communication. Decoding verbal intent could provide a naturalistic communication way for people with severe motor disabilities. Active brain computer interaction (BCI) speller is one of the most commonly used speech BCIs. To reduce the spelling time of Chinese words, identifying vowels and tones that are embedded in imagined Chinese words is essential. Functional near-infrared spectroscopy (fNIRS) has been widely used in BCI because it is portable, non-invasive, safe, low cost, and has a relatively high spatial resolution.Approach.In this study, an active BCI speller based on fNIRS is presented by covertly rehearsing tonal monosyllables with vowels (i.e. /a/, /i/, /o/, and /u/) and four lexical tones in Mandarin Chinese (i.e. tones 1, 2, 3, and 4) for 10 s.Main results.fNIRS results showed significant differences in the right superior temporal gyrus between imagined vowels with tone 2/3/4 and those with tone 1 (i.e. more activations and stronger connections to other brain regions for imagined vowels with tones 2/3/4 than for those with tone 1). Speech-related areas for tone imagery (i.e. the right hemisphere) provided majority of information for identifying tones, while the left hemisphere had advantages in vowel identification. Having decoded both vowels and tones during the post-stimulus 15 s period, the average classification accuracies exceeded 40% and 70% in multiclass (i.e. four classes) and binary settings, respectively. To spell words more quickly, the time window size for decoding was reduced from 15 s to 2.5 s while the classification accuracies were not significantly reduced.Significance.For the first time, this work demonstrated the possibility of discriminating lexical tones and vowels in imagined tonal syllables simultaneously. In addition, the reduced time window for decoding indicated that the spelling time of Chinese words could be significantly reduced in the fNIRS-based BCIs.}, }
@article {pmid36317254, year = {2022}, author = {Lee, C and Vaskov, AK and Gonzalez, MA and Vu, PP and Davis, AJ and Cederna, PS and Chestek, CA and Gates, DH}, title = {Use of regenerative peripheral nerve interfaces and intramuscular electrodes to improve prosthetic grasp selection: a case study.}, journal = {Journal of neural engineering}, volume = {19}, number = {6}, pages = {}, doi = {10.1088/1741-2552/ac9e1c}, pmid = {36317254}, issn = {1741-2552}, mesh = {Female ; Humans ; *Artificial Limbs ; Electrodes ; Electromyography/methods ; Hand/physiology ; Hand Strength ; *Muscle, Skeletal/physiology ; Peripheral Nerves/physiology ; }, abstract = {Objective.Advanced myoelectric hands enable users to select from multiple functional grasps. Current methods for controlling these hands are unintuitive and require frequent recalibration. This case study assessed the performance of tasks involving grasp selection, object interaction, and dynamic postural changes using intramuscular electrodes with regenerative peripheral nerve interfaces (RPNIs) and residual muscles.Approach.One female with unilateral transradial amputation participated in a series of experiments to compare the performance of grasp selection controllers with RPNIs and intramuscular control signals with controllers using surface electrodes. These experiments included a virtual grasp-matching task with and without a concurrent cognitive task and physical tasks with a prosthesis including standardized functional assessments and a functional assessment where the individual made a cup of coffee ('Coffee Task') that required grasp transitions.Main results.In the virtual environment, the participant was able to select between four functional grasps with higher accuracy using the RPNI controller (92.5%) compared to surface controllers (81.9%). With the concurrent cognitive task, performance of the virtual task was more consistent with RPNI controllers (reduced accuracy by 1.1%) compared to with surface controllers (4.8%). When RPNI signals were excluded from the controller with intramuscular electromyography (i.e. residual muscles only), grasp selection accuracy decreased by up to 24%. The participant completed the Coffee Task with 11.7% longer completion time with the surface controller than with the RPNI controller. She also completed the Coffee Task with 11 fewer transition errors out of a maximum of 25 total errors when using the RPNI controller compared to surface controller.Significance.The use of RPNI signals in concert with residual muscles and intramuscular electrodes can improve grasp selection accuracy in both virtual and physical environments. This approach yielded consistent performance without recalibration needs while reducing cognitive load associated with pattern recognition for myoelectric control (clinical trial registration number NCT03260400).}, }
@article {pmid36315547, year = {2022}, author = {Phang, CR and Chen, CH and Cheng, YY and Chen, YJ and Ko, LW}, title = {Frontoparietal Dysconnection in Covert Bipedal Activity for Enhancing the Performance of the Motor Preparation-based Brain-Computer Interface.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2022.3217298}, pmid = {36315547}, issn = {1558-0210}, abstract = {Motor-based brain-computer interfaces (BCIs) were developed from the brain signals during motor imagery (MI), motor preparation (MP), and motor execution (ME). Motor-based BCIs provide an active rehabilitation scheme for post-stroke patients. However, BCI based solely on MP was rarely investigated. Since MP is the precedence phase before MI or ME, MP-BCI could potentially detect brain commands at an earlier state. This study proposes a bipedal MP-BCI system, which is actuated by the reduction in frontoparietal connectivity strength. Three substudies, including bipedal classification, neurofeedback, and post-stroke analysis, were performed to validate the performance of our proposed model. In bipedal classification, functional connectivity was extracted by Pearson's correlation model from electroencephalogram (EEG) signals recorded while the subjects were performing MP and MI. The binary classification of MP achieved short-lived peak accuracy of 73.73(±7.99)% around 200-400 ms post-cue. The peak accuracy was found synchronized to the MP-related potential and the decrement in frontoparietal connection strength. The connection strengths of the right frontal and left parietal lobes in the alpha range were found negatively correlated to the classification accuracy. In the subjective neurofeedback study, the majority of subjects reported that motor preparation instead of the motor imagery activated the frontoparietal dysconnection. Post-stroke study also showed that patients exhibit lower frontoparietal connections compared to healthy subjects during both MP and ME phases. These findings suggest that MP reduced alpha band functional frontoparietal connectivity and the EEG signatures of left and right foot MP could be discriminated more effectively during this phase. A neurofeedback paradigm based on the frontoparietal network could also be utilized to evaluate post-stroke rehabilitation training.}, }
@article {pmid36315544, year = {2022}, author = {Gao, Y and Liu, Y and She, Q and Zhang, J}, title = {Domain Adaptive Algorithm Based on Multi-manifold Embedded Distributed Alignment for Brain-Computer Interfaces.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2022.3218453}, pmid = {36315544}, issn = {2168-2208}, abstract = {The use of transfer learning in brain-computer interfaces (BCIs) has potential applications. As electroencephalogram (EEG) signals vary among different paradigms and subjects, existing EEG transfer learning algorithms mainly focus on the alignment of the original space. They may not discover hidden details owing to the low-dimensional structure of EEG. To effectively transfer data from a source to target domain, a multi-manifold embedding domain adaptive algorithm is proposed for BCI. First, we aligned the EEG covariance matrix in the Riemannian manifold and extracted the characteristics of each source domain in the tangent space to reflect the differences between different source domains. Subsequently, we mapped the extracted characteristics to the Grassmann manifold to obtain a common feature representation. In domain adaptation, the geometric and statistical attributes of EEG data were considered simultaneously, and the target domain divergence matrix was updated with pseudo-labels to maximize the inter-class distance and minimize the intra-class distance. Datasets generated via BCIs were used to verify the effectiveness of the algorithm. Under two experimental paradigms, namely single-source to single-target and multi-source to single-target, the average accuracy of the algorithm on three datasets was 73.31% and 81.02%, respectively, which is more than that of several state-of-the-art EEG cross-domain classification approaches. Our multi-manifold embedded domain adaptive method achieved satisfactory results on EEG transfer learning. The method can achieve effective EEG classification without a same subject's training set.}, }
@article {pmid36315056, year = {2022}, author = {Pan, Y and Zhu, Y and Xu, C and Pan, C and Shi, Y and Zou, J and Li, Y and Hu, X and Zhou, B and Zhao, C and Gao, Q and Zhang, J and Wu, A and Chen, X and Li, J}, title = {Biomimetic Yolk-Shell Nanocatalysts for Activatable Dual-Modal-Image-Guided Triple-Augmented Chemodynamic Therapy of Cancer.}, journal = {ACS nano}, volume = {16}, number = {11}, pages = {19038-19052}, doi = {10.1021/acsnano.2c08077}, pmid = {36315056}, issn = {1936-086X}, mesh = {Humans ; Biomimetics ; Hydrogen Peroxide/metabolism ; Manganese Compounds/pharmacology ; Cell Line, Tumor ; Oxides ; *Neoplasms/diagnostic imaging/drug therapy ; Glutathione/metabolism ; Glucose Oxidase/metabolism ; *Nanoparticles ; Tumor Microenvironment ; }, abstract = {Fenton reaction-based chemodynamic therapy (CDT), which applies metal ions to convert less active hydrogen peroxide (H2O2) into more harmful hydroxyl peroxide (·OH) for tumor treatment, has attracted increasing interest recently. However, the CDT is substantially hindered by glutathione (GSH) scavenging effect on ·OH, low intracellular H2O2 level, and low reaction rate, resulting in unsatisfactory efficacy. Here, a cancer cell membrane (CM)-camouflaged Au nanorod core/mesoporous MnO2 shell yolk-shell nanocatalyst embedded with glucose oxidase (GOD) and Dox (denoted as AMGDC) is constructed for synergistic triple-augmented CDT and chemotherapy of tumor under MRI/PAI guidance. Benefiting from the homologous adhesion and immune escaping property of the cancer CM, the nanocatalysts can target tumor and gradually accumulate in tumor site. For triple-augmented CDT, first, the MnO2 shell reacts with intratumoral GSH to generate Mn[2+] and glutathione disulfide, which achieves Fenton-like ion delivery and weakening of GSH-mediated scavenging effect, leading to GSH depletion-enhanced CDT. Second, the intratumoral glucose can be oxidized to H2O2 and gluconic acid by GOD, achieving supplementary H2O2-enhanced CDT. Next, the AuNRs absorbing in NIR-II elevate the local tumor temperature upon NIR-II laser irradiation, achieving photothermal-enhanced CDT. Dox is rapidly released for adjuvant chemotherapy due to responsive degradation of MnO2 shell. Moreover, GSH-activated PAI/MRI can be used to monitor CDT process. This study provides a great paradigm for enhancing CDT-mediated antitumor efficacy.}, }
@article {pmid36313812, year = {2022}, author = {Cui, Y and Xie, S and Xie, X and Zhang, X and Liu, X}, title = {Dynamic probability integration for electroencephalography-based rapid serial visual presentation performance enhancement: Application in nighttime vehicle detection.}, journal = {Frontiers in computational neuroscience}, volume = {16}, number = {}, pages = {1006361}, pmid = {36313812}, issn = {1662-5188}, abstract = {BACKGROUND: Rapid serial visual presentation (RSVP) has become a popular target detection method by decoding electroencephalography (EEG) signals, owing to its sensitivity and effectiveness. Most current research on EEG-based RSVP tasks focused on feature extraction algorithms developed to deal with the non-stationarity and low signal-to-noise ratio (SNR) of EEG signals. However, these algorithms cannot handle the problem of no event-related potentials (ERP) component or miniature ERP components caused by the attention lapses of human vision in abnormal conditions. The fusion of human-computer vision can obtain complementary information, making it a promising way to become an efficient and general way to detect objects, especially in attention lapses.<br><br>METHODS: Dynamic probability integration (DPI) was proposed in this study to fuse human vision and computer vision. A novel basic probability assignment (BPA) method was included, which can fully consider the classification capabilities of different heterogeneous information sources for targets and non-targets and constructs the detection performance model for the weight generation based on classification capabilities. Furthermore, a spatial-temporal hybrid common spatial pattern-principal component analysis (STHCP) algorithm was designed to decode EEG signals in the RSVP task. It is a simple and effective method of distinguishing target and non-target using spatial-temporal features.<br><br>RESULTS: A nighttime vehicle detection based on the RSVP task was performed to evaluate the performance of DPI and STHCP, which is one of the conditions of attention lapses because of its decrease in visual information. The average AUC of DPI was 0.912 &#xb1; 0.041 and increased by 11.5, 5.2, 3.4, and 1.7% compared with human vision, computer vision, naive Bayesian fusion, and dynamic belief fusion (DBF), respectively. A higher average balanced accuracy of 0.845 &#xb1; 0.052 was also achieved using DPI, representing that DPI has the balanced detection capacity of target and non-target. Moreover, STHCP obtained the highest AUC of 0.818 &#xb1; 0.06 compared with the other two baseline methods and increased by 15.4 and 23.4%.<br><br>CONCLUSION: Experimental results indicated that the average AUC and balanced accuracy of the proposed fusion method were higher than individual detection methods used for fusion, as well as two excellent fusion methods. It is a promising way to improve detection performance in RSVP tasks, even in abnormal conditions.}, }
@article {pmid36313593, year = {2022}, author = {Ma, T and Li, Y and Huggins, JE and Zhu, J and Kang, J}, title = {Bayesian Inferences on Neural Activity in EEG-Based Brain-Computer Interface.}, journal = {Journal of the American Statistical Association}, volume = {117}, number = {539}, pages = {1122-1133}, pmid = {36313593}, issn = {0162-1459}, support = {R01 DA048993/DA/NIDA NIH HHS/United States ; R01 GM124061/GM/NIGMS NIH HHS/United States ; R01 MH105561/MH/NIMH NIH HHS/United States ; }, abstract = {A brain-computer interface (BCI) is a system that translates brain activity into commands to operate technology. A common design for an electroencephalogram (EEG) BCI relies on the classification of the P300 event-related potential (ERP), which is a response elicited by the rare occurrence of target stimuli among common non-target stimuli. Few existing ERP classifiers directly explore the underlying mechanism of the neural activity. To this end, we perform a novel Bayesian analysis of the probability distribution of multi-channel real EEG signals under the P300 ERP-BCI design. We aim to identify relevant spatial temporal differences of the neural activity, which provides statistical evidence of P300 ERP responses and helps design individually efficient and accurate BCIs. As one key finding of our single participant analysis, there is a 90% posterior probability that the target ERPs of the channels around visual cortex reach their negative peaks around 200 milliseconds post-stimulus. Our analysis identifies five important channels (PO7, PO8, Oz, P4, Cz) for the BCI speller leading to a 100% prediction accuracy. From the analyses of nine other participants, we consistently select the identified five channels, and the selection frequencies are robust to small variations of bandpass filters and kernel hyper-parameters.}, }
@article {pmid36312030, year = {2022}, author = {Lim, J and Wang, PT and Shaw, SJ and Gong, H and Armacost, M and Liu, CY and Do, AH and Heydari, P and Nenadic, Z}, title = {Artifact propagation in subdural cortical electrostimulation: Characterization and modeling.}, journal = {Frontiers in neuroscience}, volume = {16}, number = {}, pages = {1021097}, pmid = {36312030}, issn = {1662-4548}, abstract = {Cortical stimulation via electrocorticography (ECoG) may be an effective method for inducing artificial sensation in bi-directional brain-computer interfaces (BD-BCIs). However, strong electrical artifacts caused by electrostimulation may significantly degrade or obscure neural information. A detailed understanding of stimulation artifact propagation through relevant tissues may improve existing artifact suppression techniques or inspire the development of novel artifact mitigation strategies. Our work thus seeks to comprehensively characterize and model the propagation of artifacts in subdural ECoG stimulation. To this end, we collected and analyzed data from eloquent cortex mapping procedures of four subjects with epilepsy who were implanted with subdural ECoG electrodes. From this data, we observed that artifacts exhibited phase-locking and ratcheting characteristics in the time domain across all subjects. In the frequency domain, stimulation caused broadband power increases, as well as power bursts at the fundamental stimulation frequency and its super-harmonics. The spatial distribution of artifacts followed the potential distribution of an electric dipole with a median goodness-of-fit of R [2] = 0.80 across all subjects and stimulation channels. Artifacts as large as ±1,100 μV appeared anywhere from 4.43 to 38.34 mm from the stimulation channel. These temporal, spectral and spatial characteristics can be utilized to improve existing artifact suppression techniques, inspire new strategies for artifact mitigation, and aid in the development of novel cortical stimulation protocols. Taken together, these findings deepen our understanding of cortical electrostimulation and provide critical design specifications for future BD-BCI systems.}, }
@article {pmid36310494, year = {2022}, author = {Li, M and Gong, A and Nan, W and Xu, B and Ding, P and Fu, Y}, title = {[Neurofeedback technology based on functional near infrared spectroscopy imaging and its applications].}, journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi}, volume = {39}, number = {5}, pages = {1041-1049}, doi = {10.7507/1001-5515.202204031}, pmid = {36310494}, issn = {1001-5515}, mesh = {*Neurofeedback/methods ; Spectroscopy, Near-Infrared/methods ; Brain/diagnostic imaging ; Magnetic Resonance Imaging ; Technology ; }, abstract = {Neurofeedback (NF) technology based on electroencephalogram (EEG) data or functional magnetic resonance imaging (fMRI) has been widely studied and applied. In contrast, functional near infrared spectroscopy (fNIRS) has become a new technique in NF research in recent years. fNIRS is a neuroimaging technology based on hemodynamics, which has the advantages of low cost, good portability and high spatial resolution, and is more suitable for use in natural environments. At present, there is a lack of comprehensive review on fNIRS-NF technology (fNIRS-NF) in China. In order to provide a reference for the research of fNIRS-NF technology, this paper first describes the principle, key technologies and applications of fNIRS-NF, and focuses on the application of fNIRS-NF. Finally, the future development trend of fNIRS-NF is prospected and summarized. In conclusion, this paper summarizes fNIRS-NF technology and its application, and concludes that fNIRS-NF technology has potential practicability in neurological diseases and related fields. fNIRS can be used as a good method for NF training. This paper is expected to provide reference information for the development of fNIRS-NF technology.}, }
@article {pmid36310493, year = {2022}, author = {Cao, H and Jung, TP and Chen, Y and Mei, J and Li, A and Xu, M and Ming, D}, title = {[Research advances in non-invasive brain-computer interface control strategies].}, journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi}, volume = {39}, number = {5}, pages = {1033-1040}, doi = {10.7507/1001-5515.202205013}, pmid = {36310493}, issn = {1001-5515}, mesh = {Humans ; Electroencephalography ; *Brain-Computer Interfaces ; *Communication Aids for Disabled ; User-Computer Interface ; Brain/physiology ; }, abstract = {Brain-computer interface (BCI) can establish a direct communications pathway between the human brain and the external devices, which is independent of peripheral nerves and muscles. Compared with invasive BCI, non-invasive BCI has the advantages of low cost, low risk, and ease of operation. In recent years, using non-invasive BCI technology to control devices has gradually evolved into a new type of human-computer interaction manner. Moreover, the control strategy for BCI is an essential component of this manner. First, this study introduced how the brain control techniques were developed and classified. Second, the basic characteristics of direct and shared control strategies were thoroughly explained. And then the benefits and drawbacks of these two strategies were compared and further analyzed. Finally, the development direction and application prospects for non-invasive brain control strategies were suggested.}, }
@article {pmid36306641, year = {2022}, author = {Scott, CJ and de Mestre, AM and Verheyen, KL and Arango-Sabogal, JC}, title = {Bayesian accuracy estimates and fit for purpose thresholds of cytology and culture of endometrial swab samples for detecting endometritis in mares.}, journal = {Preventive veterinary medicine}, volume = {209}, number = {}, pages = {105783}, doi = {10.1016/j.prevetmed.2022.105783}, pmid = {36306641}, issn = {1873-1716}, mesh = {Horses ; Animals ; Female ; *Endometritis/diagnosis/veterinary/microbiology ; Retrospective Studies ; Bayes Theorem ; *Horse Diseases/diagnosis/epidemiology/microbiology ; Endometrium ; }, abstract = {The overall aim of this work was to identify the potential impact of misclassification errors associated with routine screening and diagnostic testing for endometritis in mares. Using Bayesian latent class models (BLCM), specific objectives were to: 1) estimate the diagnostic accuracy of cytology and culture of endometrial swab samples to detect endometritis in mares; 2) assess the impact of different cytology thresholds on test accuracy and misclassification costs; and 3) assess the sensitivity (Se) and specificity (Sp) of a diagnostic strategy including both tests interpreted in series and parallel. Diagnostic and pre-breeding endometrial swab samples collected from 3448 mares based at breeding premises located in the South East of England between 2014 and 2020 were retrospectively analysed. Culture results were classified as positive according to three different case definitions: (A) > 90% of the growth colonies were a monoculture; (B) pathogenic or pathogenic and non-pathogenic bacteria were identified; and (C) any growth was observed. Endometrial smears were graded based on the percent of polymorphonuclear cells (PMN) per high power field (HPF). A hierarchical BLCM was fitted using the cross-tabulated results of the three culture case definitions with a cytology threshold fixed at > 0.5% PMN. Fit for purpose cytology thresholds were proposed using a misclassification cost analysis in the context of good antimicrobial stewardship and for varying endometritis prevalence estimates. Median [95% Bayesian credible intervals (BCI)] cytology Se estimates were 6.5% (2.2-11.6), 6.4% (2.2-10.8) and 6.3% (2.2-10.8) for scenario A, B and C, respectively. Median (95% BCI) cytology Sp estimates were 88.8% (83.1-94.8), 88.9% (83.9-93.8) and 88.8% (84.0-93.8) for scenarios A, B and C, respectively. Median (95% BCI) culture Se estimates were 37.5% (29.9-46.0), 42.3% (33.8-51.1) and 46.4% (35.7-55.9) for scenarios A, B and C, respectively. Median (95% BCI) culture Sp estimates were 92.8% (84.3-99.0), 91.5% (82.5-98.0) and 90.8% (80.1-97.4) for scenarios A, B and C, respectively. Regardless of the culture case definition, Se and Sp of cytology (> 0.5% PMN) was lower than previously reported for swab samples in studies using histology as the reference standard test. The misclassification cost term decreased as the cytology threshold increased for all scenarios and all prevalence contexts, suggesting that, regardless of the endometritis prevalence in the population, increasing the cytology threshold would reduce the misclassification costs associated with false positive mares contributing to good antimicrobial stewardship.}, }
@article {pmid36306303, year = {2022}, author = {Lee, T and Nam, S and Hyun, DJ}, title = {Adaptive Window Method Based on FBCCA for Optimal SSVEP Recognition.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2022.3217789}, pmid = {36306303}, issn = {1558-0210}, abstract = {In the conventional studies related to steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs), the window length (detection time) was typically predetermined through the offline analysis, which had limitations of practical applicability of a BCI system due to the inter-subject/trial variability of electroencephalography (EEG) signals. To address these limitations, this study aims to automatically optimize the window length for each trial based on training-free approaches and proposes a novel adaptive window method (ANCOVA-based filter-bank canonical correlation analysis, ABFCCA) for SSVEP-based BCIs. The proposed method is based on analysis of covariance (ANCOVA) which is applied after feature extraction by the conventional training-free SSVEP recognition approaches. To evaluate the performance of the proposed method, conventional fixed window and recent adaptive window methods were compared using two open-access datasets. In the Benchmark dataset, the average information transfer rate (ITR) was 146.81 bits/min, the average accuracy 93.55%, and the average window length 1.53 s. In the OpenBMI dataset, the average ITR was 119.01 bits/min, the average accuracy 83.50%, and the average window length 0.65 s. The proposed method significantly outperformed the conventional approaches with fixed window in terms of the accuracy and ITR, and is applicable to various SSVEP-based BCI paradigms based on the criterion of significance level without offline analysis to find optimal hyper-parameters. ABFCCA is enabled the practical use of various BCI systems by automatically optimizing the window length independently.}, }
@article {pmid36304780, year = {2022}, author = {Valeriani, D and Santoro, F and Ienca, M}, title = {The present and future of neural interfaces.}, journal = {Frontiers in neurorobotics}, volume = {16}, number = {}, pages = {953968}, pmid = {36304780}, issn = {1662-5218}, abstract = {The 2020's decade will likely witness an unprecedented development and deployment of neurotechnologies for human rehabilitation, personalized use, and cognitive or other enhancement. New materials and algorithms are already enabling active brain monitoring and are allowing the development of biohybrid and neuromorphic systems that can adapt to the brain. Novel brain-computer interfaces (BCIs) have been proposed to tackle a variety of enhancement and therapeutic challenges, from improving decision-making to modulating mood disorders. While these BCIs have generally been developed in an open-loop modality to optimize their internal neural decoders, this decade will increasingly witness their validation in closed-loop systems that are able to continuously adapt to the user's mental states. Therefore, a proactive ethical approach is needed to ensure that these new technological developments go hand in hand with the development of a sound ethical framework. In this perspective article, we summarize recent developments in neural interfaces, ranging from neurohybrid synapses to closed-loop BCIs, and thereby identify the most promising macro-trends in BCI research, such as simulating vs. interfacing the brain, brain recording vs. brain stimulation, and hardware vs. software technology. Particular attention is devoted to central nervous system interfaces, especially those with application in healthcare and human enhancement. Finally, we critically assess the possible futures of neural interfacing and analyze the short- and long-term implications of such neurotechnologies.}, }
@article {pmid36303917, year = {2022}, author = {Liu, S}, title = {Applying antagonistic activation pattern to the single-trial classification of mental arithmetic.}, journal = {Heliyon}, volume = {8}, number = {10}, pages = {e11102}, pmid = {36303917}, issn = {2405-8440}, abstract = {BACKGROUND: At present, the application of fNIRS in the field of brain-computer interface (BCI) is being a hot topic. By fNIRS-BCI, the brain realizes the control of external devices. A state-of-the-art BCI system has five steps which are cerebral cortex signal acquisition, data pre-processing, feature selection and extraction, feature classification and application interface. Proper feature selection and extraction are crucial to the final fNIRS-BCI effect. This paper proposes a feature selection and extraction method for the mental arithmetic task. Specifically, we modified the antagonistic activation pattern approach and used the combination of antagonistic activation patterns to extract features for enhancement of the classification accuracy with low calculation costs.<br><br>METHODS: Experiments are conducted on an open-acquisition dataset including fNIRS signals of eight healthy subjects of mental arithmetic (MA) tasks and rest tasks. First, the signals are filtered using band-pass filters to remove noise. Second, channels are selected by prior knowledge about antagonistic activation patterns. We used cerebral blood volume (CBV) and cerebral oxygen exchange (COE) of selected each channel to build novel attributes. Finally, we proposed three groups of attributes which are CBV, COE and CBV + COE. Based on attributes generated by the proposed method, we calculated temporal statistical measures (average, variance, maximum, minimum and slope). Any two of five statistical measures were combined as feature sets.<br><br>MAIN RESULTS: With the LDA, QDA, and SVM classifiers, the proposed method obtained higher classification accuracies the basic control method. The maximum classification accuracies achieved by the proposed method are 67.45 &#xb1; 14.56% with LDA classifier, 89.73 &#xb1; 5.71% with QDA classifier, and 87.04 &#xb1; 6.88% with SVM classifier. The novel method reduced the running time by 3.75 times compared with the method incorporating all channels into the feature set. Therefore, the novel method reduces the computational costs while maintaining high classification accuracy. The results are validated by another open-access dataset including MA and rest tasks of 29 healthy subjects.}, }
@article {pmid36300170, year = {2022}, author = {Zhang, Y and Liu, D and Zhang, P and Li, T and Li, Z and Gao, F}, title = {Combining robust level extraction and unsupervised adaptive classification for high-accuracy fNIRS-BCI: An evidence on single-trial differentiation between mentally arithmetic- and singing-tasks.}, journal = {Frontiers in neuroscience}, volume = {16}, number = {}, pages = {938518}, pmid = {36300170}, issn = {1662-4548}, abstract = {Functional near-infrared spectroscopy (fNIRS) is a safe and non-invasive optical imaging technique that is being increasingly used in brain-computer interfaces (BCIs) to recognize mental tasks. Unlike electroencephalography (EEG) which directly measures neural activation, fNIRS signals reflect neurovascular-coupling inducing hemodynamic response that can be slow in time and varying in the pattern. The established classifiers extend the EEG-ones by mostly employing the feature based supervised models such as the support vector machine (SVM) and linear discriminant analysis (LDA), and fail to timely characterize the level-sensitive hemodynamic pattern. A dedicated classifier is desired for intentional activity recognition of fNIRS-BCI, including the adaptive acquisition of response relevant features and accurate discrimination of implied ideas. To this end, we herein propose a specifically-designed joint adaptive classification method that combines a Kalman filtering (KF) for robust level extraction and an adaptive Gaussian mixture model (a-GMM) for enhanced pattern recognition. The simulative investigations and paradigm experiments have shown that the proposed KF/a-GMM classification method can effectively track the random variations of task-evoked brain activation patterns, and improve the accuracy of single-trial classification task of mental arithmetic vs. mental singing, as compared to the conventional methods, e.g., those that employ combinations of the band-pass filtering (BPF) based feature extractors (mean, slope, and variance, etc.) and the classical recognizers (GMM, SVM, and LDA). The proposed approach paves a promising way for developing the real-time fNIRS-BCI technique.}, }
@article {pmid36299440, year = {2022}, author = {Xu, Y and Yin, H and Yi, W and Huang, X and Jian, W and Wang, C and Hu, R}, title = {Supervised and Semisupervised Manifold Embedded Knowledge Transfer in Motor Imagery-Based BCI.}, journal = {Computational intelligence and neuroscience}, volume = {2022}, number = {}, pages = {1603104}, pmid = {36299440}, issn = {1687-5273}, mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Imagery, Psychotherapy ; Algorithms ; Calibration ; Imagination ; }, abstract = {A long calibration procedure limits the use in practice for a motor imagery (MI)-based brain-computer interface (BCI) system. To tackle this problem, we consider supervised and semisupervised transfer learning. However, it is a challenge for them to cope with high intersession/subject variability in the MI electroencephalographic (EEG) signals. Based on the framework of unsupervised manifold embedded knowledge transfer (MEKT), we propose a supervised MEKT algorithm (sMEKT) and a semisupervised MEKT algorithm (ssMEKT), respectively. sMEKT only has limited labelled samples from a target subject and abundant labelled samples from multiple source subjects. Compared to sMEKT, ssMEKT adds comparably more unlabelled samples from the target subject. After performing Riemannian alignment (RA) and tangent space mapping (TSM), both sMEKT and ssMEKT execute domain adaptation to shorten the differences among subjects. During domain adaptation, to make use of the available samples, two algorithms preserve the source domain discriminability, and ssMEKT preserves the geometric structure embedded in the labelled and unlabelled target domains. Moreover, to obtain a subject-specific classifier, sMEKT minimizes the joint probability distribution shift between the labelled target and source domains, whereas ssMEKT performs the joint probability distribution shift minimization between the unlabelled target domain and all labelled domains. Experimental results on two publicly available MI datasets demonstrate that our algorithms outperform the six competing algorithms, where the sizes of labelled and unlabelled target domains are variable. Especially for the target subjects with 10 labelled samples and 270/190 unlabelled samples, ssMEKT shows 5.27% and 2.69% increase in average accuracy on the two abovementioned datasets compared to the previous best semisupervised transfer learning algorithm (RA-regularized common spatial patterns-weighted adaptation regularization, RA-RCSP-wAR), respectively. Therefore, our algorithms can effectively reduce the need of labelled samples for the target subject, which is of importance for the MI-based BCI application.}, }
@article {pmid36298430, year = {2022}, author = {Ng, CR and Fiedler, P and Kuhlmann, L and Liley, D and Vasconcelos, B and Fonseca, C and Tamburro, G and Comani, S and Lui, TK and Tse, CY and Warsito, IF and Supriyanto, E and Haueisen, J}, title = {Multi-Center Evaluation of Gel-Based and Dry Multipin EEG Caps.}, journal = {Sensors (Basel, Switzerland)}, volume = {22}, number = {20}, pages = {}, pmid = {36298430}, issn = {1424-8220}, mesh = {Humans ; Reproducibility of Results ; Electrodes ; *Electroencephalography ; *Brain-Computer Interfaces ; Electric Impedance ; }, abstract = {Dry electrodes for electroencephalography (EEG) allow new fields of application, including telemedicine, mobile EEG, emergency EEG, and long-term repetitive measurements for research, neurofeedback, or brain-computer interfaces. Different dry electrode technologies have been proposed and validated in comparison to conventional gel-based electrodes. Most previous studies have been performed at a single center and by single operators. We conducted a multi-center and multi-operator study validating multipin dry electrodes to study the reproducibility and generalizability of their performance in different environments and for different operators. Moreover, we aimed to study the interrelation of operator experience, preparation time, and wearing comfort on the EEG signal quality. EEG acquisitions using dry and gel-based EEG caps were carried out in 6 different countries with 115 volunteers, recording electrode-skin impedances, resting state EEG and evoked activity. The dry cap showed average channel reliability of 81% but higher average impedances than the gel-based cap. However, the dry EEG caps required 62% less preparation time. No statistical differences were observed between the gel-based and dry EEG signal characteristics in all signal metrics. We conclude that the performance of the dry multipin electrodes is highly reproducible, whereas the primary influences on channel reliability and signal quality are operator skill and experience.}, }
@article {pmid36298196, year = {2022}, author = {Zhang, S and Li, H and Li, L and Lu, J and Zuo, Z}, title = {A High-Capacity Steganography Algorithm Based on Adaptive Frequency Channel Attention Networks.}, journal = {Sensors (Basel, Switzerland)}, volume = {22}, number = {20}, pages = {}, pmid = {36298196}, issn = {1424-8220}, mesh = {*Image Processing, Computer-Assisted/methods ; *Algorithms ; Neural Networks, Computer ; }, abstract = {Deep learning has become an essential technique in image steganography. Most of the current deep-learning-based steganographic methods process digital images in the spatial domain. There are problems such as limited embedding capacity and unsatisfactory visual quality. To improve capacity-distortion performance, we develop a steganographic method from the frequency-domain perspective. We propose a module called the adaptive frequency-domain channel attention network (AFcaNet), which makes full use of the frequency features in each channel by a fine-grained manner of assigning weights. We apply this module to the state-of-the-art SteganoGAN, forming an Adaptive Frequency High-capacity Steganography Generative Adversarial Network (AFHS-GAN). The proposed neural network enhances the ability of high-dimensional feature extraction through overlaying densely connected convolutional blocks. In addition to this, a low-frequency loss function is introduced as an evaluation metric to guide the training of the network and thus reduces the modification of low-frequency regions of the image. Experimental results on the Div2K dataset show that our method has a better generalization capability compared to the SteganoGAN, with substantial improvement in both embedding capacity and stego-image quality. Furthermore, the embedding distribution of our method in the DCT domain is more similar to that of the traditional method, which is consistent with the prior knowledge of image steganography.}, }
@article {pmid36298064, year = {2022}, author = {Oikonomou, VP}, title = {An Adaptive Task-Related Component Analysis Method for SSVEP Recognition.}, journal = {Sensors (Basel, Switzerland)}, volume = {22}, number = {20}, pages = {}, pmid = {36298064}, issn = {1424-8220}, mesh = {*Evoked Potentials, Visual ; Electroencephalography/methods ; Bayes Theorem ; Algorithms ; *Brain-Computer Interfaces ; Photic Stimulation ; }, abstract = {Steady-State Visual Evoked Potential (SSVEP) recognition methods use a subject's calibration data to differentiate between brain responses, hence, providing the SSVEP-based brain-computer interfaces (BCIs) with high performance. However, they require sufficient calibration EEG trials to achieve that. This study develops a new method to learn from limited calibration EEG trials, and it proposes and evaluates a novel adaptive data-driven spatial filtering approach for enhancing SSVEP detection. The spatial filter learned from each stimulus utilizes temporal information from the corresponding EEG trials. To introduce the temporal information into the overall procedure, a multitask learning approach, based on the Bayesian framework, is adopted. The performance of the proposed method was evaluated into two publicly available benchmark datasets, and the results demonstrated that our method outperformed competing methods by a significant margin.}, }
@article {pmid36297922, year = {2022}, author = {Liu, N and Wang, H and Wang, S and Xu, B and Qu, L}, title = {Liquid Oxygen Compatibility and Ultra-Low-Temperature Mechanical Properties of Modified epoxy Resin Containing Phosphorus and Nitrogen.}, journal = {Polymers}, volume = {14}, number = {20}, pages = {}, pmid = {36297922}, issn = {2073-4360}, abstract = {Endowing epoxy resin (EP) with prospective liquid oxygen compatibility (LOC) as well as enhanced ultra-low-temperature mechanical properties is urgently required in order to broaden its applications in aerospace engineering. In this study, a reactive phosphorus/nitrogen-containing aromatic ethylenediamine (BSEA) was introduced as a reactive component to enhance the LOC and ultra-low-temperature mechanical properties of an EP/biscitraconimide resin (BCI) system. The resultant EP thermosets showed no sensitivity reactions in the 98J liquid oxygen impact test (LOT) when the BSEA content reached 4 wt% or 5 wt%, indicating that they were compatible with liquid oxygen. Moreover, the bending properties, fracture toughness and impact strength of BSEA-modified EP were greatly enhanced at RT and cryogenic temperatures (77 K) at an appropriate level of BSEA content. The bending strength (251.64 MPa) increased by 113.67%, the fracture toughness (2.97 MPa·m[1/2]) increased by 81.10%, and the impact strength (31.85 kJ·m[-2]) increased by 128.81% compared with that of pure EP at 77 K. All the above results demonstrate that the BSEA exhibits broad application potential in liquid oxygen tanks and in the cryogenic field.}, }
@article {pmid36291203, year = {2022}, author = {Ning, Y and Wan, G and Liu, T and Zhang, S}, title = {Volitional Generation of Reproducible, Efficient Temporal Patterns.}, journal = {Brain sciences}, volume = {12}, number = {10}, pages = {}, pmid = {36291203}, issn = {2076-3425}, abstract = {One of the extraordinary characteristics of the biological brain is the low energy expense it requires to implement a variety of biological functions and intelligence as compared to the modern artificial intelligence (AI). Spike-based energy-efficient temporal codes have long been suggested as a contributor for the brain to run on low energy expense. Despite this code having been largely reported in the sensory cortex, whether this code can be implemented in other brain areas to serve broader functions and how it evolves throughout learning have remained unaddressed. In this study, we designed a novel brain-machine interface (BMI) paradigm. Two macaques could volitionally generate reproducible energy-efficient temporal patterns in the primary motor cortex (M1) by learning the BMI paradigm. Moreover, most neurons that were not directly assigned to control the BMI did not boost their excitability, and they demonstrated an overall energy-efficient manner in performing the task. Over the course of learning, we found that the firing rates and temporal precision of selected neurons co-evolved to generate the energy-efficient temporal patterns, suggesting that a cohesive rather than dissociable processing underlies the refinement of energy-efficient temporal patterns.}, }
@article {pmid36290990, year = {2022}, author = {Filho, G and Júnior, C and Spinelli, B and Damasceno, I and Fiuza, F and Morya, E}, title = {All-Polymeric Electrode Based on PEDOT:PSS for In Vivo Neural Recording.}, journal = {Biosensors}, volume = {12}, number = {10}, pages = {}, pmid = {36290990}, issn = {2079-6374}, mesh = {Animals ; Rats ; *Neurons/physiology ; Rats, Wistar ; *Polymers ; Microelectrodes ; }, abstract = {One of the significant challenges today in the brain-machine interfaces that use invasive methods is the stability of the chronic record. In recent years, polymer-based electrodes have gained notoriety for achieving mechanical strength values close to that of brain tissue, promoting a lower immune response to the implant. In this work, we fabricated fully polymeric electrodes based on PEDOT:PSS for neural recording in Wistar rats. We characterized the electrical properties and both in vitro and in vivo functionality of the electrodes. Additionally, we employed histological processing and microscopical visualization to evaluate the tecidual immune response at 7, 14, and 21 days post-implant. Electrodes with 400-micrometer channels showed a 12 dB signal-to-noise ratio. Local field potentials were characterized under two conditions: anesthetized and free-moving. There was a proliferation of microglia at the tissue-electrode interface in the early days, though there was a decrease after 14 days. Astrocytes also migrated to the interface, but there was not continuous recruitment of these cells in the tissue; there was inflammatory stability by day 21. The signal was not affected by this inflammatory action, demonstrating that fully polymeric electrodes can be an alternative means to prolong the valuable time of neural recordings.}, }
@article {pmid36290910, year = {2022}, author = {Chen, W and Chen, SK and Liu, YH and Chen, YJ and Chen, CS}, title = {An Electric Wheelchair Manipulating System Using SSVEP-Based BCI System.}, journal = {Biosensors}, volume = {12}, number = {10}, pages = {}, pmid = {36290910}, issn = {2079-6374}, mesh = {Humans ; Evoked Potentials, Visual ; *Brain-Computer Interfaces ; *Wheelchairs ; Reactive Oxygen Species ; Photic Stimulation ; Electroencephalography/methods ; Algorithms ; }, abstract = {Most people with motor disabilities use a joystick to control an electric wheelchair. However, those who suffer from multiple sclerosis or amyotrophic lateral sclerosis may require other methods to control an electric wheelchair. This study implements an electroencephalography (EEG)-based brain-computer interface (BCI) system and a steady-state visual evoked potential (SSVEP) to manipulate an electric wheelchair. While operating the human-machine interface, three types of SSVEP scenarios involving a real-time virtual stimulus are displayed on a monitor or mixed reality (MR) goggles to produce the EEG signals. Canonical correlation analysis (CCA) is used to classify the EEG signals into the corresponding class of command and the information transfer rate (ITR) is used to determine the effect. The experimental results show that the proposed SSVEP stimulus generates the EEG signals because of the high classification accuracy of CCA. This is used to control an electric wheelchair along a specific path. Simultaneous localization and mapping (SLAM) is the mapping method that is available in the robotic operating software (ROS) platform that is used for the wheelchair system for this study.}, }
@article {pmid36289356, year = {2022}, author = {Bak, S and Jeong, Y and Yeu, M and Jeong, J}, title = {Brain-computer interface to predict impulse buying behavior using functional near-infrared spectroscopy.}, journal = {Scientific reports}, volume = {12}, number = {1}, pages = {18024}, pmid = {36289356}, issn = {2045-2322}, mesh = {Humans ; *Brain-Computer Interfaces ; Spectroscopy, Near-Infrared/methods ; *COVID-19 ; Prefrontal Cortex/diagnostic imaging/physiology ; Biomarkers ; }, abstract = {As the rate of vaccination against COVID-19 is increasing, demand for overseas travel is also increasing. Despite people's preference for duty-free shopping, previous studies reported that duty-free shopping increases impulse buying behavior. There are also self-reported tools to measure their impulse buying behavior, but it has the disadvantage of relying on the human memory and perception. Therefore, we propose a Brain-Computer Interface (BCI)-based brain signal processing methodology to supplement these limitations and to reduce ambiguity and conjecture of data. To achieve this goal, we focused on the brain's prefrontal cortex (PFC) activity, which supervises human decision-making and is closely related to impulse buying behavior. The PFC activation is observed by recording signals using a functional near-infrared spectroscopy (fNIRS) while inducing impulse buying behavior in virtual computing environments. We found that impulse buying behaviors were not only higher in online duty-free shops than in online regular stores, but the fNIRS signals were also different on the two sites. We also achieved an average accuracy of 93.78% in detecting impulse buying patterns using a support vector machine. These results were identical to the people's self-reported responses. This study provides evidence as a potential biomarker for detecting impulse buying behavior with fNIRS.}, }
@article {pmid36289267, year = {2022}, author = {An, KM and Shim, JH and Kwon, H and Lee, YH and Yu, KK and Kwon, M and Chun, WY and Hirosawa, T and Hasegawa, C and Iwasaki, S and Kikuchi, M and Kim, K}, title = {Detection of the 40 Hz auditory steady-state response with optically pumped magnetometers.}, journal = {Scientific reports}, volume = {12}, number = {1}, pages = {17993}, pmid = {36289267}, issn = {2045-2322}, mesh = {*Helium ; *Magnetoencephalography/methods ; Brain/diagnostic imaging/physiology ; Neuroimaging ; Functional Neuroimaging ; }, abstract = {Magnetoencephalography (MEG) is a functional neuroimaging technique that noninvasively detects the brain magnetic field from neuronal activations. Conventional MEG measures brain signals using superconducting quantum interference devices (SQUIDs). SQUID-MEG requires a cryogenic environment involving a bulky non-magnetic Dewar flask and the consumption of liquid helium, which restricts the variability of the sensor array and the gap between the cortical sources and sensors. Recently, miniature optically pumped magnetometers (OPMs) have been developed and commercialized. OPMs do not require cryogenic cooling and can be placed within millimeters from the scalp. In the present study, we arranged six OPM sensors on the temporal area to detect auditory-related brain responses in a two-layer magnetically shielded room. We presented the auditory stimuli of 1 kHz pure-tone bursts with 200 ms duration and obtained the M50 and M100 components of auditory-evoked fields. We delivered the periodic stimuli with a 40 Hz repetition rate and observed the gamma-band power changes and inter-trial phase coherence of auditory steady-state responses at 40 Hz. We found that the OPM sensors have a performance comparable to that of conventional SQUID-MEG sensors, and our results suggest the feasibility of using OPM sensors for functional neuroimaging and brain-computer interface applications.}, }
@article {pmid36288717, year = {2022}, author = {Wang, L and Zhan, G and Maimaitiyiming, Y and Su, Y and Lin, S and Liu, J and Su, K and Lin, J and Shen, S and He, W and Wang, F and Chen, J and Sun, S and Xue, Y and Gu, J and Chen, X and Zhang, J and Zhang, L and Wang, Q and Chang, KJ and Chiou, SH and Björklund, M and Naranmandura, H and Cheng, X and Hsu, CH}, title = {m[6]A modification confers thermal vulnerability to HPV E7 oncotranscripts via reverse regulation of its reader protein IGF2BP1 upon heat stress.}, journal = {Cell reports}, volume = {41}, number = {4}, pages = {111546}, doi = {10.1016/j.celrep.2022.111546}, pmid = {36288717}, issn = {2211-1247}, mesh = {Humans ; *Alphapapillomavirus/metabolism ; Carcinogenesis ; Heat-Shock Proteins ; Heat-Shock Response ; Papillomaviridae ; Papillomavirus E7 Proteins/genetics/metabolism ; *Papillomavirus Infections ; Proteasome Endopeptidase Complex ; RNA, Messenger/genetics/metabolism ; RNA, Viral/genetics ; Ubiquitin ; RNA-Binding Proteins ; }, abstract = {Human papillomavirus (HPV)-induced carcinogenesis critically depends on the viral early protein 7 (E7), making E7 an attractive therapeutic target. Here, we report that the E7 messenger RNA (mRNA)-containing oncotranscript complex can be selectively targeted by heat treatment. In HPV-infected cells, viral E7 mRNA is modified by N[6]-methyladenosine (m[6]A) and stabilized by IGF2BP1, a cellular m[6]A reader. Heat treatment downregulates E7 mRNA and protein by destabilizing IGF2BP1 without the involvement of canonical heat-shock proteins and reverses HPV-associated carcinogenesis in vitro and in vivo. Mechanistically, heat treatment promotes IGF2BP1 aggregation only in the presence of m[6]A-modified E7 mRNA to form distinct heat-induced m[6]A E7 mRNA-IGF2BP1 granules, which are resolved by the ubiquitin-proteasome system. Collectively, our results not only show a mutual regulation between m[6]A RNA and its reader but also provide a heat-treatment-based therapeutic strategy for HPV-associated malignancies by specifically downregulating E7 mRNA-IGF2BP1 oncogenic complex.}, }
@article {pmid36288219, year = {2022}, author = {Kalafatovich, J and Lee, M and Lee, SW}, title = {Learning Spatiotemporal Graph Representations for Visual Perception using EEG Signals.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2022.3217344}, pmid = {36288219}, issn = {1558-0210}, abstract = {Perceiving and recognizing objects enable interaction with the external environment. Recently, decoding brain signals based on brain-computer interface (BCI) that recognize the user's intentions by just looking at objects has attracted attention as a next-generation intuitive interface. However, classifying signals from different objects is very challenging, and in practice, decoding performance for visual perception is not yet high enough to be used in real environments. In this study, we aimed to classify single-trial electroencephalography signals evoked by visual stimuli into their corresponding semantic category. We proposed a two-stream convolutional neural network to increase classification performance. The model consists of a spatial stream and a temporal stream that use graph convolutional neural network and channel-wise convolutional neural network respectively. Two public datasets were used to evaluate the proposed model; (i) SU DB (a set of 72 photographs of objects belonging to 6 semantic categories) and MPI DB (8 exemplars belonging to two categories). Our results outperform state-of-the-art methods, with accuracies of 54.28 ± 7.89% for SU DB (6-class) and 84.40 ± 8.03% for MPI DB (2-class). These results could facilitate the application of intuitive BCI systems based on visual perception.}, }
@article {pmid36288214, year = {2022}, author = {Jin, J and Qu, T and Xu, R and Wang, X and Cichocki, A}, title = {Motor Imagery EEG Classification Based on Riemannian Sparse Optimization and Dempster-Shafer Fusion of Multi-Time-Frequency Patterns.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2022.3217573}, pmid = {36288214}, issn = {1558-0210}, abstract = {Motor imagery-based brain-computer interfaces (MI-BCIs) features are generally extracted from a wide fixed frequency band and time window of EEG signal. The performance suffers from individual differences in corresponding time to MI tasks. In order to solve the problem, in this study, we propose a novel method named Riemannian sparse optimization and Dempster-Shafer fusion of multi-time-frequency patterns (RSODSF) to enhance the decoding efficiency. First, we effectively combine the Riemannian geometry of the spatial covariance matrix with sparse optimization to extract more robust and distinct features. Second, the Dempster-Shafer theory is introduced and used to fuse each time window after sparse optimization of Riemannian features. Besides, the probabilistic values of the support vector machine (SVM) are obtained and transformed to effectively fuse multiple classifiers to leverage potential soft information of multiple trained SVM. The openaccess BCI Competition IV dataset IIa and Competition III dataset IIIa are employed to evaluate the performance of the proposed RSODSF. It achieves higher average accuracy (89.7% and 96.8%) than state-of-the-art methods. The improvement over the common spatial patterns (SFBCSP) are respectively 9.9% and 12.4% (p<0.01, paired t-test). These results show that our proposed RSODSF method is a promising candidate for the performance improvement of MI-BCI.}, }
@article {pmid36287496, year = {2022}, author = {Henriques, DHN and Alves, AMH and Kuntze, MM and Garcia, LDFR and Bortoluzzi, EA and Teixeira, CDS}, title = {Effect of dental tissue thickness on the measurement of oxygen saturation by two different pulse oximeters.}, journal = {Brazilian dental journal}, volume = {33}, number = {5}, pages = {26-34}, pmid = {36287496}, issn = {1806-4760}, mesh = {Humans ; *Oxygen Saturation ; *Oximetry ; Oxygen ; Dental Enamel ; Diamond ; }, abstract = {This study aimed to evaluate the influence of different dental tissue thickness on the measurement of oxygen saturation (SpO2) levels in high (HP) and low (LP) blood perfusion by comparing the values obtained from two different pulse oximeters (POs) - BCI and Sense 10. Thirty freshly extracted human teeth had their crowns interposed between the POs and an optical simulator, which emulated the SpO2 and heart beats per minute (bpm) at HP (100% SpO2/75 bpm) and LP (86% SpO2/75 bpm) modes. Afterwards, the palatine/lingual surfaces of the dental crowns were worn with diamond drills. The reading of SpO2 was performed again using the POs alternately through the buccal surface of each dental crown. Data were analyzed by the Wilcoxon, Mann-Whitney and Kendall Tau-b tests (α=5%). The results showed significant difference at the HP and LP modes in the SpO2 readouts through the different dental thicknesses with the use of BCI, and at the LP mode with the use of Sense 10, which had a significant linear correlation (p<0.0001) and lower SpO2 readout values in relation to the increase of the dental thickness. Irrespective of tooth thickness, Sense 10 had significantly higher readout values (p<0.0001) than BCI at both perfusion modes. The interposition of different thicknesses of enamel and dentin influenced the POs measurement of SpO2, specially at the low perfusion mode. The POs were more accurate in SpO2 measurement when simulated perfusion levels were higher.}, }
@article {pmid36286988, year = {2022}, author = {Zhu, L and Wang, M and Fu, P and Liu, Y and Zhang, H and Roe, AW and Xi, W}, title = {Precision 1070 nm Ultrafast Laser-Induced Photothrombosis of Depth-Targeted Vessels In Vivo.}, journal = {Small methods}, volume = {}, number = {}, pages = {e2200917}, doi = {10.1002/smtd.202200917}, pmid = {36286988}, issn = {2366-9608}, abstract = {The cerebrovasculature plays an essential role in neurovascular and homeostatic functions in health and disease conditions. Many efforts have been made for developing vascular thrombosis methods to study vascular dysfunction in vivo, while technical challenges remain, such as accuracy and depth-selectivity to target a single vessel in the cerebral cortex. Herein, this paper first demonstrates the evaluation and quantification of the feasibility and effects of Rose Bengal (RB)-induced photothrombosis with 720-1070 nm ultrafast lasers in a raster scan. A flexible and reproducible approach is then proposed to employ a 1070 nm ultrafast laser with a spiral scan for producing RB-induced occlusion, which is described as precision ultrafast laser-induced photothrombosis (PLP). Combine with two-photon microscopy imaging, this PLP displays highly precise and fast occlusion induction of various vessel types, sizes, and depths, which enhances the precision and power of the photothrombosis protocol. Overall, the PLP method provides a real-time, practical, precise, and depth-selected single-vessel photothrombosis technology in the cerebral cortex with commercially available optical equipment, which is crucial for exploring brain vascular function with high spatial-temporal resolution in the brain.}, }
@article {pmid36285909, year = {2022}, author = {Cavallaro, G and Murri, A and Nelson, E and Gorrasi, R and Quaranta, N}, title = {The Impact of the COVID-19 Lockdown on Quality of Life in Adult Cochlear Implant Users: A Survey Study.}, journal = {Audiology research}, volume = {12}, number = {5}, pages = {518-526}, pmid = {36285909}, issn = {2039-4330}, abstract = {BACKGROUND: The COVID-19 pandemic rapidly spread through Europe in the first months of 2020. On the 9th of March 2020, the Italian government ordered a national lock-down. The study's objectives were: to investigate the effect of lockdown on CI users; and to detect the difference in the perception of discomfort existing between unilateral cochlear implant (UCI) users and bilateral cochlear implant (BCI) users, due to the lockdown experience.<br><br>METHODS: A 17-item, web-based, anonymous online survey was administered to 57 CI users, exploring hearing performance, emotions, practical issues, behavior, and tinnitus. Participation in the study was voluntary.<br><br>RESULTS: all CI users obtained an abnormal score in all questionnaire themes. For the emotion theme and the practical issue theme, the age range 61-90 showed a significant difference between UCI and BCI users in favor of BCI users (emotion theme: UCI mean = 3.9, BCI mean = 2.3, p = 0.0138; practical issues: UCI mean = 4, BCI mean = 3, p = 0.0031).<br><br>CONCLUSIONS: CI users experienced the lockdown negatively as regards behavior, emotions, hearing performance, and in practical issues. CI subjects with UCI in old age suffered more from the experience of lockdown than subjects with BCI in the same age, with regards to emotions and practical issues.}, }
@article {pmid36285542, year = {2022}, author = {Yu, H and Ni, P and Tian, Y and Zhao, L and Li, M and Li, X and Wei, W and Wei, J and Du, X and Wang, Q and Guo, W and Deng, W and Ma, X and Coid, J and Li, T}, title = {Association of the plasma complement system with brain volume deficits in bipolar and major depressive disorders.}, journal = {Psychological medicine}, volume = {}, number = {}, pages = {1-11}, doi = {10.1017/S0033291722003282}, pmid = {36285542}, issn = {1469-8978}, abstract = {BACKGROUND: Inflammation plays a crucial role in the pathogenesis of major depressive disorder (MDD) and bipolar disorder (BD). This study aimed to examine whether the dysregulation of complement components contributes to brain structural defects in patients with mood disorders.<br><br>METHODS: A total of 52 BD patients, 35 MDD patients, and 53 controls were recruited. The human complement immunology assay was used to measure the levels of complement factors. Whole brain-based analysis was performed to investigate differences in gray matter volume (GMV) and cortical thickness (CT) among the BD, MDD, and control groups, and relationships were explored between neuroanatomical differences and levels of complement components.<br><br>RESULTS: GMV in the medial orbital frontal cortex (mOFC) and middle cingulum was lower in both patient groups than in controls, while the CT of the left precentral gyrus and left superior frontal gyrus were affected differently in the two disorders. Concentrations of C1q, C4, factor B, factor H, and properdin were higher in both patient groups than in controls, while concentrations of C3, C4 and factor H were significantly higher in BD than in MDD. Concentrations of C1q, factor H, and properdin showed a significant negative correlation with GMV in the mOFC at the voxel-wise level.<br><br>CONCLUSIONS: BD and MDD are associated with shared and different alterations in levels of complement factors and structural impairment in the brain. Structural defects in mOFC may be associated with elevated levels of certain complement factors, providing insight into the shared neuro-inflammatory pathogenesis of mood disorders.}, }
@article {pmid36284139, year = {2022}, author = {Li, P and Garg, AK and Zhang, LA and Rashid, MS and Callaway, EM}, title = {Cone opponent functional domains in primary visual cortex combine signals for color appearance mechanisms.}, journal = {Nature communications}, volume = {13}, number = {1}, pages = {6344}, pmid = {36284139}, issn = {2041-1723}, mesh = {*Calcium ; *Primary Visual Cortex ; Retinal Cone Photoreceptor Cells/physiology ; Color Perception/physiology ; Retina/physiology ; Photic Stimulation/methods ; Color ; }, abstract = {Studies of color perception have led to mechanistic models of how cone-opponent signals from retinal ganglion cells are integrated to generate color appearance. But it is unknown how this hypothesized integration occurs in the brain. Here we show that cone-opponent signals transmitted from retina to primary visual cortex (V1) are integrated through highly organized circuits within V1 to implement the color opponent interactions required for color appearance. Combining intrinsic signal optical imaging (ISI) and 2-photon calcium imaging (2PCI) at single cell resolution, we demonstrate cone-opponent functional domains (COFDs) that combine L/M cone-opponent and S/L + M cone-opponent signals following the rules predicted from psychophysical studies of color perception. These give rise to an orderly organization of hue preferences of the neurons within the COFDs and the generation of hue "pinwheels". Thus, spatially organized neural circuits mediate an orderly transition from cone-opponency to color appearance that begins in V1.}, }
@article {pmid36283830, year = {2022}, author = {Xing, D and Truccolo, W and Borton, DA}, title = {Emergence of distinct neural subspaces in motor cortical dynamics during volitional adjustments of ongoing locomotion.}, journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience}, volume = {}, number = {}, pages = {}, doi = {10.1523/JNEUROSCI.0746-22.2022}, pmid = {36283830}, issn = {1529-2401}, abstract = {The ability to modulate ongoing walking gait with precise, voluntary adjustments, is what allows animals to navigate complex terrains. However, how the nervous system generates the signals to precisely control the limbs while simultaneously maintaining locomotion is poorly understood. One potential strategy is to distribute the neural activity related to these two functions into distinct cortical activity co-activation subspaces so that both may be carried out simultaneously without disruptive interference. To investigate this hypothesis, we recorded the activity of primary motor cortex in male nonhuman primates during obstacle avoidance on a treadmill. We found that the same neural population was active during both basic unobstructed locomotion and volitional obstacle avoidance movements. We identified the neural modes spanning the subspace of the low-dimensional dynamics in M1 and found a subspace that consistently maintains the same cyclic activity throughout obstacle stepping, despite large changes in the movement itself. All of the variance corresponding to this large change in movement during the obstacle avoidance was confined to its own distinct subspace. Furthermore, neural decoders built for ongoing locomotion did not generalize to decoding obstacle avoidance during locomotion. Our findings suggest that separate underlying subspaces emerge during complex locomotion that coordinate ongoing locomotor-related neural dynamics with volitional gait adjustments. These findings may have important implications for the development of brain-machine interfaces.SIGNIFICANCE STATEMENT:Locomotion and precise, goal-directed movements, are two distinct movement modalities with known differing requirements of motor cortical input. Previous studies have characterized the cortical activity during obstacle avoidance while walking in rodents and felines, but to-date, no such studies have been completed in primates. Additionally, in any animal model, it is unknown how these two movements are represented in M1 low dimensional dynamics when both activities are performed at the same time, such as during obstacle avoidance. We developed a novel obstacle avoidance paradigm in freely-moving non-human primates and discovered that the rhythmic locomotion-related dynamics and the voluntary, gait-adjustment movement, separate into distinct subspaces in M1 cortical activity. Our analysis on decoding generalization may also have important implications for the development of brain-machine interfaces.}, }
@article {pmid36280665, year = {2022}, author = {Ji, SY and Dong, YJ and Chen, LN and Zang, SK and Wang, J and Shen, DD and Guo, J and Qin, J and Zhang, H and Wang, WW and Shen, Q and Zhang, Y and Song, Z and Mao, C}, title = {Molecular basis for the activation of thyrotropin-releasing hormone receptor.}, journal = {Cell discovery}, volume = {8}, number = {1}, pages = {116}, pmid = {36280665}, issn = {2056-5968}, }
@article {pmid36280089, year = {2022}, author = {Xue, Y and Zhu, J and Huang, X and Xu, X and Li, X and Zheng, Y and Zhu, Z and Jin, K and Ye, J and Gong, W and Si, K}, title = {A multi-feature deep learning system to enhance glaucoma severity diagnosis with high accuracy and fast speed.}, journal = {Journal of biomedical informatics}, volume = {136}, number = {}, pages = {104233}, doi = {10.1016/j.jbi.2022.104233}, pmid = {36280089}, issn = {1532-0480}, mesh = {Humans ; *Deep Learning ; *Glaucoma/diagnosis ; Diagnostic Techniques, Ophthalmological ; Photography/methods ; Diagnosis, Computer-Assisted/methods ; }, abstract = {Glaucoma is the leading cause of irreversible blindness, and the early detection and timely treatment are essential for glaucoma management. However, due to the interindividual variability in the characteristics of glaucoma onset, a single feature is not yet sufficient for monitoring glaucoma progression in isolation. There is an urgent need to develop more comprehensive diagnostic methods with higher accuracy. In this study, we proposed a multi- feature deep learning (MFDL) system based on intraocular pressure (IOP), color fundus photograph (CFP) and visual field (VF) to classify the glaucoma into four severity levels. We designed a three-phase framework for glaucoma severity diagnosis from coarse to fine, which contains screening, detection and classification. We trained it on 6,131 samples from 3,324 patients and tested it on independent 240 samples from 185 patients. Our results show that MFDL achieved a higher accuracy of 0.842 (95 % CI, 0.795-0.888) than the direct four classification deep learning (DFC-DL, accuracy of 0.513 [0.449-0.576]), CFP-based single-feature deep learning (CFP-DL, accuracy of 0.483 [0.420-0.547]) and VF-based single-feature deep learning (VF-DL, accuracy of 0.725 [0.668-0.782]). Its performance was statistically significantly superior to that of 8 juniors. It also outperformed 3 seniors and 1 expert, and was comparable with 2 glaucoma experts (0.842 vs 0.854, p = 0.663; 0.842 vs 0.858, p = 0.580). With the assistance of MFDL, junior ophthalmologists achieved statistically significantly higher accuracy performance, with the increased accuracy ranged from 7.50 % to 17.9 %, and that of seniors and experts were 6.30 % to 7.50 % and 5.40 % to 7.50 %. The mean diagnosis time per patient of MFDL was 5.96 s. The proposed model can potentially assist ophthalmologists in efficient and accurate glaucoma diagnosis that could aid the clinical management of glaucoma.}, }
@article {pmid36278046, year = {2022}, author = {Cao, X and Zhu, L and Qi, R and Wang, X and Sun, G and Ying, Y and Chen, R and Li, X and Gao, L}, title = {Effect of a High Estrogen Level in Early Pregnancy on the Development and Behavior of Marmoset Offspring.}, journal = {ACS omega}, volume = {7}, number = {41}, pages = {36175-36183}, pmid = {36278046}, issn = {2470-1343}, abstract = {The use of assisted reproductive technology (ART) has risen steadily worldwide over the past 3 decades and helps many infertile families. However, ART treatments lead to an abnormal internal environment in the uterus, which may increase the risks of health problems for the offspring. Higher maternal estradiol (E2) is a notable feature in women who use ART treatments, and this has been suggested as a key factor for the risk of diseases in the offspring. In the current study, we have established a marmoset model with a high E2 level in early pregnancy to examine its potential risk to the development and behavior of the offspring. In comparison with the normal group, babies of the high E2 group exhibited lower average survival rates and birth weights. However, those who survived in the high E2 group demonstrated normal vocal production with rich call repertoires, normal speed during locomotion, and normal behaviors in the home cage. In contrast to the normal group, surviving babies of the high E2 group spent more time sleeping during development without signs of sleep disorders. In summary, our study revealed that high estrogen in early pregnancy may cause low survival rates and birth weights of the offspring, though the surviving infants did not show obvious behavioral deficiencies during development. The current study is a valuable and highly important non-human primate study for evaluating the safety of ART treatments. However, it is worth noting that some results did not reach the significant level, which may be due to the small sample size caused by animal shortage stemming from the COVID-19 epidemic.}, }
@article {pmid36277512, year = {2022}, author = {Jamil, N and Belkacem, AN and Lakas, A}, title = {On enhancing students' cognitive abilities in online learning using brain activity and eye movements.}, journal = {Education and information technologies}, volume = {}, number = {}, pages = {1-35}, pmid = {36277512}, issn = {1360-2357}, abstract = {The COVID-19 pandemic has interrupted education institutions in over 150 nations, affecting billions of students. Many governments have forced a transition in higher education from in-person to remote learning. After this abrupt, worldwide transition away from the classroom, some question whether online education will continue to grow in acceptance in post-pandemic times. However, new technology, such as the brain-computer interface and eye-tracking, have the potential to improve the remote learning environment, which currently faces several obstacles and deficiencies. Cognitive brain computer interfaces can help us develop a better understanding of brain functions, allowing for the development of more effective learning methodologies and the enhancement of brain-based skills. We carried out a systematic literature review of research on the use of brain computer interfaces and eye-tracking to measure students' cognitive skills during online learning. We found that, because many experimental tasks depend on recorded rather than real-time video, students don't have direct and real-time interaction with their teacher. Further, we found no evidence in any of the reviewed papers for brain-to-brain synchronization during remote learning. This points to a potentially fruitful future application of brain computer interfaces in education, investigating whether the brains of student-teacher pairs who interact with the same course content have increasingly similar brain patterns.}, }
@article {pmid36277476, year = {2022}, author = {Wang, G and Cerf, M}, title = {Brain-Computer Interface using neural network and temporal-spectral features.}, journal = {Frontiers in neuroinformatics}, volume = {16}, number = {}, pages = {952474}, pmid = {36277476}, issn = {1662-5196}, abstract = {Brain-Computer Interfaces (BCIs) are increasingly useful for control. Such BCIs can be used to assist individuals who lost mobility or control over their limbs, for recreational purposes such as gaming or semi-autonomous driving, or as an interface toward man-machine integration. Thus far, the performance of algorithms used for thought decoding has been limited. We show that by extracting temporal and spectral features from electroencephalography (EEG) signals and, following, using deep learning neural network to classify those features, one can significantly improve the performance of BCIs in predicting which motor action was imagined by a subject. Our movement prediction algorithm uses Sequential Backward Selection technique to jointly choose temporal and spectral features and a radial basis function neural network for the classification. The method shows an average performance increase of 3.50% compared to state-of-the-art benchmark algorithms. Using two popular public datasets our algorithm reaches 90.08% accuracy (compared to an average benchmark of 79.99%) on the first dataset and 88.74% (average benchmark: 82.01%) on the second dataset. Given the high variability within- and across-subjects in EEG-based action decoding, we suggest that using features from multiple modalities along with neural network classification protocol is likely to increase the performance of BCIs across various tasks.}, }
@article {pmid36273413, year = {2022}, author = {Sinha, S and Finazzi-Agrò, E and Dmochowski, RR and Hashim, H and Iacovelli, V}, title = {The bladder contractility and bladder outlet obstruction indices in adult men: Results of a global Delphi consensus study.}, journal = {Neurourology and urodynamics}, volume = {}, number = {}, pages = {}, doi = {10.1002/nau.25073}, pmid = {36273413}, issn = {1520-6777}, abstract = {AIMS: This Delphi study was planned to examine global expert consensus with regard to utility, accuracy, and categorization of Bladder Contractility Index (BCI) and Bladder Outlet Obstruction Index (BOOI) and the related evidence.<br><br>METHODS: Twenty-eight experts were invited to answer the two-round survey including three foundation questions and 15 survey questions. Consensus was defined as &#x2265;75% agreement. The ordinal scale (0-10) in round 1 was classified into "strongly agree,"&#xa0;"agree,"&#xa0;"neutral,"&#xa0;"disagree," and "strongly disagree" for the final round. A systematic search for evidence was conducted for therapeutic studies that have examined outcome stratified by the indices in men.<br><br>RESULTS: Nineteen experts participated in the survey with 100% completion. Consensus was noted with regard to 6 of 19 questions. Experts strongly agreed with utility of quantifying bladder contractility and bladder outflow obstruction with near unanimity regarding the latter. There was consensus that BCI and BOOI were accurate, that BCI was clinically useful, and for defining severe bladder outflow obstruction as BOOI&#x2009;>&#x2009;80. Systematic search yielded 69 publications (BCI 45; BOOI 50). Most studies examined the indices as a continuous variable or by standard cutoffs (BCI 100, 150; BOOI 20, 40).<br><br>CONCLUSION: There is general agreement among experts on need for indices to quantify bladder contractility and bladder outflow obstruction as well as with regard to accuracy and utility of BCI and BOOI indices. Few studies have examined the discriminant power of existing cutoffs or explored new ones. This is an extraordinary knowledge gap in the field of urology.}, }
@article {pmid36272285, year = {2022}, author = {A, W and Du, F and He, Y and Wu, B and Liu, F and Liu, Y and Zheng, W and Li, G and Wang, X}, title = {Graphene oxide reinforced hemostasis of gelatin sponge in noncompressible hemorrhage via synergistic effects.}, journal = {Colloids and surfaces. B, Biointerfaces}, volume = {220}, number = {}, pages = {112891}, doi = {10.1016/j.colsurfb.2022.112891}, pmid = {36272285}, issn = {1873-4367}, mesh = {Humans ; *Gelatin/pharmacology ; Hemostasis ; *Hemostatics/pharmacology ; Hemorrhage/drug therapy ; }, abstract = {Effective hemostasis for noncompressible bleeding has been a key challenge because of the deep, narrow, and irregular wounds. Swellable gelatin is an available hemostatic material but is limited by weak mechanical strength and slow liquid absorption. Herein, the design of a gelatin and graphene oxide (GO) composite sponge (GP-GO) that possesses stable cross-linked networks and excellent absorbability, is reported. The GP-GOs are constructed via the thermal radical polymerization technique, using methacrylate gelatin (Gel-MA) and poly(ethylene glycol) diacrylate (PEGDA) as the crosslinker, while GO is uniformly fixed in the network via the curing reaction to further strengthen the stability. The optimized GP-GO5 with GO addition (5 wt%) exhibits high porosity (> 90%), distinguished liquid absorption rate (106 ms), rapidly responsive swelling (422% expansion within 10 s), and stable mechanical properties. The addition of GO effectively reinforces coagulation stimulation of GP-GOs though the stimulation of platelets and the enrichment effect at the interface, significantly reducing the blood coagulation index (BCI) (< 17.5%). Hemostatic mechanism study indicated the liquid absorbability of GP-GOs is the critical foundation to trigger the subsequent physical expansion, blood cells enrichment, and coagulation stimulations. Besides, GP-GO5 exhibits excellent biosafety assessed by hemolysis and cytotoxicity. Under the synergistic effects, the biocompatible GP-GO5 showed excellent hemostatic properties in the hemostasis of severe bleeding and noncompressible wounds compared with a pure gelatin sponge (GP) and the commercial hemostatic agent Celox™. This study demonstrated a promising candidate for practical application of noncompressible wound hemostasis.}, }
@article {pmid36271004, year = {2022}, author = {Zhai, X and Mao, C and Shen, Q and Zang, S and Shen, DD and Zhang, H and Chen, Z and Wang, G and Zhang, C and Zhang, Y and Liu, Z}, title = {Molecular insights into the distinct signaling duration for the peptide-induced PTH1R activation.}, journal = {Nature communications}, volume = {13}, number = {1}, pages = {6276}, pmid = {36271004}, issn = {2041-1723}, mesh = {*Receptor, Parathyroid Hormone, Type 1/genetics ; *Teriparatide/pharmacology ; Ligands ; Cryoelectron Microscopy ; Amino Acid Sequence ; Parathyroid Hormone/pharmacology ; Peptides/chemistry ; Receptors, G-Protein-Coupled ; }, abstract = {The parathyroid hormone type 1 receptor (PTH1R), a class B1 G protein-coupled receptor, plays critical roles in bone turnover and Ca[2+] homeostasis. Teriparatide (PTH) and Abaloparatide (ABL) are terms as long-acting and short-acting peptide, respectively, regarding their marked duration distinctions of the downstream signaling. However, the mechanistic details remain obscure. Here, we report the cryo-electron microscopy structures of PTH- and ABL-bound PTH1R-Gs complexes, adapting similar overall conformations yet with notable differences in the receptor ECD regions and the peptide C-terminal portions. 3D variability analysis and site-directed mutagenesis studies uncovered that PTH-bound PTH1R-Gs complexes display less motions and are more tolerant of mutations in affecting the receptor signaling than ABL-bound complexes. Furthermore, we combined the structural analysis and signaling assays to delineate the molecular basis of the differential signaling durations induced by these peptides. Our study deepens the mechanistic understanding of ligand-mediated prolonged or transient signaling.}, }
@article {pmid36270622, year = {2022}, author = {Liu, Y and Luo, C and Zheng, J and Liang, J and Ding, N}, title = {Working memory asymmetrically modulates auditory and linguistic processing of speech.}, journal = {NeuroImage}, volume = {264}, number = {}, pages = {119698}, doi = {10.1016/j.neuroimage.2022.119698}, pmid = {36270622}, issn = {1095-9572}, mesh = {Humans ; *Memory, Short-Term/physiology ; Speech/physiology ; Linguistics ; *Speech Perception/physiology ; Language ; }, abstract = {Working memory load can modulate speech perception. However, since speech perception and working memory are both complex functions, it remains elusive how each component of the working memory system interacts with each speech processing stage. To investigate this issue, we concurrently measure how the working memory load modulates neural activity tracking three levels of linguistic units, i.e., syllables, phrases, and sentences, using a multiscale frequency-tagging approach. Participants engage in a sentence comprehension task and the working memory load is manipulated by asking them to memorize either auditory verbal sequences or visual patterns. It is found that verbal and visual working memory load modulate speech processing in similar manners: Higher working memory load attenuates neural activity tracking of phrases and sentences but enhances neural activity tracking of syllables. Since verbal and visual WM load similarly influence the neural responses to speech, such influences may derive from the domain-general component of WM system. More importantly, working memory load asymmetrically modulates lower-level auditory encoding and higher-level linguistic processing of speech, possibly reflecting reallocation of attention induced by mnemonic load.}, }
@article {pmid36270502, year = {2022}, author = {Sosulski, J and Tangermann, M}, title = {Introducing block-Toeplitz covariance matrices to remaster linear discriminant analysis for event-related potential brain-computer interfaces.}, journal = {Journal of neural engineering}, volume = {19}, number = {6}, pages = {}, doi = {10.1088/1741-2552/ac9c98}, pmid = {36270502}, issn = {1741-2552}, mesh = {Humans ; Discriminant Analysis ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials ; Algorithms ; }, abstract = {Objective.Covariance matrices of noisy multichannel electroencephalogram (EEG) time series data provide essential information for the decoding of brain signals using machine learning methods. However, small datasets and high dimensionality make it hard to estimate these matrices. In brain-computer interfaces (BCI) based on event-related potentials (ERP) and a linear discriminant analysis (LDA) classifier, the state of the art covariance estimation uses shrinkage regularization. As this is a general covariance regularization approach, we aim at improving LDA further by better exploiting the domain-specific characteristics of the EEG to regularize the covariance estimates.Approach.We propose to enforce a block-Toeplitz structure for the covariance matrix of the LDA, which implements an assumption of signal stationarity in short time windows for each channel.Main results.An offline re-analysis of data collected from 213 subjects under 13 different event-related potential BCI protocols showed a significantly increased binary classification performance of this 'ToeplitzLDA' compared to shrinkage regularized LDA (up to 6 AUC points,p < 0.001) and Riemannian classification approaches (up to 2 AUC points,p < 0.001). In an unsupervised visual speller application, this improvement would translate to a relative reduction of spelling errors by 81% on average for 25 subjects. Additionally, aside from lower memory and reduced time complexity for LDA training, ToeplitzLDA proves to be robust against drastic increases of the number of temporal features.Significance.The proposed covariance estimation allows BCI researchers to improve classification rates and reduce calibration times of BCI protocols using event-related potentials and thus support the usability of corresponding applications. Its lower computational and memory needs could make it a valuable algorithm especially for mobile BCIs.}, }
@article {pmid36270467, year = {2022}, author = {Liu, S and Zhang, J and Wang, A and Wu, H and Zhao, Q and Long, J}, title = {Subject adaptation convolutional neural network for EEG-based motor imagery classification.}, journal = {Journal of neural engineering}, volume = {19}, number = {6}, pages = {}, doi = {10.1088/1741-2552/ac9c94}, pmid = {36270467}, issn = {1741-2552}, mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Neural Networks, Computer ; Imagery, Psychotherapy/methods ; Algorithms ; Imagination ; }, abstract = {Objective.Deep transfer learning has been widely used to address the nonstationarity of electroencephalogram (EEG) data during motor imagery (MI) classification. However, previous deep learning approaches suffer from limited classification accuracy because the temporal and spatial features cannot be effectively extracted.Approach.Here, we propose a novel end-to-end deep subject adaptation convolutional neural network (SACNN) to handle the problem of EEG-based MI classification. Our proposed model jointly optimizes three modules, i.e. a feature extractor, a classifier, and a subject adapter. Specifically, the feature extractor simultaneously extracts the temporal and spatial features from the raw EEG data using a parallel multiscale convolution network. In addition, we design a subject adapter to reduce the feature distribution shift between the source and target subjects by using the maximum mean discrepancy. By minimizing the classification loss and the distribution discrepancy, the model is able to extract the temporal-spatial features to the prediction of a new subject.Main results.Extensive experiments are carried out on three EEG-based MI datasets, i.e. brain-computer interface (BCI) competition IV dataset IIb, BCI competition III dataset IVa, and BCI competition IV dataset I, and the average accuracy reaches to 86.42%, 81.71% and 79.35% on the three datasets respectively. Furthermore, the statistical analysis also indicates the significant performance improvement of SACNN.Significance.This paper reveals the importance of the temporal-spatial features on EEG-based MI classification task. Our proposed SACNN model can make fully use of the temporal-spatial information to achieve the purpose.}, }
@article {pmid36270430, year = {2022}, author = {Spencer, M and Kameneva, T and Grayden, DB and Burkitt, AN and Meffin, H}, title = {Quantifying visual acuity for pre-clinical testing of visual prostheses.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ac9c95}, pmid = {36270430}, issn = {1741-2552}, abstract = {OBJECTIVE: Visual prostheses currently restore only limited vision. More research and pre-clinical work are required to improve the devices and stimulation strategies that are used to induce neural activity that results in visual perception. Evaluation of candidate strategies and devices requires an objective way to convert measured and modelled patterns of neural activity into a quantitative measure of visual acuity.<br><br>APPROACH: This study presents an approach that compares evoked patterns of neural activation with target and reference patterns. A d-prime measure of discriminability determines whether the evoked neural activation pattern is sufficient to discriminate between the target and reference patterns and thus provides a quantified level of visual perception in the clinical Snellen and MAR scales. The magnitude of the resulting value was demonstrated using scaled standardized "C" and "E" optotypes.<br><br>MAIN RESULTS: The approach was used to assess the visual acuity provided by two alternative stimulation strategies applied to simulated retinal implants with different electrode pitch configurations and differently sized spreads of neural activity. It was found that when there is substantial overlap in neural activity generated by different electrodes, an estimate of acuity based only upon electrode pitch is incorrect; our proposed method gives an accurate result in both circumstances.<br><br>SIGNIFICANCE: Quantification of visual acuity using this approach in pre-clinical development will allow for more rapid and accurate prototyping of improved devices and neural stimulation strategies.}, }
@article {pmid36269910, year = {2022}, author = {Sakkalis, V and Krana, M and Farmaki, C and Bourazanis, C and Gaitatzis, D and Pediaditis, M}, title = {Augmented Reality Driven Steady-State Visual Evoked Potentials for Wheelchair Navigation.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {30}, number = {}, pages = {2960-2969}, doi = {10.1109/TNSRE.2022.3215695}, pmid = {36269910}, issn = {1558-0210}, mesh = {Humans ; Evoked Potentials, Visual ; *Augmented Reality ; *Wheelchairs ; Electroencephalography ; *Brain-Computer Interfaces ; Photic Stimulation ; }, abstract = {Medically oriented Brain Computer Interfaces (BCIs) have been proposed as a promising approach addressed to individuals suffering from severe paralysis. Steady-State Visual Evoked Potentials (SSVEPs) in particular have been proven successful in many different applications, achieving high information throughput with short or even no training. However, efficient electric wheelchair navigation combining high accuracy and comfort is still not demonstrated. In this paper, we propose the use of an SSVEP-based universal control system featuring augmented reality (AR) glasses in an attempt to increase ease of use and patient acceptability without making compromises on BCI performance. The system received positive user-feedback, reaching a mean accuracy of 90%. Merits and pitfalls of the system proposed are also addressed.}, }
@article {pmid36269374, year = {2022}, author = {Margenau, EL and Wood, PB and Brown, DJ and Ryan, CW}, title = {Evaluating Mechanisms of Short-term Woodland Salamander Response to Forest Management.}, journal = {Environmental management}, volume = {}, number = {}, pages = {}, doi = {10.1007/s00267-022-01735-3}, pmid = {36269374}, issn = {1432-1009}, abstract = {Contemporary forest management often requires meeting diverse ecological objectives including maintaining ecosystem function and promoting biodiversity through timber harvesting. Wildlife are essential in this process by providing ecological services that can facilitate forest resiliency in response to timber harvesting. However, the mechanisms driving species' responses remain ambiguous. The goal of this study was to assess mechanisms influencing eastern red-backed salamander (RBS; Plethodon cinereus) response to overstory cover removal. We evaluated two mitigation strategies for the RBS in response to overstory removal. We used a before-after-control-impact design to study how (1) retaining residual trees or (2) eliminating soil compaction affected RBS surface counts and body condition index (BCI) up to two-years post-treatment. Additionally, we assessed how surface counts of RBS were influenced by overstory tree cover. Surface counts of RBS were not strongly influenced by overstory removal when tree residuals were retained. Body condition index increased in treatments where harvest residuals were retained. In treatments where soil compaction was eliminated, surface counts and BCI were inversely related. Finally, surface counts from both mitigation strategies were not strongly influenced by overstory cover. Overall, both mitigation techniques appeared to ameliorate impacts of overstory removal on RBS. These results highlight the importance of understanding mechanisms driving species' responses to forest management. To reduce the perceived negative effects of overstory removal on RBS, incorporating these mitigation measures may contribute to the viability and stability of RBS populations. Incorporating species' life history traits into management strategies could increase continuity of ecological function and integrity through harvesting.}, }
@article {pmid36264857, year = {2022}, author = {Yan, Z and Yang, X and Jin, Y}, title = {Considerate motion imagination classification method using deep learning.}, journal = {PloS one}, volume = {17}, number = {10}, pages = {e0276526}, pmid = {36264857}, issn = {1932-6203}, mesh = {*Deep Learning ; Algorithms ; Imagination ; *Brain-Computer Interfaces ; Electroencephalography/methods ; }, abstract = {In order to improve the classification accuracy of motion imagination, a considerate motion imagination classification method using deep learning is proposed. Specifically, based on a graph structure suitable for electroencephalography as input, the proposed model can accurately represent the distribution of electroencephalography electrodes in non-Euclidean space and fully consider the spatial correlation between electrodes. In addition, the spatial-spectral-temporal multi-dimensional feature information was extracted from the spatial-temporal graph representation and spatial-spectral graph representation transformed from the original electroencephalography signal using the dual branch architecture. Finally, the attention mechanism and global feature aggregation module were designed and combined with graph convolution to adaptively capture the dynamic correlation intensity and effective feature of electroencephalography signals in various dimensions. A series of contrast experiments and ablation experiments on several different public brain-computer interface datasets demonstrated that the excellence of proposed method. It is worth mentioning that, the proposed model is a general framework for the classification of electroencephalography signals, which is suitable for emotion recognition, sleep staging and other fields based on electroencephalography research. Moreover, the model has the potential to be applied in the medical field of motion imagination rehabilitation in real life.}, }
@article {pmid36264734, year = {2022}, author = {Feng, L and Shan, H and Zhang, Y and Zhu, Z}, title = {An Efficient Model-Compressed EEGNet Accelerator for Generalized Brain-Computer Interfaces with Near Sensor Intelligence.}, journal = {IEEE transactions on biomedical circuits and systems}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBCAS.2022.3215962}, pmid = {36264734}, issn = {1940-9990}, abstract = {Brain-computer interfaces (BCIs) is promising in interacting with machines through electroencephalogram (EEG) signal. The compact end-to-end neural network model for generalized BCIs, EEGNet, has been implemented in hardware to get near sensor intelligence, but without enough efficiency. To utilize EEGNet in low-power wearable device for long-term use, this paper proposes an efficient EEGNet inference accelerator. Firstly, the EEGNet model is compressed by embedded channel selection, normalization merging, and product quantization. The customized accelerator based on the compressed model is then designed. The multilayer convolutions are achieved by reusing multiplying-accumulators and processing elements (PEs) to minimize area of logic circuits, and the weights and intermediate results are quantized to minimize memory sizes. The PEs are clock-gated to save power. Experimental results in FPGA on three datasets show the good generalizing ability of the proposed design across three BCI diagrams, which only consumes 3.31% area and 1.35% power compared to the one-to-one parallel design. The speedup factors of 1.4, 3.5, and 3.7 are achieved by embedded channel selection with negligible loss of accuracy (-0.80%). The presented accelerator is also synthesized in 65nm CMOS low power (LP) process and consumes 0.23M gates, 24.4ms/inference, 0.267mJ/inference, which is 87.22% more efficient than the implementation of EEGNet in a RISC-V MCU realized in 40nm CMOS LP process in terms of area, and 20.77% more efficient in terms of energy efficiency on BCIC-IV-2a dataset.}, }
@article {pmid36264427, year = {2022}, author = {Ishida, S and Matsukawa, Y and Yuba, T and Naito, Y and Matsuo, K and Majima, T and Gotoh, M}, title = {Urodynamic risk factors of asymptomatic bacteriuria in men with non-neurogenic lower urinary tract symptoms.}, journal = {World journal of urology}, volume = {40}, number = {12}, pages = {3035-3041}, pmid = {36264427}, issn = {1433-8726}, mesh = {Middle Aged ; Male ; Humans ; Aged ; Urodynamics ; Retrospective Studies ; *Bacteriuria/epidemiology/complications ; Urinary Bladder ; *Lower Urinary Tract Symptoms/epidemiology/complications ; *Urinary Bladder Neck Obstruction/complications ; Risk Factors ; }, abstract = {PURPOSE: To investigate the prevalence of asymptomatic bacteriuria (ASB) in middle-aged and older men with non-neurogenic lower urinary tract symptoms (LUTS) and clarify urodynamic factors related to the presence of ASB.<br><br>METHODS: We retrospectively reviewed the clinical data of men with LUTS who underwent urine culture examination, LUTS severity assessment, and urodynamic studies. The patients were allocated into two groups (the ASB&#x2009;+&#x2009;LUTS and LUTS-only) according to presence or absence of ASB. The patients' characteristics and urodynamic factors related to the development of ASB were assessed using univariate, binomial logistic regression, and receiver-operating characteristic (ROC) curve analyses.<br><br>RESULTS: Of 440 men, 93 (21.1%) had ASB. Parameters related to voiding functions, such as maximum flow rate, post-void residual urine volume, bladder voiding efficiency (BVE), and bladder contractility index (BCI), were significantly reduced in the ASB&#x2009;+&#x2009;LUTS group, while bladder outlet obstruction index was not different between the groups. Binomial logistic regression analysis showed that the presence of diabetes, lower BCI, and lower BVE were significantly associated with the presence of ASB. In addition, ROC analysis identified 55% as the optimal cutoff value of BVE for the presence of ASB, with a sensitivity of 84% and specificity of 83%.<br><br>CONCLUSIONS: ASB was found in&#x2009;>&#x2009;20% of men with non-neurogenic LUTS and was associated with decreased bladder contractility and decreased BVE. BVE could predict presence of ASB with high sensitivity and specificity.}, }
@article {pmid36261030, year = {2022}, author = {Song, CY and Hsieh, HL and Pesaran, B and Shanechi, MM}, title = {Modeling and inference methods for switching regime-dependent dynamical systems with multiscale neural observations.}, journal = {Journal of neural engineering}, volume = {19}, number = {6}, pages = {}, doi = {10.1088/1741-2552/ac9b94}, pmid = {36261030}, issn = {1741-2552}, mesh = {*Models, Neurological ; Algorithms ; *Brain-Computer Interfaces ; Normal Distribution ; Brain ; }, abstract = {Objective.Realizing neurotechnologies that enable long-term neural recordings across multiple spatial-temporal scales during naturalistic behaviors requires new modeling and inference methods that can simultaneously address two challenges. First, the methods should aggregate information across all activity scales from multiple recording sources such as spiking and field potentials. Second, the methods should detect changes in the regimes of behavior and/or neural dynamics during naturalistic scenarios and long-term recordings. Prior regime detection methods are developed for a single scale of activity rather than multiscale activity, and prior multiscale methods have not considered regime switching and are for stationary cases.Approach.Here, we address both challenges by developing a switching multiscale dynamical system model and the associated filtering and smoothing methods. This model describes the encoding of an unobserved brain state in multiscale spike-field activity. It also allows for regime-switching dynamics using an unobserved regime state that dictates the dynamical and encoding parameters at every time-step. We also design the associated switching multiscale inference methods that estimate both the unobserved regime and brain states from simultaneous spike-field activity.Main results.We validate the methods in both extensive numerical simulations and prefrontal spike-field data recorded in a monkey performing saccades for fluid rewards. We show that these methods can successfully combine the spiking and field potential observations to simultaneously track the regime and brain states accurately. Doing so, these methods lead to better state estimation compared with single-scale switching methods or stationary multiscale methods. Also, for single-scale linear Gaussian observations, the new switching smoother can better generalize to diverse system settings compared to prior switching smoothers.Significance.These modeling and inference methods effectively incorporate both regime-detection and multiscale observations. As such, they could facilitate investigation of latent switching neural population dynamics and improve future brain-machine interfaces by enabling inference in naturalistic scenarios where regime-dependent multiscale activity and behavior arise.}, }
@article {pmid36260252, year = {2022}, author = {Lin, S and Zhu, MY and Tang, MY and Wang, M and Yu, XD and Zhu, Y and Xie, SZ and Yang, D and Chen, J and Li, XM}, title = {Somatostatin-Positive Neurons in the Rostral Zona Incerta Modulate Innate Fear-Induced Defensive Response in Mice.}, journal = {Neuroscience bulletin}, volume = {}, number = {}, pages = {}, pmid = {36260252}, issn = {1995-8218}, abstract = {Defensive behaviors induced by innate fear or Pavlovian fear conditioning are crucial for animals to avoid threats and ensure survival. The zona incerta (ZI) has been demonstrated to play important roles in fear learning and fear memory, as well as modulating auditory-induced innate defensive behavior. However, whether the neuronal subtypes in the ZI and specific circuits can mediate the innate fear response is largely unknown. Here, we found that somatostatin (SST)-positive neurons in the rostral ZI of mice were activated by a visual innate fear stimulus. Optogenetic inhibition of SST-positive neurons in the rostral ZI resulted in reduced flight responses to an overhead looming stimulus. Optogenetic activation of SST-positive neurons in the rostral ZI induced fear-like defensive behavior including increased immobility and bradycardia. In addition, we demonstrated that manipulation of the GABAergic projections from SST-positive neurons in the rostral ZI to the downstream nucleus reuniens (Re) mediated fear-like defensive behavior. Retrograde trans-synaptic tracing also revealed looming stimulus-activated neurons in the superior colliculus (SC) that projected to the Re-projecting SST-positive neurons in the rostral ZI (SC-ZIr[SST]-Re pathway). Together, our study elucidates the function of SST-positive neurons in the rostral ZI and the SC-ZIr[SST]-Re tri-synaptic circuit in mediating the innate fear response.}, }
@article {pmid36257830, year = {2022}, author = {Ni, RJ and Gao, TH and Wang, YY and Tian, Y and Wei, JX and Zhao, LS and Ni, PY and Ma, XH and Li, T}, title = {Chronic lithium treatment ameliorates ketamine-induced mania-like behavior via the PI3K-AKT signaling pathway.}, journal = {Zoological research}, volume = {43}, number = {6}, pages = {989-1004}, pmid = {36257830}, issn = {2095-8137}, mesh = {Male ; Mice ; Animals ; *Ketamine/toxicity ; Phosphatidylinositol 3-Kinases/genetics/metabolism/pharmacology ; Proto-Oncogene Proteins c-akt/genetics/metabolism/pharmacology ; Lithium/pharmacology ; Mania ; Phosphatidylinositol 3-Kinase/genetics/metabolism/pharmacology ; *Depressive Disorder, Major ; RNA, Small Interfering ; TOR Serine-Threonine Kinases/genetics ; Signal Transduction ; Antidepressive Agents/therapeutic use/pharmacology ; Sirolimus/pharmacology ; Lithium Compounds/pharmacology ; Mammals ; *Rodent Diseases/drug therapy ; }, abstract = {Ketamine, a rapid-acting antidepressant drug, has been used to treat major depressive disorder and bipolar disorder (BD). Recent studies have shown that ketamine may increase the potential risk of treatment-induced mania in patients. Ketamine has also been applied to establish animal models of mania. At present, however, the underlying mechanism is still unclear. In the current study, we found that chronic lithium exposure attenuated ketamine-induced mania-like behavior and c-Fos expression in the medial prefrontal cortex (mPFC) of adult male mice. Transcriptome sequencing was performed to determine the effect of lithium administration on the transcriptome of the PFC in ketamine-treated mice, showing inactivation of the phosphoinositide 3-kinase (PI3K)-protein kinase B (AKT) signaling pathway. Pharmacological inhibition of AKT signaling by MK2206 (40 mg/kg), a selective AKT inhibitor, reversed ketamine-induced mania. Furthermore, selective knockdown of AKT via AAV-AKT-shRNA-EGFP in the mPFC also reversed ketamine-induced mania-like behavior. Importantly, pharmacological activation of AKT signaling by SC79 (40 mg/kg), an AKT activator, contributed to mania in low-dose ketamine-treated mice. Inhibition of PI3K signaling by LY294002 (25 mg/kg), a specific PI3K inhibitor, reversed the mania-like behavior in ketamine-treated mice. However, pharmacological inhibition of mammalian target of rapamycin (mTOR) signaling with rapamycin (10 mg/kg), a specific mTOR inhibitor, had no effect on ketamine-induced mania-like behavior. These results suggest that chronic lithium treatment ameliorates ketamine-induced mania-like behavior via the PI3K-AKT signaling pathway, which may be a novel target for the development of BD treatment.}, }
@article {pmid36257070, year = {2022}, author = {Xu, F and Li, J and Dong, G and Li, J and Chen, X and Zhu, J and Hu, J and Zhang, Y and Yue, S and Wen, D and Leng, J}, title = {EEG decoding method based on multi-feature information fusion for spinal cord injury.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {156}, number = {}, pages = {135-151}, doi = {10.1016/j.neunet.2022.09.016}, pmid = {36257070}, issn = {1879-2782}, mesh = {Humans ; Reproducibility of Results ; Electroencephalography/methods ; *Brain-Computer Interfaces ; Movement/physiology ; Algorithms ; *Spinal Cord Injuries/diagnosis ; Imagination/physiology ; }, abstract = {To develop an efficient brain-computer interface (BCI) system, electroencephalography (EEG) measures neuronal activities in different brain regions through electrodes. Many EEG-based motor imagery (MI) studies do not make full use of brain network topology. In this paper, a deep learning framework based on a modified graph convolution neural network (M-GCN) is proposed, in which temporal-frequency processing is performed on the data through modified S-transform (MST) to improve the decoding performance of original EEG signals in different types of MI recognition. MST can be matched with the spatial position relationship of the electrodes. This method fusions multiple features in the temporal-frequency-spatial domain to further improve the recognition performance. By detecting the brain function characteristics of each specific rhythm, EEG generated by imaginary movement can be effectively analyzed to obtain the subjects' intention. Finally, the EEG signals of patients with spinal cord injury (SCI) are used to establish a correlation matrix containing EEG channel information, the M-GCN is employed to decode relation features. The proposed M-GCN framework has better performance than other existing methods. The accuracy of classifying and identifying MI tasks through the M-GCN method can reach 87.456%. After 10-fold cross-validation, the average accuracy rate is 87.442%, which verifies the reliability and stability of the proposed algorithm. Furthermore, the method provides effective rehabilitation training for patients with SCI to partially restore motor function.}, }
@article {pmid36253948, year = {2022}, author = {Le Moigne, V and Blouquit-Laye, S and Desquesnes, A and Girard-Misguich, F and Herrmann, JL}, title = {Liposomal amikacin and Mycobacterium abscessus: intimate interactions inside eukaryotic cells.}, journal = {The Journal of antimicrobial chemotherapy}, volume = {77}, number = {12}, pages = {3496-3503}, doi = {10.1093/jac/dkac348}, pmid = {36253948}, issn = {1460-2091}, mesh = {Humans ; Amikacin/pharmacology ; *Mycobacterium abscessus ; Eukaryotic Cells ; *Mycobacterium Infections, Nontuberculous/drug therapy/microbiology ; Anti-Bacterial Agents/pharmacology/therapeutic use ; Liposomes ; *Mycobacterium ; Microbial Sensitivity Tests ; }, abstract = {BACKGROUND: Mycobacterium abscessus (Mabs), a rapidly growing Mycobacterium species, is considered an MDR organism. Among the standard antimicrobial multi-drug regimens against Mabs, amikacin is considered as one of the most effective. Parenteral amikacin, as a consequence of its inability to penetrate inside the cells, is only active against extracellular mycobacteria. The use of inhaled liposomal amikacin may yield improved intracellular efficacy by targeting Mabs inside the cells, while reducing its systemic toxicity.<br><br>OBJECTIVES: To evaluate the colocalization of an amikacin liposomal inhalation suspension (ALIS) with intracellular Mabs, and then to measure its intracellular anti-Mabs activity.<br><br>METHODS: We evaluated the colocalization of ALIS with Mabs in eukaryotic cells such as macrophages (THP-1 and J774.2) or pulmonary epithelial cells (BCi-NS1.1 and MucilAir), using a fluorescent ALIS and GFP-expressing Mabs, to test whether ALIS reaches intracellular Mabs. We then evaluated the intracellular anti-Mabs activity of ALIS inside macrophages using cfu and/or luminescence.<br><br>RESULTS: Using confocal microscopy, we demonstrated fluorescent ALIS and GFP-Mabs colocalization in macrophages and epithelial cells. We also showed that ALIS was active against intracellular Mabs at a concentration of 32 to 64&#x2005;mg/L, at 3 and 5&#x2005;days post-infection. Finally, ALIS intracellular activity was confirmed when tested against 53 clinical Mabs isolates, showing intracellular growth reduction for nearly 80% of the isolates.<br><br>CONCLUSIONS: Our experiments demonstrate the intracellular localization and intracellular contact between Mabs and ALIS, and antibacterial activity against intracellular Mabs, showing promise for its future use for Mabs pulmonary infections.}, }
@article {pmid36252368, year = {2022}, author = {Gao, Y and Liu, A and Cui, X and Qian, R and Chen, X}, title = {A general sample-weighted framework for epileptic seizure prediction.}, journal = {Computers in biology and medicine}, volume = {150}, number = {}, pages = {106169}, doi = {10.1016/j.compbiomed.2022.106169}, pmid = {36252368}, issn = {1879-0534}, abstract = {OBJECTIVE: Effective epileptic seizure prediction can make the patients know the onset of the seizure in advance to take timely preventive measures. Many studies based on machine learning methods have been proposed to tackle this problem and achieve significant progress in recent years. However, most studies treat each EEG training sample's contribution to the model as equal, while different samples have different predictive effects on epileptic seizures (e.g., preictal samples from different times). To this end, in this paper, we propose a general sample-weighted framework for patient-specific epileptic seizure prediction.<br><br>METHODS: Specifically, we define the mapping from the sample weights of training sets to the performance of the validation sets as the fitness function to be optimized. Then, the genetic algorithm is employed to optimize this fitness function and obtain the optimal sample weights. Finally, we obtain the final model by using the training sets with optimized sample weights.<br><br>RESULTS: To evaluate the effectiveness of our framework, we conduct extensive experiments on both traditional machine learning methods and prevalent deep learning methods. Our framework can significantly improve performance based on these methods. Among them, our framework based on Transformer achieves an average sensitivity of 94.6%, an average false prediction rate of 0.06/h, and an average AUC of 0.939 in 12 pediatric patients from the CHB-MIT database with the leave-one-out method, which outperforms the state-of-the-art methods.<br><br>CONCLUSION: This study provides new insights into the field of epileptic seizure prediction by considering the discrepancies between EEG samples. Moreover, we develop a general sample-weighted framework, which applies to almost all classical classification methods and can significantly improve performance based on these methods.}, }
@article {pmid36251899, year = {2022}, author = {Jeong, JH and Cho, JH and Lee, BH and Lee, SW}, title = {Real-Time Deep Neurolinguistic Learning Enhances Noninvasive Neural Language Decoding for Brain-Machine Interaction.}, journal = {IEEE transactions on cybernetics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TCYB.2022.3211694}, pmid = {36251899}, issn = {2168-2275}, abstract = {Electroencephalogram (EEG)-based brain-machine interface (BMI) has been utilized to help patients regain motor function and has recently been validated for its use in healthy people because of its ability to directly decipher human intentions. In particular, neurolinguistic research using EEGs has been investigated as an intuitive and naturalistic communication tool between humans and machines. In this study, the human mind directly decoded the neural languages based on speech imagery using the proposed deep neurolinguistic learning. Through real-time experiments, we evaluated whether BMI-based cooperative tasks between multiple users could be accomplished using a variety of neural languages. We successfully demonstrated a BMI system that allows a variety of scenarios, such as essential activity, collaborative play, and emotional interaction. This outcome presents a novel BMI frontier that can interact at the level of human-like intelligence in real time and extends the boundaries of the communication paradigm.}, }
@article {pmid36251867, year = {2022}, author = {Elliott, C and Sutherland, D and Gerhard, D and Theys, C}, title = {An Evaluation of the P300 Brain-Computer Interface, EyeLink Board, and Eye-Tracking Camera as Augmentative and Alternative Communication Devices.}, journal = {Journal of speech, language, and hearing research : JSLHR}, volume = {65}, number = {11}, pages = {4280-4290}, doi = {10.1044/2022_JSLHR-21-00572}, pmid = {36251867}, issn = {1558-9102}, mesh = {Humans ; *Brain-Computer Interfaces ; Eye-Tracking Technology ; *Communication Aids for Disabled ; *Communication Disorders ; Communication ; Electroencephalography ; }, abstract = {PURPOSE: Augmentative and alternative communication (AAC) systems are important to support communication for individuals with complex communication needs. A recent addition to AAC system options is the brain-computer interface (BCI). This study aimed to compare the clinical application of the P300 speller BCI with two more common AAC systems, the EyeLink board, and an eye-tracking camera.<br><br>METHOD: Ten participants without communication impairment (18-35 years of age) used each of the three AAC systems to spell three-letter words in one session. Accuracy and speed of letter selection were measured, and questionnaires were administered to evaluate usability, cognitive workload, and user preferences.<br><br>RESULTS: The results showed that the BCI was significantly less accurate, slower, and with lower usability and higher cognitive workload compared to the eye-tracking camera and EyeLink board. Participants rated the eye-tracking camera as the most favorable AAC system on all measures.<br><br>CONCLUSIONS: The results demonstrated that while the P300 speller BCI was usable by most participants, it did not function as well as the eye-tracking camera and EyeLink board. The clinical use of the BCI is, therefore, currently difficult to justify for most individuals, particularly when considering the substantial cost and setup resourcing needed.<br><br>SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.21291384.}, }
@article {pmid36248616, year = {2023}, author = {Klein, F and Lührs, M and Benitez-Andonegui, A and Roehn, P and Kranczioch, C}, title = {Performance comparison of systemic activity correction in functional near-infrared spectroscopy for methods with and without short distance channels.}, journal = {Neurophotonics}, volume = {10}, number = {1}, pages = {013503}, pmid = {36248616}, issn = {2329-423X}, abstract = {Significance: Functional near-infrared spectroscopy (fNIRS) is a promising tool for neurofeedback (NFB) or brain-computer interfaces (BCIs). However, fNIRS signals are typically highly contaminated by systemic activity (SA) artifacts, and, if not properly corrected, NFB or BCIs run the risk of being based on noise instead of brain activity. This risk can likely be reduced by correcting for SA, in particular when short-distance channels (SDCs) are available. Literature comparing correction methods with and without SDCs is still sparse, specifically comparisons considering single trials are lacking. Aim: This study aimed at comparing the performance of SA correction methods with and without SDCs. Approach: Semisimulated and real motor task data of healthy older adults were used. Correction methods without SDCs included a simple and a more advanced spatial filter. Correction methods with SDCs included a regression approach considering only the closest SDC and two GLM-based methods, one including all eight SDCs and one using only two a priori selected SDCs as regressors. All methods were compared with data uncorrected for SA and correction performance was assessed with quality measures quantifying signal improvement and spatial specificity at single trial level. Results: All correction methods were found to improve signal quality and enhance spatial specificity as compared with the uncorrected data. Methods with SDCs usually outperformed methods without SDCs. Correction methods without SDCs tended to overcorrect the data. However, the exact pattern of results and the degree of differences observable between correction methods varied between semisimulated and real data, and also between quality measures. Conclusions: Overall, results confirmed that both Δ [ HbO ] and Δ [ HbR ] are affected by SA and that correction methods with SDCs outperform methods without SDCs. Nonetheless, improvements in signal quality can also be achieved without SDCs and should therefore be given priority over not correcting for SA.}, }
@article {pmid36247357, year = {2022}, author = {Chen, G and Zhang, X and Zhang, J and Li, F and Duan, S}, title = {A novel brain-computer interface based on audio-assisted visual evoked EEG and spatial-temporal attention CNN.}, journal = {Frontiers in neurorobotics}, volume = {16}, number = {}, pages = {995552}, pmid = {36247357}, issn = {1662-5218}, abstract = {OBJECTIVE: Brain-computer interface (BCI) can translate intentions directly into instructions and greatly improve the interaction experience for disabled people or some specific interactive applications. To improve the efficiency of BCI, the objective of this study is to explore the feasibility of an audio-assisted visual BCI speller and a deep learning-based single-trial event related potentials (ERP) decoding strategy.<br><br>APPROACH: In this study, a two-stage BCI speller combining the motion-onset visual evoked potential (mVEP) and semantically congruent audio evoked ERP was designed to output the target characters. In the first stage, the different group of characters were presented in the different locations of visual field simultaneously and the stimuli were coded to the mVEP based on a new space division multiple access scheme. And then, the target character can be output based on the audio-assisted mVEP in the second stage. Meanwhile, a spatial-temporal attention-based convolutional neural network (STA-CNN) was proposed to recognize the single-trial ERP components. The CNN can learn 2-dimentional features including the spatial information of different activated channels and time dependence among ERP components. In addition, the STA mechanism can enhance the discriminative event-related features by adaptively learning probability weights.<br><br>MAIN RESULTS: The performance of the proposed two-stage audio-assisted visual BCI paradigm and STA-CNN model was evaluated using the Electroencephalogram (EEG) recorded from 10 subjects. The average classification accuracy of proposed STA-CNN can reach 59.6 and 77.7% for the first and second stages, which were always significantly higher than those of the comparison methods (p < 0.05).<br><br>SIGNIFICANCE: The proposed two-stage audio-assisted visual paradigm showed a great potential to be used to BCI speller. Moreover, through the analysis of the attention weights from time sequence and spatial topographies, it was proved that STA-CNN could effectively extract interpretable spatiotemporal EEG features.}, }
@article {pmid36246365, year = {2022}, author = {Zhang, J and Wang, T and Zhang, Y and Lu, P and Shi, N and Zhu, W and Cai, C and He, N}, title = {Soft integration of a neural cells network and bionic interfaces.}, journal = {Frontiers in bioengineering and biotechnology}, volume = {10}, number = {}, pages = {950235}, pmid = {36246365}, issn = {2296-4185}, abstract = {Both glial cells and neurons can be considered basic computational units in neural networks, and the brain-computer interface (BCI) can play a role in awakening the latency portion and being sensitive to positive feedback through learning. However, high-quality information gained from BCI requires invasive approaches such as microelectrodes implanted under the endocranium. As a hard foreign object in the aqueous microenvironment, the soft cerebral cortex's chronic inflammation state and scar tissue appear subsequently. To avoid the obvious defects caused by hard electrodes, this review focuses on the bioinspired neural interface, guiding and optimizing the implant system for better biocompatibility and accuracy. At the same time, the bionic techniques of signal reception and transmission interfaces are summarized and the structural units with functions similar to nerve cells are introduced. Multiple electrical and electromagnetic transmissions, regulating the secretion of neuromodulators or neurotransmitters via nanofluidic channels, have been flexibly applied. The accurate regulation of neural networks from the nanoscale to the cellular reconstruction of protein pathways will make BCI the extension of the brain.}, }
@article {pmid36246355, year = {2022}, author = {Xu, H and Piao, L and Wu, Y and Liu, X}, title = {IFN-γ enhances the antitumor activity of attenuated salmonella-mediated cancer immunotherapy by increasing M1 macrophage and CD4 and CD8 T cell counts and decreasing neutrophil counts.}, journal = {Frontiers in bioengineering and biotechnology}, volume = {10}, number = {}, pages = {996055}, pmid = {36246355}, issn = {2296-4185}, abstract = {Bacteria-mediated cancer immunotherapy (BCI) inhibits tumor progression and has a synergistic antitumor effect when combined with chemotherapy. The anti- or pro-tumorigenic effects of interferon-γ (IFN-γ) are controversial; hence, we were interested in the antitumor effects of IFN-γ/BCI combination therapy. Here, we demonstrated that IFN-γ increased the tumor cell killing efficacy of attenuated Salmonella by prolonging the survival of tumor-colonizing bacteria via blockade of tumor-infiltrating neutrophil recruitment. In addition, IFN-γ attenuated Salmonella-stimulated immune responses by stimulating tumor infiltration by M1-like macrophages and CD4[+] and CD8[+] T cells, thereby facilitating tumor eradication. Taken together, these findings suggest that combination treatment with IFN-γ boosts the therapeutic response of BCI with S. tΔppGpp, suggesting that IFN-γ/BCI is a promising approach to immunotherapy.}, }
@article {pmid36246302, year = {2022}, author = {Collinger, JL and Krusienski, DJ}, title = {The 8[th] international brain-computer interface meeting, BCIs: the next frontier.}, journal = {Brain computer interfaces (Abingdon, England)}, volume = {9}, number = {2}, pages = {67-68}, pmid = {36246302}, issn = {2326-263X}, support = {R13 DC018466/DC/NIDCD NIH HHS/United States ; }, }
@article {pmid36245840, year = {2022}, author = {Sun, M}, title = {Study on Antidepressant Emotion Regulation Based on Feedback Analysis of Music Therapy with Brain-Computer Interface.}, journal = {Computational and mathematical methods in medicine}, volume = {2022}, number = {}, pages = {7200678}, pmid = {36245840}, issn = {1748-6718}, mesh = {Aged ; Antidepressive Agents/therapeutic use ; *Brain-Computer Interfaces ; Cholinergic Antagonists ; *Emotional Regulation ; Emotions/physiology ; Feedback ; Humans ; *Music/psychology ; *Music Therapy ; }, abstract = {In today's society, people with poor mental ability are prone to neuropsychiatric diseases such as anxiety, ADHD, and depression due to long-term negative emotions. Although conventional Western medicine has certain curative effect, these drugs have significant anticholinergic side effects central toxicity as well as cardiovascular and gastrointestinal side effects which limit their application in the elderly. At present, several antidepressants used in clinic have certain limitations. According to the symptoms of depression, this paper proposes a feedback emotion regulation method of brain-computer interface music therapy. This method uses special music stimulation to regulate the release of inhibiting sex hormones in the body, reduce the influence of negative emotions on the internal environment of the body, and maintain the steady state of the body. In this method, EEG is used as the emotional control signal of depressed patients, and this biological signal is transformed into music that depressed patients can understand, so as to clarify their physiological and psychological state and realize emotional self-regulation by feedback.}, }
@article {pmid36241018, year = {2022}, author = {Chen, J and Meng, X and Liu, Z and Shang, B and Chang, C and Ku, Y}, title = {Decoding semantics from intermodulation responses in frequency-tagged stereotactic EEG.}, journal = {Journal of neuroscience methods}, volume = {382}, number = {}, pages = {109727}, doi = {10.1016/j.jneumeth.2022.109727}, pmid = {36241018}, issn = {1872-678X}, mesh = {Humans ; *Evoked Potentials, Visual ; Semantics ; *Brain-Computer Interfaces ; Photic Stimulation ; Electroencephalography ; }, abstract = {BACKGROUND: Humans perform object recognition using holistic processing, which is different from computers. Intermodulation responses in the steady-state visual evoked potential (SSVEP) of scalp electroencephalography (EEG) have recently been used as an objective label for holistic processing.<br><br>NEW METHOD: Using stereotactic EEG (sEEG) to record SSVEP directly from inside of the brain, we aimed to decode Chinese characters from non-characters with activation from multiple brain areas including occipital, parietal, temporal, and frontal cortices.<br><br>RESULTS: Semantic categories could be decoded from responses at the intermodulation frequency with high accuracy (80%-90%), but not the base frequency. Moreover, semantic categories could be decoded with activation from multiple areas including temporal, parietal, and frontal areas.<br><br>Previous studies investigated holistic processing in faces and words with frequency-tagged scalp EEGs. The current study extended the results to stereotactic EEG signals directly recorded from the brain.<br><br>CONCLUSIONS: The human brain applies holistic processing in recognizing objects like Chinese characters. Our findings could be extended to an add-on feature in the existing SSVEP BCI speller.}, }
@article {pmid36240942, year = {2022}, author = {Wang, W and Li, B and Wang, H}, title = {A Novel End-to-end Network Based on a bidirectional GRU and a Self-Attention Mechanism for Denoising of Electroencephalography Signals.}, journal = {Neuroscience}, volume = {505}, number = {}, pages = {10-20}, doi = {10.1016/j.neuroscience.2022.10.006}, pmid = {36240942}, issn = {1873-7544}, mesh = {*Algorithms ; Electroencephalography/methods ; *Brain-Computer Interfaces ; Neural Networks, Computer ; Signal Processing, Computer-Assisted ; }, abstract = {Electroencephalography (EEG) signals are nonlinear and non-stationary sequences that carry much information. However, physiological signals from other body regions may readily interfere with EEG signal capture, having a significant unfavorable influence on subsequent analysis. Therefore, signal denoising is a crucial step in EEG signal processing. This paper proposes a bidirectional gated recurrent unit (GRU) network based on a self-attention mechanism (BG-Attention) for extracting pure EEG signals from noise-contaminated EEG signals. The bidirectional GRU network can simultaneously capture past and future information while processing continuous time sequence. And by paying different levels of attention to the content of varying importance, the model can learn more significant feature of EEG signal sequences, highlighting the contribution of essential samples to denoising. The proposed model is evaluated on the EEGdenoiseNet data set. We compared the proposed model with a fully connected network (FCNN), the one-dimensional residual convolutional neural network (1D-ResCNN), and a recurrent neural network (RNN). The experimental results show that the proposed model can reconstruct a clear EEG waveform with a decent signal-to-noise ratio (SNR) and the relative root mean squared error (RRMSE) value. This study demonstrates the potential of BG-Attention in the pre-processing phase of EEG experiments, which has significant implications for medical technology and brain-computer interface (BCI) applications.}, }
@article {pmid36240727, year = {2022}, author = {Faes, A and Hulle, MMV}, title = {Finger movement and coactivation predicted from intracranial brain activity using extended block-term tensor regression.}, journal = {Journal of neural engineering}, volume = {19}, number = {6}, pages = {}, doi = {10.1088/1741-2552/ac9a75}, pmid = {36240727}, issn = {1741-2552}, mesh = {Humans ; *Movement/physiology ; Fingers/physiology ; Brain/physiology ; *Brain-Computer Interfaces ; Electrocorticography/methods ; }, abstract = {Objective.We introduce extended Block-Term Tensor Regression (eBTTR), a novel regression method designed to account for the multilinear nature of human intracranial finger movement recordings.Approach.The proposed method relies on recursive Tucker decomposition combined with automatic component extraction.Main results.eBTTR outperforms state-of-the-art regression approaches, including multilinear and deep learning ones, in accurately predicting finger trajectories as well as unintentional finger coactivations.Significance.eBTTR rivals state-of-the-art approaches while being less computationally expensive which is an advantage when intracranial electrodes are implanted acutely, as part of the patient's presurgical workup, limiting time for decoder development and testing.}, }
@article {pmid36237407, year = {2022}, author = {Fu, R and Xu, D and Li, W and Shi, P}, title = {Single-trial motor imagery electroencephalogram intention recognition by optimal discriminant hyperplane and interpretable discriminative rectangle mixture model.}, journal = {Cognitive neurodynamics}, volume = {16}, number = {5}, pages = {1073-1085}, pmid = {36237407}, issn = {1871-4080}, abstract = {UNLABELLED: Spatial filtering is widely used in brain-computer interface (BCI) systems to augmented signal characteristics of electroencephalogram (EEG) signals. In this study, a spatial domain filtering based EEG feature extraction method, optimal discriminant hyperplane-common spatial subspace decomposition (ODH-CSSD) is proposed. Specifically, the multi-dimensional EEG features were extracted from the original EEG signals by common space subspace decomposition (CSSD) algorithm, and the optimal feature criterion was established to find the multi-dimensional optimal projection space. A classic method of data dimension optimizing is using the eigenvectors of a lumped covariance matrix corresponding to the maximum eigenvalues. Then, the cost function is defined as the extreme value of the discriminant criterion, and the orthogonal N discriminant vectors corresponding to the N extreme value of the criterion are solved and constructed into the N-dimensional optimal feature space. Finally, the multi-dimensional EEG features are projected into the N-dimensional optimal projection space to obtain the optimal N-dimensional EEG features. Moreover, this study involves the extraction of two-dimensional and three-dimensional optimal EEG features from motor imagery EEG datasets, and the optimal EEG features are identified using the interpretable discriminative rectangular mixture model (DRMM). Experimental results show that the accuracy of DRMM to identify two-dimensional optimal features is more than 0.91, and the highest accuracy even reaches 0.975. Meanwhile, DRMM has the most stable recognition accuracy for two-dimensional optimal features, and its average clustering accuracy reaches 0.942, the gap between the accuracy of the DRMM with the accuracy of the FCM and K-means can reach 0.26. And the optimal three-dimensional features, for most subjects, the clustering accuracy of DRMM is higher than that of FCM and K-means. In general, the decision rectangle obtained by DRMM can clearly explain the difference of each cluster, notably, the optimization of multidimensional EEG features by optimal projection is superior to Fisher's ratio, and this method provides an alternative for the application of BCI.<br><br>SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-021-09768-w.}, }
@article {pmid36237403, year = {2022}, author = {Dong, E and Zhang, H and Zhu, L and Du, S and Tong, J}, title = {A multi-modal brain-computer interface based on threshold discrimination and its application in wheelchair control.}, journal = {Cognitive neurodynamics}, volume = {16}, number = {5}, pages = {1123-1133}, pmid = {36237403}, issn = {1871-4080}, abstract = {In this study, we propose a novel multi-modal brain-computer interface (BCI) system based on the threshold discrimination, which is proposed for the first time to distinguish between SSVEP and MI potentials. The system combines these two heterogeneous signals to increase the number of control commands and improve the performance of asynchronous control of external devices. In this research, an electric wheelchair is controlled as an example. The user can continuously control the wheelchair to turn left/right through motion imagination (MI) by imagining left/right-hand movement and generate another 6 commands for the wheelchair control by focusing on the SSVEP stimulation panel. Ten subjects participated in a MI training session and eight of them completed a mobile obstacle-avoidance experiment in a complex environment requesting high control accuracy for successful manipulation. Comparing with the single-modal BCI-controlled wheelchair system, the results demonstrate that the proposed multi-modal method is effective by providing more satisfactory control accuracy, and show the potential of BCI-controlled systems to be applied in complex daily tasks.}, }
@article {pmid36237402, year = {2022}, author = {Bagherzadeh, S and Maghooli, K and Shalbaf, A and Maghsoudi, A}, title = {Emotion recognition using effective connectivity and pre-trained convolutional neural networks in EEG signals.}, journal = {Cognitive neurodynamics}, volume = {16}, number = {5}, pages = {1087-1106}, pmid = {36237402}, issn = {1871-4080}, abstract = {Convolutional Neural Networks (CNN) have recently made considerable advances in the field of biomedical signal processing. These methodologies can assist in emotion recognition for affective brain computer interface. In this paper, a novel emotion recognition system based on the effective connectivity and the fine-tuned CNNs from multichannel Electroencephalogram (EEG) signal is presented. After preprocessing EEG signals, the relationships among 32 channels of EEG in the form of effective brain connectivity analysis which represents information flow between regions are computed by direct Directed Transfer Function (dDTF) method which yields a 32*32 image. Then, these constructed images from EEG signals for each subject were fed as input to four versions of pre-trained CNN models, AlexNet, ResNet-50, Inception-v3 and VGG-19 and the parameters of these models are fine-tuned, independently. The proposed deep learning architectures automatically learn patterns in the constructed image of the EEG signals in frequency bands. The efficiency of the proposed approach is evaluated on MAHNOB-HCI and DEAP databases. The experiments for classifying five emotional states show that the ResNet-50 applied on dDTF images in alpha band achieves best results due to specific architecture which captures the brain connectivity, efficiently. The accuracy and F1-score values for MAHNOB-HCI were obtained 99.41, 99.42 and for DEAP databases, 98.17, and 98.23. Newly proposed model is capable of effectively analyzing the brain function using information flow from multichannel EEG signals using effective connectivity measure of dDTF and ResNet-50.}, }
@article {pmid36237399, year = {2022}, author = {Tao, Q and Jiang, L and Li, F and Qiu, Y and Yi, C and Si, Y and Li, C and Zhang, T and Yao, D and Xu, P}, title = {Dynamic networks of P300-related process.}, journal = {Cognitive neurodynamics}, volume = {16}, number = {5}, pages = {975-985}, pmid = {36237399}, issn = {1871-4080}, abstract = {P300 as an effective biomarker to index attention and memory has been widely used for brain-computer interface, cognitive evaluation, and clinical diagnosis. To evoke clear P300, an oddball paradigm consisting of two types of stimuli, i.e., infrequent target stimuli and frequent standard stimuli, is usually used. However, to simply and quickly explore the P300-related process, previous studies predominately focused on the target condition but ignored the fusion of target and standard conditions, as well as the difference of brain networks between them. Therefore, in this study, we used the hidden Markov model to investigate the fused multi-conditional electroencephalogram dataset of P300, aiming to effectively identify the underlying brain networks and explore the difference between conditions. Specifically, the inferred networks, including their transition sequences and spatial distributions, were scrutinized first. Then, we found that the difference between target and standard conditions was mainly concentrated in two phases. One was the stimulation phase that mainly related to the cortical activities of the postcentral gyrus and superior parietal lobule, and the other corresponded to the response phase that involved the activities of superior and medial frontal gyri. This might be attributed to distinct cognitive functions, as the stimulation phase is associated with visual information integration whereas the response phase involves stimulus discrimination and behavior control. Taken together, the current work explored dynamic networks underlying the P300-related process and provided a complementary understanding of distinct P300 conditions, which may contribute to the design of P300-related brain-machine systems.}, }
@article {pmid36236694, year = {2022}, author = {Antony, MJ and Sankaralingam, BP and Mahendran, RK and Gardezi, AA and Shafiq, M and Choi, JG and Hamam, H}, title = {Classification of EEG Using Adaptive SVM Classifier with CSP and Online Recursive Independent Component Analysis.}, journal = {Sensors (Basel, Switzerland)}, volume = {22}, number = {19}, pages = {}, pmid = {36236694}, issn = {1424-8220}, mesh = {Algorithms ; Artifacts ; *Brain-Computer Interfaces ; Discriminant Analysis ; Electroencephalography/methods ; Signal Processing, Computer-Assisted ; *Support Vector Machine ; }, abstract = {An efficient feature extraction method for two classes of electroencephalography (EEG) is demonstrated using Common Spatial Patterns (CSP) with optimal spatial filters. However, the effects of artifacts and non-stationary uncertainty are more pronounced when CSP filtering is used. Furthermore, traditional CSP methods lack frequency domain information and require many input channels. Therefore, to overcome this shortcoming, a feature extraction method based on Online Recursive Independent Component Analysis (ORICA)-CSP is proposed. For EEG-based brain-computer interfaces (BCIs), especially online and real-time BCIs, the most widely used classifiers used to be linear discriminant analysis (LDA) and support vector machines (SVM). Previous evaluations clearly show that SVMs generally outperform other classifiers in terms of performance. In this case, Adaptive Support Vector Machine (A-SVM) is used for classification together with the ORICA-CSP method. The results are promising, and the experiments are performed on EEG data of 4 classes' motor images, namely Dataset 2a of BCI Competition IV.}, }
@article {pmid36234792, year = {2022}, author = {Barros, MT and Siljak, H and Mullen, P and Papadias, C and Hyttinen, J and Marchetti, N}, title = {Objective Supervised Machine Learning-Based Classification and Inference of Biological Neuronal Networks.}, journal = {Molecules (Basel, Switzerland)}, volume = {27}, number = {19}, pages = {}, pmid = {36234792}, issn = {1420-3049}, mesh = {Humans ; Machine Learning ; *Neural Networks, Computer ; Neurons ; *Supervised Machine Learning ; Support Vector Machine ; }, abstract = {The classification of biological neuron types and networks poses challenges to the full understanding of the human brain's organisation and functioning. In this paper, we develop a novel objective classification model of biological neuronal morphology and electrical types and their networks, based on the attributes of neuronal communication using supervised machine learning solutions. This presents advantages compared to the existing approaches in neuroinformatics since the data related to mutual information or delay between neurons obtained from spike trains are more abundant than conventional morphological data. We constructed two open-access computational platforms of various neuronal circuits from the Blue Brain Project realistic models, named Neurpy and Neurgen. Then, we investigated how we could perform network tomography with cortical neuronal circuits for the morphological, topological and electrical classification of neurons. We extracted the simulated data of 10,000 network topology combinations with five layers, 25 morphological type (m-type) cells, and 14 electrical type (e-type) cells. We applied the data to several different classifiers (including Support Vector Machine (SVM), Decision Trees, Random Forest, and Artificial Neural Networks). We achieved accuracies of up to 70%, and the inference of biological network structures using network tomography reached up to 65% of accuracy. Objective classification of biological networks can be achieved with cascaded machine learning methods using neuron communication data. SVM methods seem to perform better amongst used techniques. Our research not only contributes to existing classification efforts but sets the road-map for future usage of brain-machine interfaces towards an in vivo objective classification of neurons as a sensing mechanism of the brain's structure.}, }
@article {pmid36234564, year = {2022}, author = {Sharifi, S and Maleki Dizaj, S and Ahmadian, E and Karimpour, A and Maleki, A and Memar, MY and Ghavimi, MA and Dalir Abdolahinia, E and Goh, KW}, title = {A Biodegradable Flexible Micro/Nano-Structured Porous Hemostatic Dental Sponge.}, journal = {Nanomaterials (Basel, Switzerland)}, volume = {12}, number = {19}, pages = {}, pmid = {36234564}, issn = {2079-4991}, abstract = {A biodegradable micro/nano-structured porous hemostatic gelatin-based sponge as a dentistry surgery foam was prepared using a freeze-drying method. In vitro function evaluation tests were performed to ensure its hemostatic effect. Biocompatibility tests were also performed to show the compatibility of the sponge on human fetal foreskin fibroblasts (HFFF2) cells and red blood cells (RBCs). Then, 10 patients who required the extraction of two teeth were selected, and after teeth extraction, for dressing, the produced sponge was placed in one of the extracavities while a commercial sponge was placed in the cavity in the other tooth as a control. The total weight of the absorbed blood in each group was compared. The results showed a porous structure with micrometric and nanometric pores, flexibility, a two-week range for degradation, and an ability to absorb blood 35 times its weight in vitro. The prepared sponge showed lower blood clotting times (BCTs) (243.33 ± 2.35 s) and a lower blood clotting index (BCI) (10.67 ± 0.004%) compared to two commercial sponges that displayed its ability for faster coagulation and good hemostatic function. It also had no toxic effects on the HFFF2 cells and RBCs. The clinical assessment showed a better ability of blood absorption for the produced sponge (p-value = 0.0015). The sponge is recommended for use in dental surgeries because of its outstanding abilities.}, }
@article {pmid36230358, year = {2022}, author = {Miljević, M and Čabrilo, B and Budinski, I and Rajičić, M and Bajić, B and Bjelić-Čabrilo, O and Blagojević, J}, title = {Host-Parasite Relationship-Nematode Communities in Populations of Small Mammals.}, journal = {Animals : an open access journal from MDPI}, volume = {12}, number = {19}, pages = {}, pmid = {36230358}, issn = {2076-2615}, abstract = {Nematode burdens and variation in morphological characteristics were assessed in eighty-eight animals from three host species (Apodemus sylvaticus, Apodemus flavicollis, and Myodes glareolus) from eight localities in Serbia. In total, 15 species of nematodes were identified, and the overall mean parasite species richness (IndPSR) was 1.61 per animal (1.98 in A. flavicollis, 1.43 in M. glareolus, and 0.83 in A. sylvaticus). Furthermore, the studied host species significantly differed in individual parasite load (IndPL) and in the following morphological characters: spleen mass, body condition index (BCI), and body mass. We aimed to analyze the relationship between the burden of intestinal nematodes, on one hand, and the body conditions of the host and its capability to develop immune defends on the other. Spleen mass was considered as a measure of immune response. In all host species, larger animals with a better condition (higher BCI) were infected with more parasites species (IndPSR), while parasite load was not related to BCI. Only in A. flavicollis were males significantly larger, but females of the same sizes were infected with more parasite species. This female-biased parasitism is contrary to the theoretical expectation that males should be more parasitized, being larger, more active, with a wider home range. Although the spleen size was significantly correlated with body condition and body mass, IndPSR was not related to spleen mass in any studied species, but in M. galareolus, we found that a smaller spleen was related to higher infection intensity (IndPL).}, }
@article {pmid36228894, year = {2022}, author = {Brickwedde, M and Bezsudnova, Y and Kowalczyk, A and Jensen, O and Zhigalov, A}, title = {Application of rapid invisible frequency tagging for brain computer interfaces.}, journal = {Journal of neuroscience methods}, volume = {382}, number = {}, pages = {109726}, doi = {10.1016/j.jneumeth.2022.109726}, pmid = {36228894}, issn = {1872-678X}, mesh = {Humans ; *Evoked Potentials, Visual ; *Brain-Computer Interfaces ; Photic Stimulation/methods ; Electroencephalography/methods ; Magnetoencephalography ; }, abstract = {BACKGROUND: Brain-computer interfaces (BCI) based on steady-state visual evoked potentials (SSVEPs/SSVEFs) are among the most commonly used BCI systems. They require participants to covertly attend to visual objects flickering at specified frequencies. The attended location is decoded online by analysing the power of neuronal responses at the flicker frequency.<br><br>NEW METHOD: We implemented a novel rapid invisible frequency-tagging technique, utilizing a state-of-the-art projector with refresh rates of up to 1440&#xa0;Hz. We flickered the luminance of visual objects at 56 and 60&#xa0;Hz, which was invisible to participants but produced strong neuronal responses measurable with magnetoencephalography (MEG). The direction of covert attention, decoded from frequency-tagging responses, was used to control an online BCI PONG game.<br><br>RESULTS: Our results show that seven out of eight participants were able to play the pong game controlled by the frequency-tagging signal, with average accuracies exceeding 60 %. Importantly, participants were able to modulate the power of the frequency-tagging response within a 1-second interval, while only seven occipital sensors were required to reliably decode the neuronal response.<br><br>In contrast to existing SSVEP-based BCI systems, rapid frequency-tagging does not produce a visible flicker. This extends the time-period participants can use it without fatigue, by avoiding distracting visual input. Furthermore, higher frequencies increase the temporal resolution of decoding, resulting in higher communication rates.<br><br>CONCLUSION: Using rapid invisible frequency-tagging opens new avenues for fundamental research and practical applications. In combination with novel optically pumped magnetometers (OPMs), it could facilitate the development of high-speed and mobile next-generation BCI systems.}, }
@article {pmid36228578, year = {2022}, author = {Tong, J and Wei, X and Dong, E and Sun, Z and Du, S and Duan, F}, title = {Hybrid mental tasks based human computer interface via integration of pronunciation and motor imagery.}, journal = {Journal of neural engineering}, volume = {19}, number = {5}, pages = {}, doi = {10.1088/1741-2552/ac9a01}, pmid = {36228578}, issn = {1741-2552}, mesh = {Humans ; *Brain-Computer Interfaces ; Imagination ; Electroencephalography/methods ; Imagery, Psychotherapy ; Computers ; Algorithms ; }, abstract = {Objective.Among the existing active brain-computer interfaces (BCI), the motor imagination (MI) is widely used. To operate the MI BCI effectively, subjects need to carry out trainings on corresponding imagining tasks. Here, we studied how to reduce the discomfort and fatigue of active BCI imaginary tasks and the inability to concentrate on them while improving the accuracy.Approach.This paper proposes a hybrid BCI composed of MI and pronunciation imagination (PI). The electroencephalogram signals of ten subjects are recognized by the adaptive Riemannian distance classification and the improved frequency selective filter-bank Common Spatial Pattern recognition.Main results.The results show that under the new paradigm with the combination of MI and PI, the recognition accuracy is higher than the MI alone. The highest recognition rate of the proposed hybrid system can reach more than 90%. Furthermore, through the subjects' scoring results of the operation difficulty, it is concluded that the designed hybrid paradigm is more operable than the traditional BCI paradigm.Significance.The separable tasks in the active BCI are limited and the accuracy needs to be improved. The new hybrid paradigm proposed by us improves the accuracy and operability of the active BCI system, providing a new possibility for the research direction of the active BCI.}, }
@article {pmid36226318, year = {2022}, author = {Wang, X and Li, H}, title = {Chronic high-fat diet induces overeating and impairs synaptic transmission in feeding-related brain regions.}, journal = {Frontiers in molecular neuroscience}, volume = {15}, number = {}, pages = {1019446}, pmid = {36226318}, issn = {1662-5099}, abstract = {Obesity is linked to overeating, which can exacerbate unhealthy weight gain. However, the mechanisms for mediating such linkages are elusive. In the current study, we hypothesized that synaptic remodeling occurs in feeding-related brain regions of obese mice. To investigate this, we established a high-fat diet (HFD)-induced obese mouse model and observed that these mice consumed excessive calories. The effect of chronic HFD feeding on lipid droplet accumulation in different brain structures was also investigated. We found that lipid droplets accumulated on the ependyma of the third ventricle (3V), which is surrounded by key areas of the hypothalamus that are involved in feeding. Then, the spontaneous synaptic activity of miniature excitatory postsynaptic current (mEPSC) and miniature inhibitory postsynaptic current (mIPSC) was recorded in these hypothalamic areas. HFD induced a decreased amplitude of mEPSC in the arcuate nucleus (ARC) and the ventromedial hypothalamus (VMH), meanwhile, increased the frequency in the VMH. In addition, HFD reduced the frequency of mIPSC in the lateral hypothalamus (LH) and increased the amplitude of mIPSC in the paraventricular nucleus of the hypothalamus (PVH). Subsequently, we also measured the synaptic activity of nucleus accumbens (NAc) neurons, which play a vital role in the hedonic aspect of eating, and discovered that HFD diminished the frequency of both mEPSC and mIPSC in the NAc. These findings suggest that chronic HFD feeding leads to lipid accumulation and synaptic dysfunction in specific brain regions, which are associated with energy homeostasis and reward regulation, and these impairments may lead to the overeating of obesity.}, }
@article {pmid36222713, year = {2022}, author = {Huang, WC and Hung, CH and Lin, YW and Zheng, YC and Lei, WL and Lu, HE}, title = {Electrically Copolymerized Polydopamine Melanin/Poly(3,4-ethylenedioxythiophene) Applied for Bioactive Multimodal Neural Interfaces with Induced Pluripotent Stem Cell-Derived Neurons.}, journal = {ACS biomaterials science &amp; engineering}, volume = {8}, number = {11}, pages = {4807-4818}, doi = {10.1021/acsbiomaterials.2c00822}, pmid = {36222713}, issn = {2373-9878}, mesh = {Humans ; *Induced Pluripotent Stem Cells ; Melanins ; Polymers/pharmacology ; Neurons/physiology ; }, abstract = {Multimodal neural interfaces include combined functions of electrical neuromodulation and synchronic monitoring of neurochemical and physiological signals in one device. The remarkable biocompatibility and electrochemical performance of polystyrene sulfonate-doped poly(3,4-ethylenedioxythiophene) (PEDOT:PSS) have made it the most recommended conductive polymer neural electrode material. However, PEDOT:PSS formed by electrochemical deposition, called PEDOT/PSS, often need multiple doping to improve structural instability in moisture, resolve the difficulties of functionalization, and overcome the poor cellular affinity. In this work, inspired by the catechol-derived adhesion and semiconductive properties of polydopamine melanin (PDAM), we used electrochemical oxidation polymerization to develop PDAM-doped PEDOT (PEDOT/PDAM) as a bioactive multimodal neural interface that permits robust electrochemical performance, structural stability, analyte-trapping capacity, and neural stem cell affinity. The use of potentiodynamic scans resolved the problem of copolymerizing 3,4-ethylenedioxythiophene (EDOT) and dopamine (DA), enabling the formation of PEDOT/PDAM self-assembled nanodomains with an ideal doping state associated with remarkable current storage and charge transfer capacity. Owing to the richness of hydrogen bond donors/acceptors provided by the hydroxyl groups of PDAM, PEDOT/PDAM presented better electrochemical and mechanical stability than PEDOT/PSS. It has also enabled high sensitivity and selectivity in the electrochemical detection of DA. Different from PEDOT/PSS, which inhibited the survival of human induced pluripotent stem cell-derived neural progenitor cells, PEDOT/PDAM maintained cell proliferation and even promoted cell differentiation into neuronal networks. Finally, PEDOT/PDAM was modified on a commercialized microelectrode array system, which resulted in the reduction of impedance by more than one order of magnitude; this significantly improved the resolution and reduced the noise of neuronal signal recording. With these advantages, PEDOT/PDAM is anticipated to be an efficient bioactive multimodal neural electrode material with potential application to brain-machine interfaces.}, }
@article {pmid36222132, year = {2022}, author = {Zhong, D and Zhan, Z and Zhang, J and Liu, Y and He, Z}, title = {SMYD3 regulates the abnormal proliferation of non-small-cell lung cancer cells via the H3K4me3/ANO1 axis.}, journal = {Journal of biosciences}, volume = {47}, number = {}, pages = {}, pmid = {36222132}, issn = {0973-7138}, mesh = {Anoctamin-1/genetics/metabolism ; *Carcinoma, Non-Small-Cell Lung/genetics ; Cell Line, Tumor ; Cell Proliferation/genetics ; Chromatin ; Gene Expression Regulation, Neoplastic ; Histone-Lysine N-Methyltransferase/genetics ; Histones ; Humans ; *Lung Neoplasms/genetics ; Lysine/genetics ; Neoplasm Proteins/genetics/metabolism ; RNA, Messenger/genetics ; }, abstract = {Non-small-cell lung cancer (NSCLC) is the most prevalent type of lung cancer. This study evaluated the mechanism of histone methyltransferase SET and MYND domain-containing 3 (SMYD3) in the abnormal proliferation of NSCLC cells. The human bronchial epithelial cell (HBEC) line (16HBE) and NSCLC cell lines (H1299, A549, H460, and H1650) were collected. A549 and H1650 cells were transfected with si-SMYD3 and Anoctamin-1 (ANO1) and their negative controls or treated with BCI-121, or A549 cells were treated with CPI-455. SMYD3, H3 lysine 4 tri-methylation (H3K4me3), and ANO1 levels in the cells were detected. The proliferation ability of A549 and H1650 cells were examined. We found that SMYD3, H3K4me3, and ANO1 were highly expressed in NSCLC cell lines. Silencing SMYD3 or SMYD3 activity in A549 and H1650 cells inhibited the cell proliferation ability and decreased H3K4me3 level and ANO1 mRNA level in the cells. H3K4me3 upregulation orANO1 overexpression reversed the inhibitory effects of silencing SMYD3 on the abnormal proliferation of NSCLC cells. Chromatin-Immunoprecipitation (Ch-IP) assay detected that SMYD3 bound to and enriched in the ANO1 promoter region, and the ANO1 promoter region was enriched with H3K4me3. Collectively, SMYD3 promoted ANO1 transcription by upregulating H3K4me3 in the ANO1 promoter region, thus facilitating the abnormal proliferation of NSCLC cells.}, }
@article {pmid36220896, year = {2022}, author = {Yang, YY and Hwang, AH and Wu, CT and Huang, TR}, title = {Person-identifying brainprints are stably embedded in EEG mindprints.}, journal = {Scientific reports}, volume = {12}, number = {1}, pages = {17031}, pmid = {36220896}, issn = {2045-2322}, mesh = {Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Twins, Dizygotic ; Twins, Monozygotic ; }, abstract = {Electroencephalography (EEG) signals measured under fixed conditions have been exploited as biometric identifiers. However, what contributes to the uniqueness of one's brain signals remains unclear. In the present research, we conducted a multi-task and multi-week EEG study with ten pairs of monozygotic (MZ) twins to examine the nature and components of person-identifiable brain signals. Through machine-learning analyses, we uncovered a person-identifying EEG component that served as "base signals" shared across tasks and weeks. Such task invariance and temporal stability suggest that these person-identifying EEG characteristics are more of structural brainprints than functional mindprints. Moreover, while these base signals were more similar within than between MZ twins, it was still possible to distinguish twin siblings, particularly using EEG signals coming primarily from late rather than early developed areas in the brain. Besides theoretical clarifications, the discovery of the EEG base signals has practical implications for privacy protection and the application of brain-computer interfaces.}, }
@article {pmid36219654, year = {2022}, author = {Shen, X and Zhang, X and Huang, Y and Chen, S and Yu, Z and Wang, Y}, title = {Intermediate Sensory Feedback Assisted Multi-Step Neural Decoding for Reinforcement Learning Based Brain-Machine Interfaces.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {30}, number = {}, pages = {2834-2844}, doi = {10.1109/TNSRE.2022.3210700}, pmid = {36219654}, issn = {1558-0210}, mesh = {Animals ; Rats ; *Brain-Computer Interfaces ; Feedback, Sensory ; Reinforcement, Psychology ; Learning ; Movement ; }, abstract = {Reinforcement-learning (RL)-based brain-machine interfaces (BMIs) interpret dynamic neural activity into movement intention without patients' real limb movements, which is promising for clinical applications. A movement task generally requires the subjects to reach the target within one step and rewards the subjects instantaneously. However, a real BMI scenario involves tasks that require multiple steps, during which sensory feedback is provided to indicate the status of the prosthesis, and the reward is only given at the end of the trial. Actually, subjects internally evaluate the sensory feedback to adjust motor activity. Existing RL-BMI tasks have not fully utilized the internal evaluation from the brain upon the sensory feedback to guide the decoder training, and there lacks an effective tool to assign credit for the multi-step decoding task. We propose first to extract intermediate guidance from the medial prefrontal cortex (mPFC) to assist the learning of multi-step decoding in an RL framework. To effectively explore the neural-action mapping in a large state-action space, a temporal difference (TD) method is incorporated into quantized attention-gated kernel reinforcement learning (QAGKRL) to assign the credit over the temporal sequence of movement, but also discriminate spatially in the Reproducing Kernel Hilbert Space (RKHS). We test our approach on the data collected from the primary motor cortex (M1) and the mPFC of rats when they brain control the cursor to reach the target within multiple steps. Compared with the models which only utilize the final reward, the intermediate evaluation interpreted from the mPFC can help improve the prediction accuracy by 10.9% on average across subjects, with faster convergence and more stability. Moreover, our proposed algorithm further increases 18.2% decoding accuracy compared with existing TD-RL methods. The results reveal the possibility of achieving better multi-step decoding performance for more complicated BMI tasks.}, }
@article {pmid36216898, year = {2022}, author = {Wang, QQ and Hussain, L and Yu, PH and Yang, C and Zhu, CY and Ma, YF and Wang, SC and Yang, T and Kang, YY and Yu, WJ and Maimaitiyiming, Y and Naranmandura, H}, title = {Hyperthermia promotes degradation of the acute promyelocytic leukemia driver oncoprotein ZBTB16/RARα.}, journal = {Acta pharmacologica Sinica}, volume = {}, number = {}, pages = {}, pmid = {36216898}, issn = {1745-7254}, abstract = {The acute promyelocytic leukemia (APL) driver ZBTB16/RARα is generated by the t(11;17) (q23;q21) chromosomal translocation, which is resistant to combined treatment of all-trans retinoic acid (ATRA) and arsenic trioxide (ATO) or conventional chemotherapy, resulting in extremely low survival rates. In the current study, we investigated the effects of hyperthermia on the oncogenic fusion ZBTB16/RARα protein to explore a potential therapeutic approach for this variant APL. We showed that Z/R fusion protein expressed in HeLa cells was resistant to ATO, ATRA, and conventional chemotherapeutic agents. However, mild hyperthermia (42 °C) rapidly destabilized the ZBTB16/RARα fusion protein expressed in HeLa, 293T, and OCI-AML3 cells, followed by robust ubiquitination and proteasomal degradation. In contrast, hyperthermia did not affect the normal (i.e., unfused) ZBTB16 and RARα proteins, suggesting a specific thermal sensitivity of the ZBTB16/RARα fusion protein. Importantly, we found that the destabilization of ZBTB16/RARα was the initial step for oncogenic fusion protein degradation by hyperthermia, which could be blocked by deletion of nuclear receptor corepressor (NCoR) binding sites or knockdown of NCoRs. Furthermore, SIAH2 was identified as the E3 ligase participating in hyperthermia-induced ubiquitination of ZBTB16/RARα. In short, these results demonstrate that hyperthermia could effectively destabilize and subsequently degrade the ZBTB16/RARα fusion protein in an NCoR-dependent manner, suggesting a thermal-based therapeutic strategy that may improve the outcome in refractory ZBTB16/RARα-driven APL patients in the clinic.}, }
@article {pmid36215972, year = {2022}, author = {Merken, L and Schelles, M and Ceyssens, F and Kraft, M and Janssen, P}, title = {Thin flexible arrays for long-term multi-electrode recordings in macaque primary visual cortex.}, journal = {Journal of neural engineering}, volume = {19}, number = {6}, pages = {}, doi = {10.1088/1741-2552/ac98e2}, pmid = {36215972}, issn = {1741-2552}, abstract = {Objective.Basic, translational and clinical neuroscience are increasingly focusing on large-scale invasive recordings of neuronal activity. However, in large animals such as nonhuman primates and humans-in which the larger brain size with sulci and gyri imposes additional challenges compared to rodents, there is a huge unmet need to record from hundreds of neurons simultaneously anywhere in the brain for long periods of time. Here, we tested the electrical and mechanical properties of thin, flexible multi-electrode arrays (MEAs) inserted into the primary visual cortex of two macaque monkeys, and assessed their magnetic resonance imaging (MRI) compatibility and their capacity to record extracellular activity over a period of 1 year.Approach.To allow insertion of the floating arrays into the visual cortex, the 20 by 100µm[2]shafts were temporarily strengthened by means of a resorbable poly(lactic-co-glycolic acid) coating.Main results. After manual insertion of the arrays, theex vivoandin vivoMRI compatibility of the arrays proved to be excellent. We recorded clear single-unit activity from up to 50% of the electrodes, and multi-unit activity (MUA) on 60%-100% of the electrodes, which allowed detailed measurements of the receptive fields and the orientation selectivity of the neurons. Even 1 year after insertion, we obtained significant MUA responses on 70%-100% of the electrodes, while the receptive fields remained remarkably stable over the entire recording period.Significance.Thus, the thin and flexible MEAs we tested offer several crucial advantages compared to existing arrays, most notably in terms of brain tissue compliance, scalability, and brain coverage. Future brain-machine interface applications in humans may strongly benefit from this new generation of chronically implanted MEAs.}, }
@article {pmid36213754, year = {2022}, author = {Mitskopoulos, L and Amvrosiadis, T and Onken, A}, title = {Mixed vine copula flows for flexible modeling of neural dependencies.}, journal = {Frontiers in neuroscience}, volume = {16}, number = {}, pages = {910122}, pmid = {36213754}, issn = {1662-4548}, abstract = {Recordings of complex neural population responses provide a unique opportunity for advancing our understanding of neural information processing at multiple scales and improving performance of brain computer interfaces. However, most existing analytical techniques fall short of capturing the complexity of interactions within the concerted population activity. Vine copula-based approaches have shown to be successful at addressing complex high-order dependencies within the population, disentangled from the single-neuron statistics. However, most applications have focused on parametric copulas which bear the risk of misspecifying dependence structures. In order to avoid this risk, we adopted a fully non-parametric approach for the single-neuron margins and copulas by using Neural Spline Flows (NSF). We validated the NSF framework on simulated data of continuous and discrete types with various forms of dependency structures and with different dimensionality. Overall, NSFs performed similarly to existing non-parametric estimators, while allowing for considerably faster and more flexible sampling which also enables faster Monte Carlo estimation of copula entropy. Moreover, our framework was able to capture low and higher order heavy tail dependencies in neuronal responses recorded in the mouse primary visual cortex during a visual learning task while the animal was navigating a virtual reality environment. These findings highlight an often ignored aspect of complexity in coordinated neuronal activity which can be important for understanding and deciphering collective neural dynamics for neurotechnological applications.}, }
@article {pmid36213753, year = {2022}, author = {Sebastián-Romagosa, M and Udina, E and Ortner, R and Dinarès-Ferran, J and Cho, W and Murovec, N and Matencio-Peralba, C and Sieghartsleitner, S and Allison, BZ and Guger, C}, title = {Corrigendum: EEG biomarkers related with the functional state of stroke patients.}, journal = {Frontiers in neuroscience}, volume = {16}, number = {}, pages = {1032959}, doi = {10.3389/fnins.2022.1032959}, pmid = {36213753}, issn = {1662-4548}, abstract = {[This corrects the article DOI: 10.3389/fnins.2020.00582.].}, }
@article {pmid36213545, year = {2022}, author = {Kwon, J and Hwang, J and Nam, H and Im, CH}, title = {Novel hybrid visual stimuli incorporating periodic motions into conventional flickering or pattern-reversal visual stimuli for steady-state visual evoked potential-based brain-computer interfaces.}, journal = {Frontiers in neuroinformatics}, volume = {16}, number = {}, pages = {997068}, pmid = {36213545}, issn = {1662-5196}, abstract = {In this study, we proposed a new type of hybrid visual stimuli for steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs), which incorporate various periodic motions into conventional flickering stimuli (FS) or pattern reversal stimuli (PRS). Furthermore, we investigated optimal periodic motions for each FS and PRS to enhance the performance of SSVEP-based BCIs. Periodic motions were implemented by changing the size of the stimulus according to four different temporal functions denoted by none, square, triangular, and sine, yielding a total of eight hybrid visual stimuli. Additionally, we developed the extended version of filter bank canonical correlation analysis (FBCCA), which is a state-of-the-art training-free classification algorithm for SSVEP-based BCIs, to enhance the classification accuracy for PRS-based hybrid visual stimuli. Twenty healthy individuals participated in the SSVEP-based BCI experiment to discriminate four visual stimuli with different frequencies. An average classification accuracy and information transfer rate (ITR) were evaluated to compare the performances of SSVEP-based BCIs for different hybrid visual stimuli. Additionally, the user's visual fatigue for each of the hybrid visual stimuli was also evaluated. As the result, for FS, the highest performances were reported when the periodic motion of the sine waveform was incorporated for all window sizes except for 3 s. For PRS, the periodic motion of the square waveform showed the highest classification accuracies for all tested window sizes. A significant statistical difference in the performance between the two best stimuli was not observed. The averaged fatigue scores were reported to be 5.3 ± 2.05 and 4.05 ± 1.28 for FS with sine-wave periodic motion and PRS with square-wave periodic motion, respectively. Consequently, our results demonstrated that FS with sine-wave periodic motion and PRS with square-wave periodic motion could effectively improve the BCI performances compared to conventional FS and PRS. In addition, thanks to its low visual fatigue, PRS with square-wave periodic motion can be regarded as the most appropriate visual stimulus for the long-term use of SSVEP-based BCIs, particularly for window sizes equal to or larger than 2 s.}, }
@article {pmid36213341, year = {2022}, author = {Du, R and Zhu, S and Ni, H and Mao, T and Li, J and Wei, R}, title = {Valence-arousal classification of emotion evoked by Chinese ancient-style music using 1D-CNN-BiLSTM model on EEG signals for college students.}, journal = {Multimedia tools and applications}, volume = {}, number = {}, pages = {1-18}, pmid = {36213341}, issn = {1380-7501}, abstract = {During the COVID-19 pandemic, young people are using multimedia content more frequently to communicate with each other on Internet platforms. Among them, music, as psychological support for a lonely life in this special period, is a powerful tool for emotional self-regulation and getting rid of loneliness. More and more attention has been paid to the music recommender system based on emotion. In recent years, Chinese music has tended to be considered an independent genre. Chinese ancient-style music is one of the new folk music styles in Chinese music and is becoming more and more popular among young people. The complexity of Chinese-style music brings significant challenges to the quantitative calculation of music. To effectively solve the problem of emotion classification in music information search, emotion is often characterized by valence and arousal. This paper focuses on the valence and arousal classification of Chinese ancient-style music-evoked emotion. It proposes a hybrid one-dimensional convolutional neural network and bidirectional and unidirectional long short-term memory model (1D-CNN-BiLSTM). And a self-acquisition EEG dataset for Chinese college students was designed to classify music-induced emotion by valence-arousal based on EEG. In addition to that, the proposed 1D-CNN-BILSTM model verified the performance of public datasets DEAP and DREAMER, as well as the self-acquisition dataset DESC. The experimental results show that, compared with traditional LSTM and 1D-CNN-LSTM models, the proposed method has the highest accuracy in the valence classification task of music-induced emotion, reaching 94.85%, 98.41%, and 99.27%, respectively. The accuracy of the arousal classification task also gained 93.40%, 98.23%, and 99.20%, respectively. In addition, compared with the positive valence classification results of emotion, this method has obvious advantages in negative valence classification. This study provides a computational classification model for a music recommender system with emotion. It also provides some theoretical support for the brain-computer interactive (BCI) application products of Chinese ancient-style music which is popular among young people.}, }
@article {pmid36211589, year = {2022}, author = {Jangwan, NS and Ashraf, GM and Ram, V and Singh, V and Alghamdi, BS and Abuzenadah, AM and Singh, MF}, title = {Brain augmentation and neuroscience technologies: current applications, challenges, ethics and future prospects.}, journal = {Frontiers in systems neuroscience}, volume = {16}, number = {}, pages = {1000495}, pmid = {36211589}, issn = {1662-5137}, abstract = {Ever since the dawn of antiquity, people have strived to improve their cognitive abilities. From the advent of the wheel to the development of artificial intelligence, technology has had a profound leverage on civilization. Cognitive enhancement or augmentation of brain functions has become a trending topic both in academic and public debates in improving physical and mental abilities. The last years have seen a plethora of suggestions for boosting cognitive functions and biochemical, physical, and behavioral strategies are being explored in the field of cognitive enhancement. Despite expansion of behavioral and biochemical approaches, various physical strategies are known to boost mental abilities in diseased and healthy individuals. Clinical applications of neuroscience technologies offer alternatives to pharmaceutical approaches and devices for diseases that have been fatal, so far. Importantly, the distinctive aspect of these technologies, which shapes their existing and anticipated participation in brain augmentations, is used to compare and contrast them. As a preview of the next two decades of progress in brain augmentation, this article presents a plausible estimation of the many neuroscience technologies, their virtues, demerits, and applications. The review also focuses on the ethical implications and challenges linked to modern neuroscientific technology. There are times when it looks as if ethics discussions are more concerned with the hypothetical than with the factual. We conclude by providing recommendations for potential future studies and development areas, taking into account future advancements in neuroscience innovation for brain enhancement, analyzing historical patterns, considering neuroethics and looking at other related forecasts.}, }
@article {pmid36211127, year = {2022}, author = {Kennedy, P and Cervantes, AJ}, title = {Recruitment and Differential Firing Patterns of Single Units During Conditioning to a Tone in a Mute Locked-In Human.}, journal = {Frontiers in human neuroscience}, volume = {16}, number = {}, pages = {864983}, pmid = {36211127}, issn = {1662-5161}, abstract = {Single units that are not related to the desired task can become related to the task by conditioning their firing rates. We theorized that, during conditioning of firing rates to a tone, (a) unrelated single units would be recruited to the task; (b) the recruitment would depend on the phase of the task; (c) tones of different frequencies would produce different patterns of single unit recruitment. In our mute locked-in participant, we conditioned single units using tones of different frequencies emitted from a tone generator. The conditioning task had three phases: Listen to the tone for 20 s, then silently sing the tone for 10 s, with a prior control period of resting for 10 s. Twenty single units were recorded simultaneously while feedback of one of the twenty single units was made audible to the mute locked-in participant. The results indicate that (a) some of the non-audible single units were recruited during conditioning, (b) some were recruited differentially depending on the phase of the paradigm (listen, rest, or silent sing), and (c) single unit firing patterns were specific for different tone frequencies such that the tone could be recognized from the pattern of single unit firings. These data are important when conditioning single unit firings in brain-computer interfacing tasks because they provide evidence that increased numbers of previously unrelated single units can be incorporated into the task. This incorporation expands the bandwidth of the recorded single unit population and thus enhances the brain-computer interface. This is the first report of conditioning of single unit firings in a human participant with a brain to computer implant.}, }
@article {pmid36211121, year = {2022}, author = {Floreani, ED and Orlandi, S and Chau, T}, title = {A pediatric near-infrared spectroscopy brain-computer interface based on the detection of emotional valence.}, journal = {Frontiers in human neuroscience}, volume = {16}, number = {}, pages = {938708}, pmid = {36211121}, issn = {1662-5161}, abstract = {Brain-computer interfaces (BCIs) are being investigated as an access pathway to communication for individuals with physical disabilities, as the technology obviates the need for voluntary motor control. However, to date, minimal research has investigated the use of BCIs for children. Traditional BCI communication paradigms may be suboptimal given that children with physical disabilities may face delays in cognitive development and acquisition of literacy skills. Instead, in this study we explored emotional state as an alternative access pathway to communication. We developed a pediatric BCI to identify positive and negative emotional states from changes in hemodynamic activity of the prefrontal cortex (PFC). To train and test the BCI, 10 neurotypical children aged 8-14 underwent a series of emotion-induction trials over four experimental sessions (one offline, three online) while their brain activity was measured with functional near-infrared spectroscopy (fNIRS). Visual neurofeedback was used to assist participants in regulating their emotional states and modulating their hemodynamic activity in response to the affective stimuli. Child-specific linear discriminant classifiers were trained on cumulatively available data from previous sessions and adaptively updated throughout each session. Average online valence classification exceeded chance across participants by the last two online sessions (with 7 and 8 of the 10 participants performing better than chance, respectively, in Sessions 3 and 4). There was a small significant positive correlation with online BCI performance and age, suggesting older participants were more successful at regulating their emotional state and/or brain activity. Variability was seen across participants in regards to BCI performance, hemodynamic response, and discriminatory features and channels. Retrospective offline analyses yielded accuracies comparable to those reported in adult affective BCI studies using fNIRS. Affective fNIRS-BCIs appear to be feasible for school-aged children, but to further gauge the practical potential of this type of BCI, replication with more training sessions, larger sample sizes, and end-users with disabilities is necessary.}, }
@article {pmid36209529, year = {2022}, author = {Hsieh, JC and Alawieh, H and Li, Y and Iwane, F and Zhao, L and Anderson, R and Abdullah, SI and Kevin Tang, KW and Wang, W and Pyatnitskiy, I and Jia, Y and Millán, JDR and Wang, H}, title = {A highly stable electrode with low electrode-skin impedance for wearable brain-computer interface.}, journal = {Biosensors &amp; bioelectronics}, volume = {218}, number = {}, pages = {114756}, doi = {10.1016/j.bios.2022.114756}, pmid = {36209529}, issn = {1873-4235}, mesh = {Humans ; *Brain-Computer Interfaces ; Silver ; Electric Impedance ; Chlorides ; *Biosensing Techniques ; Electrodes ; *Wearable Electronic Devices ; Hydrogels ; Polymers ; }, abstract = {To date, brain-computer interfaces (BCIs) have proved to play a key role in many medical applications, for example, the rehabilitation of stroke patients. For post-stroke rehabilitation, the BCIs require the EEG electrodes to precisely translate the brain signals of patients into intended movements of the paralyzed limb for months. However, the gold standard silver/silver-chloride electrodes cannot satisfy the requirements for long-term stability and preparation-free recording capability in wearable EEG devices, thus limiting the versatility of EEG in wearable BCI applications over time outside the rehabilitation center. Here, we design a long-term stable and low electrode-skin interfacial impedance conductive polymer-hydrogel EEG electrode that maintains a lower impedance value than gel-based electrodes for 29 days. With this technology, EEG-based long-term and wearable BCIs could be realized in the near future. To demonstrate this, our designed electrode is applied for a wireless single-channel EEG device that detects changes in alpha rhythms in eye-open/eye-close conditions. In addition, we validate that the designed electrodes could capture oscillatory rhythms in motor imagery protocols as well as low-frequency time-locked event-related potentials from healthy subjects, with similar or better performance than gel-based electrodes. Finally, we demonstrate the use of the designed electrode in online BCI-based functional electrical stimulation, which could be used for post-stroke rehabilitation.}, }
@article {pmid36209298, year = {2022}, author = {Lu, CY and Dong, L and Wang, D and Li, S and Fang, BZ and Han, MX and Liu, F and Jiang, HC and Ahmed, I and Li, WJ}, title = {Dongia deserti sp. nov., Isolated from the Gurbantunggut Desert Soil.}, journal = {Current microbiology}, volume = {79}, number = {11}, pages = {342}, pmid = {36209298}, issn = {1432-0991}, mesh = {Agar ; Bacterial Typing Techniques ; DNA, Bacterial/genetics ; Fatty Acids/chemistry ; *Phosphatidylethanolamines ; Phospholipids/chemistry ; Phylogeny ; Quinones ; RNA, Ribosomal, 16S/genetics ; Sequence Analysis, DNA ; Sodium Chloride ; Soil ; *Soil Microbiology ; }, abstract = {A Gram-stain-negative, aerobic, short rod-shaped strain, designated as SYSU D60009[T], was isolated from a dry sandy soil sample collected from the Gurbantunggut Desert in Xinjiang, northwest China. Strain SYSU D60009[T] was observed to grow at 15-42 °C (optimum at 37 °C), pH 4.0-10.0 (optimum at 7.0), and with 0-0.5% (w/v) NaCl (optimum, 0%). The strain grew well on R2A agar, and colonies were smooth, white-pigmented, and circular with low convexity. The polar lipids consisted of phosphatidylethanolamine, aminolipid, aminophospholipid, and unknown lipids. The major cellular fatty acid (> 10%) was C16:0 and the predominant respiratory quinone was Q-10. Whole genome sequencing of strain SYSU D60009[T] revealed 6,132,710 bp with a DNA G + C content of 63.6%. The ANI and dDDH values of strains SYSU D60009[T] to Dongia mobilis CGMCC 1.7660[ T] were 72.8% and 19.0%, respectively. Based on the phenotypic, phylogenetic, and chemotaxonomic properties, strain SYSU D60009[T] represents a novel species of the genus Dongia, for which the name Dongia deserti sp. nov. is proposed, the type strain is SYSU D60009[T] (= CGMCC 1.16441[ T] = KCTC 52790[ T]).}, }
@article {pmid36208730, year = {2022}, author = {Blanco-Díaz, CF and Guerrero-Méndez, CD and Bastos-Filho, T and Jaramillo-Isaza, S and Ruiz-Olaya, AF}, title = {Effects of the concentration level, eye fatigue and coffee consumption on the performance of a BCI system based on visual ERP-P300.}, journal = {Journal of neuroscience methods}, volume = {382}, number = {}, pages = {109722}, doi = {10.1016/j.jneumeth.2022.109722}, pmid = {36208730}, issn = {1872-678X}, mesh = {Humans ; *Brain-Computer Interfaces ; Coffee ; Electroencephalography/methods ; *Asthenopia ; Event-Related Potentials, P300/physiology ; Photic Stimulation ; }, abstract = {BACKGROUND: A widely used paradigm for Brain-Computer Interfaces (BCI) is based on detecting P300 Event-Related Potentials (ERPs) in response to stimulation and concentration tasks. An open challenge corresponds to maximizing the performance of a BCI by considering artifacts arising from the user's cognitive and physical conditions during task execution.<br><br>NEW METHOD: In this study, an analysis of the performance of a visual BCI-P300 system was performed under the metrics of Sensitivity (Sen), Specificity (Spe), Accuracy (Acc), and Area-Under the ROC Curve (AUC), considering the main reported factors affecting the neurophysiological behavior of the P300 signal: Concentration Level, Eye Fatigue, and Coffee Consumption.<br><br>We compared the performance of three P300 signal detection methods (MA-LDA, CCA-RLR, and MA+CCA-RLR) using a public database (GigaScience) in different groups. Data were segmented according to three factors of interest: high and low levels of concentration, high and low eye fatigue, and coffee consumption at different times.<br><br>RESULTS: The results showed a significant improvement between 3% and 6% for the metrics evaluated for identifying the P300 signal in relation to concentration levels and coffee consumption.<br><br>CONCLUSION: P300 signal can be influenced by physical and mental factors during the execution of ERPs evocation tasks, which could be controlled to maximize the interface's capacity to detect the individual's intention.}, }
@article {pmid36206939, year = {2022}, author = {Huang, G and Hu, Z and Chen, W and Zhang, S and Liang, Z and Li, L and Zhang, L and Zhang, Z}, title = {M[3]CV: A multi-subject, multi-session, and multi-task database for EEG-based biometrics challenge.}, journal = {NeuroImage}, volume = {264}, number = {}, pages = {119666}, doi = {10.1016/j.neuroimage.2022.119666}, pmid = {36206939}, issn = {1095-9572}, mesh = {Humans ; *Electroencephalography/methods ; Algorithms ; Machine Learning ; Databases, Factual ; *Brain-Computer Interfaces ; }, abstract = {EEG signals exhibit commonality and variability across subjects, sessions, and tasks. But most existing EEG studies focus on mean group effects (commonality) by averaging signals over trials and subjects. The substantial intra- and inter-subject variability of EEG have often been overlooked. The recently significant technological advances in machine learning, especially deep learning, have brought technological innovations to EEG signal application in many aspects, but there are still great challenges in cross-session, cross-task, and cross-subject EEG decoding. In this work, an EEG-based biometric competition based on a large-scale M[3]CV (A Multi-subject, Multi-session, and Multi-task Database for investigation of EEG Commonality and Variability) database was launched to better characterize and harness the intra- and inter-subject variability and promote the development of machine learning algorithm in this field. In the M[3]CV database, EEG signals were recorded from 106 subjects, of which 95 subjects repeated two sessions of the experiments on different days. The whole experiment consisted of 6 paradigms, including resting-state, transient-state sensory, steady-state sensory, cognitive oddball, motor execution, and steady-state sensory with selective attention with 14 types of EEG signals, 120000 epochs. Two learning tasks (identification and verification), performance metrics, and baseline methods were introduced in the competition. In general, the proposed M[3]CV dataset and the EEG-based biometric competition aim to provide the opportunity to develop advanced machine learning algorithms for achieving an in-depth understanding of the commonality and variability of EEG signals across subjects, sessions, and tasks.}, }
@article {pmid36206725, year = {2022}, author = {Lo, YT and Premchand, B and Libedinsky, C and So, RQY}, title = {Neural correlates of learning in a linear discriminant analysis brain-computer interface paradigm.}, journal = {Journal of neural engineering}, volume = {19}, number = {5}, pages = {}, doi = {10.1088/1741-2552/ac985f}, pmid = {36206725}, issn = {1741-2552}, mesh = {Animals ; *Brain-Computer Interfaces ; Discriminant Analysis ; Movement/physiology ; Learning ; Neurons ; Haplorhini ; Macaca ; Electroencephalography ; }, abstract = {Objective.With practice, the control of brain-computer interfaces (BCI) would improve over time; the neural correlate for such learning had not been well studied. We demonstrated here that monkeys controlling a motor BCI using a linear discriminant analysis (LDA) decoder could learn to make the firing patterns of the recorded neurons more distinct over a short period of time for different output classes to improve task performance.Approach.Using an LDA decoder, we studied two Macaque monkeys implanted with microelectrode arrays as they controlled the movement of a mobile robotic platform. The LDA decoder mapped high-dimensional neuronal firing patterns linearly onto a lower-dimensional linear discriminant (LD) space, and we studied the changes in the spatial coordinates of these neural signals in the LD space over time, and their correspondence to trial performance. Direction selectivity was quantified with permutation feature importance (FI).Main results.We observed that, within individual sessions, there was a tendency for the points in the LD space encoding different directions to diverge, leading to fewer misclassification errors, and, hence, improvement in task accuracy. Accuracy was correlated with the presence of channels with strong directional preference (i.e. high FI), as well as a varied population code (i.e. high variance in FI distribution).Significance.We emphasized the importance of studying the short-term/intra-sessional variations in neural representations during the use of BCI. Over the course of individual sessions, both monkeys could modulate their neural activities to create increasingly distinct neural representations.}, }
@article {pmid36206723, year = {2022}, author = {Xiao, X and Xu, L and Yue, J and Pan, B and Xu, M and Ming, D}, title = {Fixed template network and dynamic template network: novel network designs for decoding steady-state visual evoked potentials.}, journal = {Journal of neural engineering}, volume = {19}, number = {5}, pages = {}, doi = {10.1088/1741-2552/ac9861}, pmid = {36206723}, issn = {1741-2552}, mesh = {*Evoked Potentials, Visual ; Electroencephalography/methods ; Photic Stimulation/methods ; *Brain-Computer Interfaces ; }, abstract = {Objective. Decomposition methods are efficient to decode steady-state visual evoked potentials (SSVEPs). In recent years, the brain-computer interface community has also been developing deep learning networks for decoding SSVEPs. However, there is no clear evidence that current deep learning models outperform decomposition methods on the SSVEP decoding tasks. Many studies lacked the comparison with state-of-the-art decomposition methods in a fair environment.Approach. This study proposed a novel network design motivated by the works of decomposition methods. Fixed template network (FTN) and dynamic template network (DTN) are two novel networks combining the advantages of fixed templates and subject-specific templates. This study also proposed a data augmentation method for SSVEPs. This study compared the intra-subject classification performance of DTN and FTN with that of state-of-the-art decomposition methods on three public SSVEP datasets.Main results. The results show that both FTN and DTN achieved the suboptimal classification performance compared with state-of-the-art decomposition methods.Significance. Both network designs could enhance the decoding performance of SSVEPs, making them promising networks for improving the practicality of SSVEP-based applications.}, }
@article {pmid36205739, year = {2022}, author = {Yoshida, M and Gotoh, M and Yokoyama, O and Kakizaki, H and Yamanishi, T and Yamaguchi, O}, title = {Efficacy of TAC-302 for patients with detrusor underactivity and overactive bladder: a randomized, double-blind, placebo-controlled phase 2 study.}, journal = {World journal of urology}, volume = {40}, number = {11}, pages = {2799-2805}, pmid = {36205739}, issn = {1433-8726}, mesh = {Male ; Female ; Humans ; *Urinary Bladder, Overactive/drug therapy/complications ; *Urinary Bladder, Underactive/complications ; Urodynamics ; Urination ; Double-Blind Method ; Treatment Outcome ; }, abstract = {PURPOSE: This multicenter, randomized, double-blind, placebo-controlled phase 2 study evaluated the efficacy and safety of TAC-302, a novel drug that restores neurite outgrowth, in patients with detrusor underactivity (DU) and overactive bladder (OAB).<br><br>METHODS: After 2-4&#xa0;weeks of observation, patients were randomized 2:1 to receive oral TAC-302 200&#xa0;mg or placebo twice daily for 12&#xa0;weeks. The primary endpoint was detrusor contraction strength, estimated by bladder contractility index (BCI) for males and projected isovolumetric pressure 1 (PIP1) for females. Secondary endpoints included changes in bladder voiding efficiency (BVE) and safety.<br><br>RESULTS: Seventy-six patients were included (TAC-302, n&#x2009;=&#x2009;52; placebo, n&#x2009;=&#x2009;24). The mean (standard deviation [SD]) BCI for males was 64.6 (16.6) at baseline and 75.2 (21.1) at week 12 (p&#x2009;<&#x2009;0.001) with TAC-302 (n&#x2009;=&#x2009;27), and 61.3 (16.6) and 60.5 (16.7) (p&#x2009;=&#x2009;0.82) with placebo (n&#x2009;=&#x2009;11). The respective mean (SD) PIP1 for females was 18.8 (6.6) and 29.4 (9.4) (p&#x2009;<&#x2009;0.001) with TAC-302 (n&#x2009;=&#x2009;15), and 20.6 (7.5) and 25.5 (9.6) (p&#x2009;=&#x2009;0.14) with placebo (n&#x2009;=&#x2009;7). TAC-302 significantly increased BCI in males and BVE in both sexes. TAC-302 efficacy on OAB was not clearly shown. The incidences of adverse events (AEs), serious AEs, and AEs leading to dose interruption were similar between groups; no adverse drug reactions occurred.<br><br>CONCLUSION: Considering the significant effects on BCI in males and BVE in both sexes, TAC-302 may benefit patients with DU.<br><br>REGISTRATION: ClinicalTrials.gov Identifier NCT03175029 registered 6/5/2017.}, }
@article {pmid36204719, year = {2022}, author = {Huggins, JE and Karlsson, P and Warschausky, SA}, title = {Challenges of brain-computer interface facilitated cognitive assessment for children with cerebral palsy.}, journal = {Frontiers in human neuroscience}, volume = {16}, number = {}, pages = {977042}, pmid = {36204719}, issn = {1662-5161}, abstract = {Brain-computer interfaces (BCIs) have been successfully used by adults, but little information is available on BCI use by children, especially children with severe multiple impairments who may need technology to facilitate communication. Here we discuss the challenges of using non-invasive BCI with children, especially children who do not have another established method of communication with unfamiliar partners. Strategies to manage these challenges require consideration of multiple factors related to accessibility, cognition, and participation. These factors include decisions regarding where (home, clinic, or lab) participation will take place, the number of sessions involved, and the degree of participation necessary for success. A strategic approach to addressing the unique challenges inherent in BCI use by children with disabilities will increase the potential for successful BCI calibration and adoption of BCI as a valuable access method for children with the most significant impairments in movement and communication.}, }
@article {pmid36204424, year = {2022}, author = {Knierim, MT and Schemmer, M and Bauer, N}, title = {A simplified design of a cEEGrid ear-electrode adapter for the OpenBCI biosensing platform.}, journal = {HardwareX}, volume = {12}, number = {}, pages = {e00357}, pmid = {36204424}, issn = {2468-0672}, abstract = {We present a simplified design of an ear-centered sensing system built around the OpenBCI Cyton &amp; Daisy biosignal amplifiers and the flex-printed cEEGrid ear-EEG electrodes. This design reduces the number of components that need to be sourced, reduces mechanical artefacts on the recording data through better cable placement, and simplifies the assembly. Besides describing how to replicate and use the system, we highlight promising application scenarios, particularly the observation of large-amplitude activity patterns (e.g., facial muscle activities) and frequency-band neural activity (e.g., alpha and beta band power modulations for mental workload detection). Further, examples for common measurement artefacts and methods for removing them are provided, introducing a prototypical application of adaptive filters to this system. Lastly, as a promising use case, we present findings from a single-user study that highlights the system's capability of detecting jaw clenching events robustly when contrasted with 26 other facial activities. Thereby, the system could, for instance, be used to devise applications that reduce pathological jaw clenching and teeth grinding (bruxism). These findings underline that the system represents a valuable prototyping platform for advancing ear-based electrophysiological sensing systems and a low-cost alternative to current commercial alternatives.}, }
@article {pmid36198278, year = {2022}, author = {Awasthi, P and Lin, TH and Bae, J and Miller, LE and Danziger, ZC}, title = {Validation of a non-invasive, real-time, human-in-the-loop model of intracortical brain-computer interfaces.}, journal = {Journal of neural engineering}, volume = {19}, number = {5}, pages = {}, doi = {10.1088/1741-2552/ac97c3}, pmid = {36198278}, issn = {1741-2552}, support = {R01 NS109257/NS/NINDS NIH HHS/United States ; }, mesh = {Algorithms ; *Brain-Computer Interfaces ; Electrodes, Implanted ; Humans ; *Motor Cortex/physiology ; Movement ; }, abstract = {Objective. Despite the tremendous promise of invasive brain-computer interfaces (iBCIs), the associated study costs, risks, and ethical considerations limit the opportunity to develop and test the algorithms that decode neural activity into a user's intentions. Our goal was to address this challenge by designing an iBCI model capable of testing many human subjects in closed-loop.Approach. We developed an iBCI model that uses artificial neural networks (ANNs) to translate human finger movements into realistic motor cortex firing patterns, which can then be decoded in real time. We call the model the joint angle BCI, or jaBCI. jaBCI allows readily recruited, healthy subjects to perform closed-loop iBCI tasks using any neural decoder, preserving subjects' control-relevant short-latency error correction and learning dynamics.Main results. We validated jaBCI offline through emulated neuron firing statistics, confirming that emulated neural signals have firing rates, low-dimensional PCA geometry, and rotational jPCA dynamics that are quite similar to the actual neurons (recorded in monkey M1) on which we trained the ANN. We also tested jaBCI in closed-loop experiments, our single study examining roughly as many subjects as have been tested world-wide with iBCIs (n= 25). Performance was consistent with that of the paralyzed, human iBCI users with implanted intracortical electrodes. jaBCI allowed us to imitate the experimental protocols (e.g. the same velocity Kalman filter decoder and center-out task) and compute the same seven behavioral measures used in three critical studies.Significance. These encouraging results suggest the jaBCI's real-time firing rate emulation is a useful means to provide statistically robust sample sizes for rapid prototyping and optimization of decoding algorithms, the study of bi-directional learning in iBCIs, and improving iBCI control.}, }
@article {pmid36197872, year = {2022}, author = {Sun, Y and Liang, L and Sun, J and Chen, X and Tian, R and Chen, Y and Zhang, L and Gao, X}, title = {A Binocular Vision SSVEP Brain-Computer Interface Paradigm for Dual-Frequency Modulation.}, journal = {IEEE transactions on bio-medical engineering}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBME.2022.3212192}, pmid = {36197872}, issn = {1558-2531}, abstract = {OBJECTIVE: This study presents a novel brain-computer interface paradigm of dual-frequency modulated steady-state visual evoked potential (SSVEP), aiming to suppress the unpredictable intermodulation components in current applications. This paradigm is especially suitable for training-free scenarios.<br><br>APPROACH: This study built a dual-frequency binocular vision SSVEP brain-computer interface system using circularly polarized light technology. Two experiments, including a 6-target offline experiment and a 40-target online experiment, were taken with this system. Meanwhile, an improved algorithm filter bank dual-frequency canonical correlation analysis (FBDCCA) was presented for the dual-frequency SSVEP paradigm.<br><br>MAIN RESULTS: Energy analysis was conducted for 9 subjects in the 6-target dual-frequency offline experiment, among which the signal-to-noise ratio of target frequency components have increased by 2&#xa0;dB compared to the one of unpredictable intermodulation components. Subsequently, the online experiment with 40 targets was conducted with 12 subjects. With this new dual-frequency paradigm, the online training-free experiment's average information transmission rate (ITR) reached 104.56 &#xb1; 15.74 bits/min, which was almost twice as fast as the current best dual-frequency paradigm. And the average information transfer rate for offline training analysis of this new paradigm was 180.87 &#xb1; 17.88 bits/min.<br><br>SIGNIFICANCE: These results demonstrate that this new dual-frequency SSVEP brain-computer interface paradigm can suppress the unpredictable intermodulation harmonics and generate higher quality responses while completing dual-frequency encoding. Moreover, its performance shows high ITR in applications both with and without training. It is thus believed that this paradigm is competent for achieving large target numbers in brain-computer interface systems and has more possible practices.}, }
@article {pmid36197639, year = {2022}, author = {Wu, C and Liu, Y and Guo, X and Zhu, T and Bao, Z}, title = {Enhancing the feasibility of cognitive load recognition in remote learning using physiological measures and an adaptive feature recalibration convolutional neural network.}, journal = {Medical &amp; biological engineering &amp; computing}, volume = {60}, number = {12}, pages = {3447-3460}, pmid = {36197639}, issn = {1741-0444}, mesh = {Humans ; *Electroencephalography/methods ; Feasibility Studies ; *COVID-19 ; Neural Networks, Computer ; Cognition ; }, abstract = {The precise assessment of cognitive load during a learning phase is an important pathway to improving students' learning efficiency and performance. Physiological measures make it possible to continuously monitor learners' cognitive load in remote learning during the COVID-19 outbreak. However, maintaining a good balance between performance and computational cost is still a major challenge in advancing cognitive load recognition technology to real-world applications. This paper introduced an adaptive feature recalibration (AFR) convolutional neural network to overcome this challenge by capturing the most discriminative physiological features (EEG and eye-tracking). The results revealed that the optimal average classification accuracy of the feature combination obtained by the AFR method reached 95.56% with only 60 feature dimensions. Additionally, compared with the best result of the conventional correlation-based feature selection (CFS) method, the introduced AFR algorithm achieved higher accuracy and cheaper computational cost, as well as a 2.06% improvement in accuracy and a 51.21% reduction in feature dimension, which is more in line with the requirements of low delay and real-time performance in practical BCI applications.}, }
@article {pmid36196121, year = {2022}, author = {Nicolelis, MAL}, title = {Brain-machine-brain interfaces as the foundation for the next generation of neuroprostheses.}, journal = {National science review}, volume = {9}, number = {10}, pages = {nwab206}, pmid = {36196121}, issn = {2053-714X}, }
@article {pmid36196114, year = {2022}, author = {Chen, Y and Zhang, G and Guan, L and Gong, C and Ma, B and Hao, H and Li, L}, title = {Progress in the development of a fully implantable brain-computer interface: the potential of sensing-enabled neurostimulators.}, journal = {National science review}, volume = {9}, number = {10}, pages = {nwac099}, pmid = {36196114}, issn = {2053-714X}, abstract = {This perspective article investigates the performance of using a sensing-enabled neurostimulator as a motor brain-computer interface.}, }
@article {pmid36195625, year = {2022}, author = {Lotun, S and Lamarche, VM and Samothrakis, S and Sandstrom, GM and Matran-Fernandez, A}, title = {Parasocial relationships on YouTube reduce prejudice towards mental health issues.}, journal = {Scientific reports}, volume = {12}, number = {1}, pages = {16565}, pmid = {36195625}, issn = {2045-2322}, mesh = {*Attitude ; Humans ; Interpersonal Relations ; Mental Health ; Prejudice ; *Social Media ; }, abstract = {Intergroup contact has long been established as a way to reduce prejudice among society, but in-person interventions can be resource intensive and limited in reach. Parasocial relationships (PSRs) might navigate these problems by reaching large audiences with minimal resources and have been shown to help reduce prejudice in an extended version of contact theory. However, previous studies have shown inconsistent success. We assessed whether parasocial interventions reduce prejudice towards people with mental health issues by first creating a new PSR with a YouTube creator disclosing their experiences with borderline personality disorder. Our intervention successfully reduced explicit prejudice and intergroup anxiety. We corroborated these effects through causal analyses, where lower prejudice levels were mediated by the strength of parasocial bond. Preliminary findings suggest that this lower prejudice is sustained over time. Our results support the parasocial contact hypothesis and provide an organic method to passively reduce prejudice on a large scale.}, }
@article {pmid36194720, year = {2022}, author = {Chen, P and Wang, H and Sun, X and Li, H and Grebogi, C and Gao, Z}, title = {Transfer Learning With Optimal Transportation and Frequency Mixup for EEG-Based Motor Imagery Recognition.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {30}, number = {}, pages = {2866-2875}, doi = {10.1109/TNSRE.2022.3211881}, pmid = {36194720}, issn = {1558-0210}, mesh = {Humans ; *Algorithms ; Electroencephalography/methods ; *Brain-Computer Interfaces ; Machine Learning ; Recognition, Psychology ; Imagination ; }, abstract = {Electroencephalography-based Brain Computer Interfaces (BCIs) invariably have a degenerate performance due to the considerable individual variability. To address this problem, we develop a novel domain adaptation method with optimal transport and frequency mixup for cross-subject transfer learning in motor imagery BCIs. Specifically, the preprocessed EEG signals from source and target domain are mapped into latent space with an embedding module, where the representation distributions and label distributions across domains have a large discrepancy. We assume that there exists a non-linear coupling matrix between both domains, which can be utilized to estimate the distance of joint distributions for different domains. Depending on the optimal transport, the Wasserstein distance between source and target domains is minimized, yielding the alignment of joint distributions. Moreover, a new mixup strategy is also introduced to generalize the model, where the inputs trials are mixed in frequency domain rather than in raw space. The extensive experiments on three evaluation benchmarks are conducted to validate the proposed framework. All the results demonstrate that our method achieves a superior performance than previous state-of-the-art domain adaptation approaches.}, }
@article {pmid36194480, year = {2022}, author = {Chen, L and Hu, Y and Wang, S and Cao, K and Mai, W and Sha, W and Ma, H and Zeng, LH and Xu, ZZ and Gao, YJ and Duan, S and Wang, Y and Gao, Z}, title = {mTOR-neuropeptide Y signaling sensitizes nociceptors to drive neuropathic pain.}, journal = {JCI insight}, volume = {7}, number = {22}, pages = {}, pmid = {36194480}, issn = {2379-3708}, mesh = {Rats ; Mice ; Animals ; *Neuropeptide Y/metabolism ; Rats, Sprague-Dawley ; *Neuralgia/drug therapy/metabolism ; Nociceptors/metabolism ; Ganglia, Spinal/metabolism ; Receptors, G-Protein-Coupled/metabolism ; TOR Serine-Threonine Kinases/metabolism ; }, abstract = {Neuropathic pain is a refractory condition that involves de novo protein synthesis in the nociceptive pathway. The mTOR is a master regulator of protein translation; however, mechanisms underlying its role in neuropathic pain remain elusive. Using the spared nerve injury-induced neuropathic pain model, we found that mTOR was preferentially activated in large-diameter dorsal root ganglion (DRG) neurons and spinal microglia. However, selective ablation of mTOR in DRG neurons, rather than microglia, alleviated acute neuropathic pain in mice. We show that injury-induced mTOR activation promoted the transcriptional induction of neuropeptide Y (Npy), likely via signal transducer and activator of transcription 3 phosphorylation. NPY further acted primarily on Y2 receptors (Y2R) to enhance neuronal excitability. Peripheral replenishment of NPY reversed pain alleviation upon mTOR removal, whereas Y2R antagonists prevented pain restoration. Our findings reveal an unexpected link between mTOR and NPY/Y2R in promoting nociceptor sensitization and neuropathic pain.}, }
@article {pmid36191111, year = {2022}, author = {Wang, J and Bi, L and Fei, W and Tian, K}, title = {EEG-Based Continuous Hand Movement Decoding Using Improved Center-Out Paradigm.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {30}, number = {}, pages = {2845-2855}, doi = {10.1109/TNSRE.2022.3211276}, pmid = {36191111}, issn = {1558-0210}, mesh = {Humans ; *Electroencephalography ; *Brain-Computer Interfaces ; Hand ; Movement ; Upper Extremity ; }, abstract = {The continuous decoding of human movement intention based on electroencephalogram (EEG) signals is valuable for developing a more natural motor augmented or assistive system instead of its discrete classifications. The classic center-out paradigm has been widely used to study discrete and continuous hand movement parameter decoding. However, when applying it in studying continuous movement decoding, the classic paradigm needs to be improved to increase the decoding performance, especially generalization performance. In this paper, we first discuss the limitations of the classic center-out paradigm in exploring the hand movement's continuous decoding. Then, an improved paradigm is proposed to enhance the continuous decoding performance. Besides, an adaptive decoder-ensemble framework is developed for continuous kinematic parameter decoding. Finally, with the improved center-out paradigm and the ensemble decoding framework, the average Pearson's correlation coefficients between the predicted and recorded movement kinematic parameters improve significantly by about 75 percent for the directional parameters and about 10 percent for the non-directional parameters. Furthermore, its generalization performance improves significantly by about 20 percent for the directional parameters. This study indicates the advantage of the improved paradigm in predicting the hand movement's kinematic information from low-frequency scalp EEG signals. It can advance the applications of the noninvasive motor brain-computer interface (BCI) in rehabilitation, daily assistance, and human augmentation areas.}, }
@article {pmid36188944, year = {2022}, author = {McGeady, C and Vučković, A and Singh Tharu, N and Zheng, YP and Alam, M}, title = {Brain-Computer Interface Priming for Cervical Transcutaneous Spinal Cord Stimulation Therapy: An Exploratory Case Study.}, journal = {Frontiers in rehabilitation sciences}, volume = {3}, number = {}, pages = {896766}, pmid = {36188944}, issn = {2673-6861}, abstract = {Loss of arm and hand function is one of the most devastating consequences of cervical spinal cord injury (SCI). Although some residual functional neurons often pass the site of injury, recovery after SCI is extremely limited. Recent efforts have aimed to augment traditional rehabilitation by combining exercise-based training with techniques such as transcutaneous spinal cord stimulation (tSCS), and movement priming. Such methods have been linked with elevated corticospinal excitability, and enhanced neuroplastic effects following activity-based therapy. In the present study, we investigated the potential for facilitating tSCS-based exercise-training with brain-computer interface (BCI) motor priming. An individual with chronic AIS A cervical SCI with both sensory and motor complete tetraplegia participated in a two-phase cross-over intervention whereby they engaged in 15 sessions of intensive tSCS-mediated hand training for 1 h, 3 times/week, followed by a two week washout period, and a further 15 sessions of tSCS training with bimanual BCI motor priming preceding each session. We found using the Graded Redefined Assessment for Strength, Sensibility, and Prehension that the participant's arm and hand function improved considerably across each phase of the study: from 96/232 points at baseline, to 117/232 after tSCS training alone, and to 131/232 points after BCI priming with tSCS training, reflecting improved strength, sensation, and gross and fine motor skills. Improved motor scores and heightened perception to sharp sensations improved the neurological level of injury from C4 to C5 following training and improvements were generally maintained four weeks after the final training session. Although functional improvements were similar regardless of the presence of BCI priming, there was a moderate improvement of bilateral strength only when priming preceded tSCS training, perhaps suggesting a benefit of motor priming for tSCS training.}, }
@article {pmid36188803, year = {2021}, author = {Hekmatmanesh, A and Wu, H and Handroos, H}, title = {Largest Lyapunov Exponent Optimization for Control of a Bionic-Hand: A Brain Computer Interface Study.}, journal = {Frontiers in rehabilitation sciences}, volume = {2}, number = {}, pages = {802070}, pmid = {36188803}, issn = {2673-6861}, abstract = {This paper introduces a brain control bionic-hand, and several methods have been developed for predicting and quantifying the behavior of a non-linear system such as a brain. Non-invasive investigations on the brain were conducted by means of electroencephalograph (EEG) signal oscillations. One of the prominent concepts necessary to understand EEG signals is the chaotic concept named the fractal dimension and the largest Lyapunov exponent (LLE). Specifically, the LLE algorithm called the chaotic quantifier method has been employed to compute the complexity of a system. The LLE helps us to understand how the complexity of the brain changes while making a decision to close and open a fist. The LLE has been used for a long time, but here we optimize the traditional LLE algorithm to attain higher accuracy and precision for controlling a bionic hand. In the current study, the main constant input parameters of the LLE, named the false nearest neighbor and mutual information, are parameterized and then optimized by means of the Water Drop (WD) and Chaotic Tug of War (CTW) optimizers. The optimized LLE is then employed to identify imaginary movement patterns from the EEG signals for control of a bionic hand. The experiment includes 21 subjects for recording imaginary patterns. The results illustrated that the CTW solution achieved a higher average accuracy rate of 72.31% in comparison to the traditional LLE and optimized LLE by using a WD optimizer. The study concluded that the traditional LLE required enhancement using optimization methods. In addition, the CTW approximation method has the potential for more efficient solutions in comparison to the WD method.}, }
@article {pmid36188181, year = {2022}, author = {Liang, S and Su, L and Fu, Y and Wu, L}, title = {Multi-source joint domain adaptation for cross-subject and cross-session emotion recognition from electroencephalography.}, journal = {Frontiers in human neuroscience}, volume = {16}, number = {}, pages = {921346}, pmid = {36188181}, issn = {1662-5161}, abstract = {As an important component to promote the development of affective brain-computer interfaces, the study of emotion recognition based on electroencephalography (EEG) has encountered a difficult challenge; the distribution of EEG data changes among different subjects and at different time periods. Domain adaptation methods can effectively alleviate the generalization problem of EEG emotion recognition models. However, most of them treat multiple source domains, with significantly different distributions, as one single source domain, and only adapt the cross-domain marginal distribution while ignoring the joint distribution difference between the domains. To gain the advantages of multiple source distributions, and better match the distributions of the source and target domains, this paper proposes a novel multi-source joint domain adaptation (MSJDA) network. We first map all domains to a shared feature space and then align the joint distributions of the further extracted private representations and the corresponding classification predictions for each pair of source and target domains. Extensive cross-subject and cross-session experiments on the benchmark dataset, SEED, demonstrate the effectiveness of the proposed model, where more significant classification results are obtained on the more difficult cross-subject emotion recognition task.}, }
@article {pmid36188180, year = {2022}, author = {Kostoglou, K and Müller-Putz, GR}, title = {Using linear parameter varying autoregressive models to measure cross frequency couplings in EEG signals.}, journal = {Frontiers in human neuroscience}, volume = {16}, number = {}, pages = {915815}, pmid = {36188180}, issn = {1662-5161}, abstract = {For years now, phase-amplitude cross frequency coupling (CFC) has been observed across multiple brain regions under different physiological and pathological conditions. It has been suggested that CFC serves as a mechanism that facilitates communication and information transfer between local and spatially separated neuronal populations. In non-invasive brain computer interfaces (BCI), CFC has not been thoroughly explored. In this work, we propose a CFC estimation method based on Linear Parameter Varying Autoregressive (LPV-AR) models and we assess its performance using both synthetic data and electroencephalographic (EEG) data recorded during attempted arm/hand movements of spinal cord injured (SCI) participants. Our results corroborate the potentiality of CFC as a feature for movement attempt decoding and provide evidence of the superiority of our proposed CFC estimation approach compared to other commonly used techniques.}, }
@article {pmid36188178, year = {2022}, author = {Annaheim, C and Hug, K and Stumm, C and Messerli, M and Simon, Y and Hund-Georgiadis, M}, title = {Neurofeedback in patients with frontal brain lesions: A randomized, controlled double-blind trial.}, journal = {Frontiers in human neuroscience}, volume = {16}, number = {}, pages = {979723}, pmid = {36188178}, issn = {1662-5161}, abstract = {BACKGROUND: Frontal brain dysfunction is a major challenge in neurorehabilitation. Neurofeedback (NF), as an EEG-based brain training method, is currently applied in a wide spectrum of mental health conditions, including traumatic brain injury.<br><br>OBJECTIVE: This study aimed to explore the capacity of Infra-Low Frequency Neurofeedback (ILF-NF) to promote the recovery of brain function in patients with frontal brain injury.<br><br>MATERIALS AND METHODS: Twenty patients hospitalized at a neurorehabilitation clinic in Switzerland with recently acquired, frontal and optionally other brain lesions were randomized to either receive NF or sham-NF. Cognitive improvement was assessed using the Frontal Assessment Battery (FAB) and the Test of Attentional Performance (TAP) tasks regarding intrinsic alertness, phasic alertness and impulse control.<br><br>RESULTS: With respect to cognitive improvements, there was no significant difference between the two groups after 20 sessions of either NF or sham-NF. However, in a subgroup of patients with predominantly frontal brain lesions, the improvements measured by the FAB and intrinsic alertness were significantly higher in the NF-group.<br><br>CONCLUSION: This is the first double-blind controlled study using NF in recovery from brain injury, and thus also the first such study of ILF NF. Although the result of the subgroup has limited significance because of the small number of participants, it accentuates the trend seen in the whole group regarding the FAB and intrinsic alertness (p = 0.068, p = 0.079, respectively). We therefore conclude that NF could be a promising candidate promoting the recoveryfrom frontal brain lesions. Further studies with larger numbers of patients and less lesion heterogeneity are needed to verify the usefulness of NF in the neurorehabilitation of patients with frontal brain injury (NCT02957695 ClinicalTrials.gov).}, }
@article {pmid36186339, year = {2022}, author = {Gao, Y and Kassymova, RT and Luo, Y}, title = {Application of virtual simulation situational model in Russian spatial preposition teaching.}, journal = {Frontiers in psychology}, volume = {13}, number = {}, pages = {985887}, pmid = {36186339}, issn = {1664-1078}, abstract = {The purpose is to improve the teaching quality of Russian spatial prepositions in colleges. This work takes teaching Russian spatial prepositions as an example to study the key technologies in 3D Virtual Simulation (VS) teaching. 3D VS situational teaching is a high-end visual teaching technology. VS situation construction focuses on Human-Computer Interaction (HCI) to explore and present a realistic language teaching scene. Here, the Steady State Visual Evoked Potential (SSVEP) is used to control Brain-Computer Interface (BCI). An SSVEP-BCI system is constructed through the Hybrid Frequency-Phase Modulation (HFPM). The acquisition system can obtain the current SSVEP from the user's brain to know which module the user is watching to complete instructions encoded by the module. Experiments show that the recognition accuracy of the proposed SSVEP-BCI system based on HFPM increases with data length. When the data length is 0.6-s, the Information Transfer Rate (ITR) reaches the highest: 242.21 ± 46.88 bits/min. Therefore, a high-speed BCI character input system based on SSVEP is designed using HFPM. The main contribution of this work is to build a SSVEP-BCI system based on joint frequency phase modulation. It is better than the currently-known brain computer interface character input system, and is of great value to optimize the performance of the virtual simulation situation system for Russian spatial preposition teaching.}, }
@article {pmid36186085, year = {2022}, author = {Lim, J and Lee, J and Moon, E and Barrow, M and Atzeni, G and Letner, JG and Costello, JT and Nason, SR and Patel, PR and Sun, Y and Patil, PG and Kim, HS and Chestek, CA and Phillips, J and Blaauw, D and Sylvester, D and Jang, T}, title = {A Light-Tolerant Wireless Neural Recording IC for Motor Prediction With Near-Infrared-Based Power and Data Telemetry.}, journal = {IEEE journal of solid-state circuits}, volume = {57}, number = {4}, pages = {1061-1074}, pmid = {36186085}, issn = {0018-9200}, support = {R21 EY029452/EY/NEI NIH HHS/United States ; }, abstract = {Miniaturized and wireless near-infrared (NIR) based neural recorders with optical powering and data telemetry have been introduced as a promising approach for safe long-term monitoring with the smallest physical dimension among state-of-the-art standalone recorders. However, a main challenge for the NIR based neural recording ICs is to maintain robust operation in the presence of light-induced parasitic short circuit current from junction diodes. This is especially true when the signal currents are kept small to reduce power consumption. In this work, we present a light-tolerant and low-power neural recording IC for motor prediction that can fully function in up to 300 μW/mm[2] of light exposure. It achieves best-in-class power consumption of 0.57 μW at 38° C with a 4.1 NEF pseudo-resistorless amplifier, an on-chip neural feature extractor, and individual mote level gain control. Applying the 20-channel pre-recorded neural signals of a monkey, the IC predicts finger position and velocity with correlation coefficient up to 0.870 and 0.569, respectively, with individual mote level gain control enabled. In addition, wireless measurement is demonstrated through optical power and data telemetry using a custom PV/LED GaAs chip wire bonded to the proposed IC.}, }
@article {pmid36182360, year = {2022}, author = {Cai, Q and An, JP and Li, HY and Guo, JY and Gao, ZK}, title = {Cross-subject emotion recognition using visibility graph and genetic algorithm-based convolution neural network.}, journal = {Chaos (Woodbury, N.Y.)}, volume = {32}, number = {9}, pages = {093110}, doi = {10.1063/5.0098454}, pmid = {36182360}, issn = {1089-7682}, mesh = {*Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Emotions/physiology ; Humans ; Neural Networks, Computer ; }, abstract = {An efficient emotion recognition model is an important research branch in electroencephalogram (EEG)-based brain-computer interfaces. However, the input of the emotion recognition model is often a whole set of EEG channels obtained by electrodes placed on subjects. The unnecessary information produced by redundant channels affects the recognition rate and depletes computing resources, thereby hindering the practical applications of emotion recognition. In this work, we aim to optimize the input of EEG channels using a visibility graph (VG) and genetic algorithm-based convolutional neural network (GA-CNN). First, we design an experiment to evoke three types of emotion states using movies and collect the multi-channel EEG signals of each subject under different emotion states. Then, we construct VGs for each EEG channel and derive nonlinear features representing each EEG channel. We employ the genetic algorithm (GA) to find the optimal subset of EEG channels for emotion recognition and use the recognition results of the CNN as fitness values. The experimental results show that the recognition performance of the proposed method using a subset of EEG channels is superior to that of the CNN using all channels for each subject. Last, based on the subset of EEG channels searched by the GA-CNN, we perform cross-subject emotion recognition tasks employing leave-one-subject-out cross-validation. These results demonstrate the effectiveness of the proposed method in recognizing emotion states using fewer EEG channels and further enrich the methods of EEG classification using nonlinear features.}, }
@article {pmid36179659, year = {2022}, author = {Valeriani, D and O'Flynn, LC and Worthley, A and Sichani, AH and Simonyan, K}, title = {Multimodal collaborative brain-computer interfaces aid human-machine team decision-making in a pandemic scenario.}, journal = {Journal of neural engineering}, volume = {19}, number = {5}, pages = {}, doi = {10.1088/1741-2552/ac96a5}, pmid = {36179659}, issn = {1741-2552}, mesh = {*Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Magnetic Resonance Imaging ; Pandemics ; Parietal Lobe ; }, abstract = {Objective.Critical decisions are made by effective teams that are characterized by individuals who trust each other and know how to best integrate their opinions. Here, we introduce a multimodal brain-computer interface (BCI) to help collaborative teams of humans and an artificial agent achieve more accurate decisions in assessing danger zones during a pandemic scenario.Approach.Using high-resolution simultaneous electroencephalography/functional MRI (EEG/fMRI), we first disentangled the neural markers of decision-making confidence and trust and then employed machine-learning to decode these neural signatures for BCI-augmented team decision-making. We assessed the benefits of BCI on the team's decision-making process compared to the performance of teams of different sizes using the standard majority or weighing individual decisions.Main results.We showed that BCI-assisted teams are significantly more accurate in their decisions than traditional teams, as the BCI is capable of capturing distinct neural correlates of confidence on a trial-by-trial basis. Accuracy and subjective confidence in the context of collaborative BCI engaged parallel, spatially distributed, and temporally distinct neural circuits, with the former being focused on incorporating perceptual information processing and the latter involving action planning and executive operations during decision making. Among these, the superior parietal lobule emerged as a pivotal region that flexibly modulated its activity and engaged premotor, prefrontal, visual, and subcortical areas for shared spatial-temporal control of confidence and trust during decision-making.Significance.Multimodal, collaborative BCIs that assist human-artificial agent teams may be utilized in critical settings for augmented and optimized decision-making strategies.}, }
@article {pmid36177890, year = {2022}, author = {Chen, X and Dong, D and Zhou, F and Gao, X and Liu, Y and Wang, J and Qin, J and Tian, Y and Xiao, M and Xu, X and Li, W and Qiu, J and Feng, T and He, Q and Lei, X and Chen, H}, title = {Connectome-based prediction of eating disorder-associated symptomatology.}, journal = {Psychological medicine}, volume = {}, number = {}, pages = {1-14}, doi = {10.1017/S0033291722003026}, pmid = {36177890}, issn = {1469-8978}, abstract = {BACKGROUND: Despite increasing knowledge on the neuroimaging patterns of eating disorder (ED) symptoms in non-clinical populations, studies using whole-brain machine learning to identify connectome-based neuromarkers of ED symptomatology are absent. This study examined the association of connectivity within and between large-scale functional networks with specific symptomatic behaviors and cognitions using connectome-based predictive modeling (CPM).<br><br>METHODS: CPM with ten-fold cross-validation was carried out to probe functional networks that were predictive of ED-associated symptomatology, including body image concerns, binge eating, and compensatory behaviors, within the discovery sample of 660 participants. The predictive ability of the identified networks was validated using an independent sample of 821 participants.<br><br>RESULTS: The connectivity predictive of body image concerns was identified within and between networks implicated in cognitive control (frontoparietal and medial frontal), reward sensitivity (subcortical), and visual perception (visual). Crucially, the set of connections in the positive network related to body image concerns identified in one sample was generalized to predict body image concerns in an independent sample, suggesting the replicability of this effect.<br><br>CONCLUSIONS: These findings point to the feasibility of using the functional connectome to predict ED symptomatology in the general population and provide the first evidence that functional interplay among distributed networks predicts body shape/weight concerns.}, }
@article {pmid36177358, year = {2022}, author = {Li, R and Liu, D and Li, Z and Liu, J and Zhou, J and Liu, W and Liu, B and Fu, W and Alhassan, AB}, title = {A novel EEG decoding method for a facial-expression-based BCI system using the combined convolutional neural network and genetic algorithm.}, journal = {Frontiers in neuroscience}, volume = {16}, number = {}, pages = {988535}, pmid = {36177358}, issn = {1662-4548}, abstract = {Multiple types of brain-control systems have been applied in the field of rehabilitation. As an alternative scheme for balancing user fatigue and the classification accuracy of brain-computer interface (BCI) systems, facial-expression-based brain control technologies have been proposed in the form of novel BCI systems. Unfortunately, existing machine learning algorithms fail to identify the most relevant features of electroencephalogram signals, which further limits the performance of the classifiers. To address this problem, an improved classification method is proposed for facial-expression-based BCI (FE-BCI) systems, using a convolutional neural network (CNN) combined with a genetic algorithm (GA). The CNN was applied to extract features and classify them. The GA was used for hyperparameter selection to extract the most relevant parameters for classification. To validate the superiority of the proposed algorithm used in this study, various experimental performance results were systematically evaluated, and a trained CNN-GA model was constructed to control an intelligent car in real time. The average accuracy across all subjects was 89.21 ± 3.79%, and the highest accuracy was 97.71 ± 2.07%. The superior performance of the proposed algorithm was demonstrated through offline and online experiments. The experimental results demonstrate that our improved FE-BCI system outperforms the traditional methods.}, }
@article {pmid36176162, year = {2022}, author = {Patwardhan, S and Schofield, J and Joiner, WM and Sikdar, S}, title = {Sonomyography shows feasibility as a tool to quantify joint movement at the muscle level.}, journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]}, volume = {2022}, number = {}, pages = {1-5}, doi = {10.1109/ICORR55369.2022.9896582}, pmid = {36176162}, issn = {1945-7901}, mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Electromyography ; Feasibility Studies ; Humans ; *Movement/physiology ; Muscles ; }, abstract = {Several methods have been used to quantify human movement at different levels, from coordinated multi joint movements to those taking place at the single muscle level. These methods are developed either in order to allow us to interact with computers and machines, or to use such technologies for aiding rehabilitation among those with mobility impairments or movement disorders. Human machine interfaces typically rely on some existing human movement ability and measure it using motion tracking or inertial measurement units, while the rehabilitation applications may require us to measure human motor intent. Surface or implanted electrodes, electromyography, electroencephalography, and brain computer interfaces are beneficial in this regard, but have their own shortcomings. We have previously shown feasibility of using ultrasound imaging (Sonomyography) to infer human motor intent and allow users to control external biomechatronic devices such as prosthetics. Here, we asked users to freely move their hand in three different movement patterns, measuring their actual joint angles and passively computing their Sonomyographic output signal. We found a high correlation between these two signals, demonstrating that the Sonomyography signal is not only user-controlled and stable, but it is closely linked with the user's actual movement level. These results could help design wearable rehabilitation or human computer interaction devices based on Sonomyography to decode human motor intent.}, }
@article {pmid36176154, year = {2022}, author = {Cardoso, ASS and Andreasen Struijk, LNS and Kaeseler, RL and Jochumsen, M}, title = {Comparing the Usability of Alternative EEG Devices to Traditional Electrode Caps for SSVEP-BCI Controlled Assistive Robots.}, journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]}, volume = {2022}, number = {}, pages = {1-6}, doi = {10.1109/ICORR55369.2022.9896588}, pmid = {36176154}, issn = {1945-7901}, mesh = {*Brain-Computer Interfaces ; Electrodes ; Electroencephalography ; Evoked Potentials ; Evoked Potentials, Visual ; Humans ; *Robotics ; }, abstract = {Despite having the potential to improve the lives of severely paralyzed users, non-invasive Brain Computer Interfaces (BCI) have yet to be integrated into their daily lives. The widespread adoption of BCI-driven assistive technology is hindered by its lacking usability, as both end-users and researchers alike find fault with traditional EEG caps. In this paper, we compare the usability of four EEG recording devices for Steady-State Visually Evoked Potentials (SSVEP)-BCI applications: an EEG cap (active gel electrodes), two headbands (passive gel or active dry electrodes), and two adhesive electrodes placed on each mastoid. Ten able-bodied participants tested each device by completing an 8-target SSVEP paradigm. Setup times were recorded, and participants rated their satisfaction with each device. The EEG cap obtained the best classification accuracies (Median = 98.96%), followed by the gel electrode headband (Median = 93.75%), and the dry electrode headband (Median = 91.14%). The mastoid electrodes obtained classification accuracies close to chance level (Med = 29.69%). Unknowing of the classification accuracy, participants found the mastoid electrodes to be the most comfortable and discrete. The dry electrode headband obtained the lowest user satisfaction score and was criticized for being too uncomfortable. Participants also noted that the EEG cap was too conspicuous. The gel-based headband provided a good trade-off between BCI performance and user satisfaction.}, }
@article {pmid36176143, year = {2022}, author = {Behboodi, A and Lee, WA and Bulea, TC and Damiano, DL}, title = {Evaluation of Multi-layer Perceptron Neural Networks in Predicting Ankle Dorsiflexion in Healthy Adults using Movement-related Cortical Potentials for BCI-Neurofeedback Applications.}, journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]}, volume = {2022}, number = {}, pages = {1-5}, pmid = {36176143}, issn = {1945-7901}, support = {ZIA CL090084/ImNIH/Intramural NIH HHS/United States ; }, mesh = {Adolescent ; Adult ; Ankle ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Movement/physiology ; Neural Networks, Computer ; *Neurofeedback/physiology ; Reproducibility of Results ; Young Adult ; }, abstract = {Brain computer interface (BCI) systems were initially developed to replace lost function; however, they are being increasingly utilized in rehabilitation to restore motor functioning after brain injury. In such BCI-mediated neurofeedback training (BCI-NFT), the brain-state associated with movement attempt or intention is used to activate an external device which assists the movement while providing sensory feedback to enhance neuroplasticity. A critical element in the success of BCI-NFT is accurate timing of the feedback within the active period of the brain state. The overarching goal of this work was to develop a reliable deep learning model that can predict motion before its onset, and thereby deliver the sensory stimuli in a timely manner for BCI-NFT applications. To this end, the main objective of the current study was to design and evaluate a Multi-layer Perceptron Neural Network (MLP-NN). Movement-related cortical potentials (MRCP) during planning and execution of ankle dorsiflexion was used to train the model to classify dorsiflexion planning vs. rest. The accuracy and reliability of the model was evaluated offline using data from eight healthy individuals (age: 26.3 ± 7.6 years). First, we evaluated three different epoching strategies for defining our 2 classes, to identify the one which best discriminated rest from dorsiflexion. The best model accuracy for predicting ankle dorsiflexion from EEG before movement execution was 84.7%. Second, the effect of various spatial filters on the model accuracy was evaluated, demonstrating that the spatial filtering had minimal effect on model accuracy and reliability.}, }
@article {pmid36176084, year = {2022}, author = {Jo, S and Jung, JH and Yang, MJ and Lee, Y and Jang, SJ and Feng, J and Heo, SH and Kim, J and Shin, JH and Jeong, J and Park, HS}, title = {EEG-EMG hybrid real-time classification of hand grasp and release movements intention in chronic stroke patients.}, journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]}, volume = {2022}, number = {}, pages = {1-6}, doi = {10.1109/ICORR55369.2022.9896592}, pmid = {36176084}, issn = {1945-7901}, mesh = {Activities of Daily Living ; Electroencephalography/methods ; Hand ; Hand Strength ; Humans ; Intention ; *Stroke ; *Stroke Rehabilitation/methods ; }, abstract = {Rehabilitation of the hand motor function is essential for stroke patients to resume activities of daily living. Recent studies have shown that wearable robot systems, like a multi degree-of-freedom soft glove, have the potential to improve hand motor impairment. The rehabilitation system, which is intuitively controlled according to the user's intention, is expected to induce active participation of the user and further promote brain plasticity. However, due to the patient-specific nature of stroke patients, extracting the intention from stroke patients is still challenging. In this study, we implemented a classifier that combines EEG and EMG to detect chronic stroke patients' four types of intention: rest, grasp, hold, and release. Three chronic stroke patients participated in the experiment and performed rest, grasp, hold, and release actions. The rest vs. grasp binary classifier and release vs. hold binary classifier showed 76.9% and 86.6% classification accuracy in real-time, respectively. In addition, patient-specific accuracy comparisons showed that the hybrid approach was robust to upper limb impairment level compared to other approaches. We believe that these results could pave the way for the development of BCI-based robotic hand rehabilitation therapy.}, }
@article {pmid36172602, year = {2022}, author = {Song, M and Jeong, H and Kim, J and Jang, SH and Kim, J}, title = {An EEG-based asynchronous MI-BCI system to reduce false positives with a small number of channels for neurorehabilitation: A pilot study.}, journal = {Frontiers in neurorobotics}, volume = {16}, number = {}, pages = {971547}, pmid = {36172602}, issn = {1662-5218}, abstract = {Many studies have used motor imagery-based brain-computer interface (MI-BCI) systems for stroke rehabilitation to induce brain plasticity. However, they mainly focused on detecting motor imagery but did not consider the effect of false positive (FP) detection. The FP could be a threat to patients with stroke as it can induce wrong-directed brain plasticity that would result in adverse effects. In this study, we proposed a rehabilitative MI-BCI system that focuses on rejecting the FP. To this end, we first identified numerous electroencephalogram (EEG) signals as the causes of the FP, and based on the characteristics of the signals, we designed a novel two-phase classifier using a small number of EEG channels, including the source of the FP. Through experiments with eight healthy participants and nine patients with stroke, our proposed MI-BCI system showed 71.76% selectivity and 13.70% FP rate by using only four EEG channels in the patient group with stroke. Moreover, our system can compensate for day-to-day variations for prolonged session intervals by recalibration. The results suggest that our proposed system, a practical approach for the clinical setting, could improve the therapeutic effect of MI-BCI by reducing the adverse effect of the FP.}, }
@article {pmid36171871, year = {2022}, author = {Duan, J and Li, S and Ling, L and Zhang, N and Meng, J}, title = {Exploring the effects of head movements and accompanying gaze fixation switch on steady-state visual evoked potential.}, journal = {Frontiers in human neuroscience}, volume = {16}, number = {}, pages = {943070}, pmid = {36171871}, issn = {1662-5161}, abstract = {In a realistic steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) application like driving a car or controlling a quadrotor, observing the surrounding environment while simultaneously gazing at the stimulus is necessary. This kind of application inevitably could cause head movements and variation of the accompanying gaze fixation point, which might affect the SSVEP and BCI's performance. However, few papers studied the effects of head movements and gaze fixation switch on SSVEP response, and the corresponding BCI performance. This study aimed to explore these effects by designing a new ball tracking paradigm in a virtual reality (VR) environment with two different moving tasks, i.e., the following and free moving tasks, and three moving patterns, pitch, yaw, and static. Sixteen subjects were recruited to conduct a BCI VR experiment. The offline data analysis showed that head moving patterns [F(2, 30) = 9.369, p = 0.001, effect size = 0.384] resulted in significantly different BCI decoding performance but the moving tasks had no effect on the results [F(1, 15) = 3.484, p = 0.082, effect size = 0.188]. Besides, the canonical correlation analysis (CCA) and filter bank canonical correlation analysis (FBCCA) accuracy were better than the PSDA and MEC methods in all of the conditions. These results implied that head movement could significantly affect the SSVEP performance but it was possible to switch gaze fixation to interact with the surroundings in a realistic BCI application.}, }
@article {pmid36171602, year = {2022}, author = {Behboodi, A and Lee, WA and Hinchberger, VS and Damiano, DL}, title = {Determining optimal mobile neurofeedback methods for motor neurorehabilitation in children and adults with non-progressive neurological disorders: a scoping review.}, journal = {Journal of neuroengineering and rehabilitation}, volume = {19}, number = {1}, pages = {104}, pmid = {36171602}, issn = {1743-0003}, mesh = {Adult ; *Brain-Computer Interfaces ; Child ; Electroencephalography/methods ; Humans ; *Neurofeedback ; *Neurological Rehabilitation ; *Stroke ; }, abstract = {BACKGROUND: Brain-computer interfaces (BCI), initially designed to bypass the peripheral motor system to externally control movement using brain signals, are additionally being utilized for motor rehabilitation in stroke and other neurological disorders. Also called neurofeedback training, multiple approaches have been developed to link motor-related cortical signals to assistive robotic or electrical stimulation devices during active motor training with variable, but mostly positive, functional outcomes reported. Our specific research question for this scoping review was: for persons with non-progressive neurological injuries who have the potential to improve voluntary motor control, which mobile BCI-based neurofeedback methods demonstrate or are associated with improved motor outcomes for Neurorehabilitation applications?<br><br>METHODS: We searched PubMed, Web of Science, and Scopus databases with all steps from study selection to data extraction performed independently by at least 2 individuals. Search terms included: brain machine or computer interfaces, neurofeedback and motor; however, only studies requiring a motor attempt, versus motor imagery, were retained. Data extraction included participant characteristics, study design details and motor outcomes.<br><br>RESULTS: From 5109 papers, 139 full texts were reviewed with 23 unique studies identified. All utilized EEG and, except for one, were on the stroke population. The most commonly reported functional outcomes were the Fugl-Meyer Assessment (FMA; n&#xa0;=&#xa0;13) and the Action Research Arm Test (ARAT; n&#xa0;=&#xa0;6) which were then utilized to assess effectiveness, evaluate design features, and correlate with training doses. Statistically and functionally significant pre-to post training changes were seen in FMA, but not ARAT. Results did not differ between robotic and electrical stimulation feedback paradigms. Notably, FMA outcomes were positively correlated with training dose.<br><br>CONCLUSION: This review on BCI-based neurofeedback training confirms previous findings of effectiveness in improving motor outcomes with some evidence of enhanced neuroplasticity in adults with stroke. Associative learning paradigms have emerged more recently which may be particularly feasible and effective methods for Neurorehabilitation. More clinical trials in pediatric and adult neurorehabilitation to refine methods and doses and to compare to other evidence-based training strategies are warranted.}, }
@article {pmid36171400, year = {2022}, author = {Mansour, S and Giles, J and Ang, KK and Nair, KPS and Phua, KS and Arvaneh, M}, title = {Exploring the ability of stroke survivors in using the contralesional hemisphere to control a brain-computer interface.}, journal = {Scientific reports}, volume = {12}, number = {1}, pages = {16223}, pmid = {36171400}, issn = {2045-2322}, support = {MC-PC-19051/MRC_/Medical Research Council/United Kingdom ; }, mesh = {*Brain-Computer Interfaces ; Humans ; *Stroke ; *Stroke Rehabilitation/methods ; Survivors ; Upper Extremity ; }, abstract = {Brain-computer interfaces (BCIs) have recently been shown to be clinically effective as a novel method of stroke rehabilitation. In many BCI-based studies, the activation of the ipsilesional hemisphere was considered a key factor required for motor recovery after stroke. However, emerging evidence suggests that the contralesional hemisphere also plays a role in motor function rehabilitation. The objective of this study is to investigate the effectiveness of the BCI in detecting motor imagery of the affected hand from contralesional hemisphere. We analyzed a large EEG dataset from 136 stroke patients who performed motor imagery of their stroke-impaired hand. BCI features were extracted from channels covering either the ipsilesional, contralesional or bilateral hemisphere, and the offline BCI accuracy was computed using 10 [Formula: see text] 10-fold cross-validations. Our results showed that most stroke patients can operate the BCI using either their contralesional or ipsilesional hemisphere. Those with the ipsilesional BCI accuracy of less than 60% had significantly higher motor impairments than those with the ipsilesional BCI accuracy above 80%. Interestingly, those with the ipsilesional BCI accuracy of less than 60% achieved a significantly higher contralesional BCI accuracy, whereas those with the ipsilesional BCI accuracy more than 80% had significantly poorer contralesional BCI accuracy. This study suggests that contralesional BCI may be a useful approach for those with a high motor impairment who cannot accurately generate signals from ipsilesional hemisphere to effectively operate BCI.}, }
@article {pmid36170408, year = {2022}, author = {Yan, T and Suzuki, K and Kameda, S and Kuratomi, T and Mihara, M and Maeda, M and Hirata, M}, title = {Intracranial EEG Recordings of High-frequency Activity from a Wireless Implantable BMI Device in Awake Nonhuman Primates.}, journal = {IEEE transactions on bio-medical engineering}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBME.2022.3210286}, pmid = {36170408}, issn = {1558-2531}, abstract = {OBJECTIVE: Wireless implantable brain machine interfaces (BMIs) are a promising tool to restore communication and motor functions for individuals with severe motor disability. Prior to clinical application, recording performance must be sufficiently confirmed by animal experiments. In this paper, we aimed to evaluate the performance of a novel BMI wireless device for recording brain activity in two nonhuman primates.<br><br>METHOD: We customized a wireless device for implantable BMIs for clinical application. We used a battery instead of a wireless power charging system. Thirty-two electrodes were subdurally implanted over the left temporoparietal cortex. We evaluated the recording performance of the wireless device by auditory steady-state responses (ASSRs) and ketamine-induced responses.<br><br>RESULT: The devices successfully recorded broadband oscillatory activities up to the high-frequency band from the temporal cortex in two awake macaque monkeys. Spectral analysis of raw signals demonstrated that the devices detected characteristic results of a 40-Hz ASSR and prominent high-frequency band activity induced by ketamine injection.<br><br>CONCLUSION: We confirmed the functionality of the wireless device in recording and transmitting electrocorticography (ECoG) signals with both millisecond precision and recording stability.<br><br>SIGNIFICANCE: These results provide confidence that this wireless device can be a translational tool for other fundamental neuroscientific studies in free-moving models.}, }
@article {pmid36170151, year = {2022}, author = {Lu, X and Chen, L and Jiang, C and Cao, K and Gao, Z and Wang, Y}, title = {Microglia and macrophages contribute to the development and maintenance of sciatica in lumbar disc herniation.}, journal = {Pain}, volume = {}, number = {}, pages = {}, doi = {10.1097/j.pain.0000000000002708}, pmid = {36170151}, issn = {1872-6623}, abstract = {Lumbar disc herniation (LDH) is a major cause of sciatica. Emerging evidence indicated that inflammation induced by the herniated nucleus pulposus (NP) tissues plays a major role in the pathogenesis of sciatica. However, the underlying mechanisms are still elusive. Although microglia and macrophages have been implicated in nerve injury-induced neuropathic pain, their roles in LDH-induced sciatica largely remain unknown. This study successfully established and modified a mouse model of LDH. We found that nerve root compression using degenerated NP tissues can initiate remarkable and persistent sciatica, with increased and prolonged macrophage infiltration in dorsal root ganglia (DRG) and significant activation of microglia in the spinal dorsal horn. Instead, compression of the nerve root with nondegenerated NP tissues only led to transient sciatica, with transient infiltration and activation of macrophages and microglia. Moreover, continuous treatment of PLX5622, a specific colony-stimulating factor 1 receptor antagonist, ablated both macrophages and microglia, which effectively alleviated LDH-induced sciatica. However, mechanical allodynia reoccurred along with the repopulation of macrophages and microglia after the withdrawal of PLX5622. Using RNA sequencing analysis, the current study depicted transcriptional profile changes of DRG after LDH and identified several macrophage-related potential target candidates. Our results suggested that microglia and macrophages may play an essential role in the development and maintenance of LDH-induced sciatica. Targeting microglia and macrophages may be a promising treatment for chronic LDH-induced sciatica.}, }
@article {pmid36168224, year = {2022}, author = {He, X and Wu, M and Li, H and Liu, S and Liu, B and Qi, H}, title = {Real-time regulation of room temperature based on individual thermal sensation using an online brain-computer interface.}, journal = {Indoor air}, volume = {32}, number = {9}, pages = {e13106}, doi = {10.1111/ina.13106}, pmid = {36168224}, issn = {1600-0668}, mesh = {*Air Pollution, Indoor ; *Brain-Computer Interfaces ; Humans ; Skin Temperature ; Temperature ; Thermosensing/physiology ; }, abstract = {Regulation of indoor temperature based on neurophysiological and psychological signals is one of the most promising technologies for intelligent buildings. In this study, we developed a system for closed-loop control of indoor temperature based on brain-computer interface (BCI) technology for the first time. Electroencephalogram (EEG) signals were collected from subjects for two room temperature categories (cool comfortable and hot uncomfortable) and used to build a thermal-sensation discrimination model (TSDM) with an ensemble learning method. Then, an online BCI system was developed based on the TSDM. In the online room temperature control experiment, when the TSDM detected that the subjects felt hot and uncomfortable, BCI would automatically turn on the air conditioner, and when the TSDM detected that the subjects felt cool and comfortable, BCI would automatically turn off the air conditioner. The results of online experiments in a hot environment showed that a BCI could significantly improve the thermal comfort of subjects (the subjective thermal comfort score decreased from 2.45 (hot uncomfortable) to 0.55 (cool comfortable), p < 0.001). A parallel experiment further showed that if the subjects wore thicker clothes during the experiment, the BCI would turn on the air conditioner for a longer time to ensure the thermal comfort of the subjects. This has further confirmed the effectiveness of TSDM model in evaluating thermal sensation under the dynamic change of room temperature and showed the model's good robustness. This study proposed a new paradigm of human-building interaction, which is expected to play a promising role in the development of human-centered intelligent buildings.}, }
@article {pmid36166987, year = {2022}, author = {Tang, Z and Wang, X and Wu, J and Ping, Y and Guo, X and Cui, Z}, title = {A BCI painting system using a hybrid control approach based on SSVEP and P300.}, journal = {Computers in biology and medicine}, volume = {150}, number = {}, pages = {106118}, doi = {10.1016/j.compbiomed.2022.106118}, pmid = {36166987}, issn = {1879-0534}, abstract = {Brain-computer interfaces (BCIs) can help people with disabilities to communicate with others, express themselves, and even create art. In this paper, a BCI painting system using a hybrid control approach based on steady-state visual evoked potential (SSVEP) and P300 was developed, which can enable simple painting through brain-controlled painting tools. The BCI painting system is composed of two parts: a hybrid stimulus interface and a hybrid electroencephalogram (EEG) signal processing module. The user selects the menus and tools through the SSVEP and P300 stimulus matrices, respectively, and the paintings are displayed in the canvas area of the hybrid stimulus interface in real time. Twenty subjects participated in this study. An offline training experiment was performed to construct the P300 and SSVEP recognition models for each subject; an online painting experiment, which included a copy-painting task and a free-painting task, was performed to evaluate the BCI painting system. The results of the online painting experiment showed that the average tool selection accuracy (88.92 ± 3.94%) of the BCI painting system using the hybrid stimulus interface was slightly higher than that of the traditional brain painting system based on the P300 stimulus interface; the average information transfer rate (ITR) (74.20 ± 5.28 bpm, 71.80 ± 5.15 bpm) in the copy-painting and free-painting tasks of the BCI painting system was significantly higher than that of the traditional brain painting system. Our BCI painting system can effectively help users express their artistic creativity and improve their painting efficiency, and can provide new methods and new ideas for developing BCI-controlled applications.}, }
@article {pmid36166895, year = {2022}, author = {Zhang, H and Fu, P and Liu, Y and Zheng, Z and Zhu, L and Wang, M and Abdellah, M and He, M and Qian, J and Roe, AW and Xi, W}, title = {Large-depth three-photon fluorescence microscopy imaging of cortical microvasculature on nonhuman primates with bright AIE probe In vivo.}, journal = {Biomaterials}, volume = {289}, number = {}, pages = {121809}, doi = {10.1016/j.biomaterials.2022.121809}, pmid = {36166895}, issn = {1878-5905}, mesh = {Animals ; *Cerebral Cortex ; *Fluorescent Dyes/chemistry ; Humans ; Macaca ; Mice ; Microscopy, Fluorescence ; Microscopy, Fluorescence, Multiphoton/methods ; Microvessels ; Optical Imaging ; }, abstract = {Multiphoton microscopy has been a powerful tool in brain research, three-photon fluorescence microscopy is increasingly becoming an emerging technique for neurological research of the cortex in depth. Nonhuman primates play important roles in the study of brain science because of their neural and vascular similarity to humans. However, there are few research results of three-photon fluorescence microscopy on the brain of nonhuman primates due to the lack of optimized imaging systems and excellent fluorescent probes. Here we introduced a bright aggregation-induced emission (AIE) probe with excellent three-photon fluorescence efficiency as well as facile synthesis process and we validated its biocompatibility in the macaque monkey. We achieved a large-depth vascular imaging of approximately 1 mm in the cerebral cortex of macaque monkey with our lab-modified three-photon fluorescence microscopy system and the AIE probe. Functional measurement of blood velocity in deep cortex capillaries was also performed. Furthermore, the comparison of cortical deep vascular structure parameters across species was presented on the monkey and mouse cortex. This work is the first in vivo three-photon fluorescence microscopic imaging research on the macaque monkey cortex reaching the imaging depth of ∼1 mm with the bright AIE probe. The results demonstrate the potential of three-photon microscopy as primate-compatible method for imaging fine vascular networks and will advance our understanding of vascular function in normal and disease in humans.}, }
@article {pmid36163326, year = {2022}, author = {Wang, Q and Liu, Y and Wang, H and Jiang, P and Qian, W and You, M and Han, Y and Zeng, X and Li, J and Lu, H and Jiang, L and Zhu, M and Li, S and Huang, K and Tang, M and Wang, X and Yan, L and Xiong, Z and Shi, X and Bai, G and Liu, H and Li, Y and Zhao, Y and Chen, C and Qian, P}, title = {Graphdiyne oxide nanosheets display selective anti-leukemia efficacy against DNMT3A-mutant AML cells.}, journal = {Nature communications}, volume = {13}, number = {1}, pages = {5657}, pmid = {36163326}, issn = {2041-1723}, mesh = {Actins/genetics ; CD18 Antigens ; DNA ; *DNA (Cytosine-5-)-Methyltransferases/genetics ; DNA Methyltransferase 3A ; DNA Modification Methylases/genetics ; Graphite ; Humans ; *Leukemia, Myeloid, Acute/drug therapy/genetics ; Mutation ; Oxides ; }, abstract = {DNA methyltransferase 3 A (DNMT3A) is the most frequently mutated gene in acute myeloid leukemia (AML). Although chemotherapy agents have improved outcomes for DNMT3A-mutant AML patients, there is still no targeted therapy highlighting the need for further study of how DNMT3A mutations affect AML phenotype. Here, we demonstrate that cell adhesion-related genes are predominantly enriched in DNMT3A-mutant AML cells and identify that graphdiyne oxide (GDYO) display an anti-leukemia effect specifically against these mutated cells. Mechanistically, GDYO directly interacts with integrin β2 (ITGB2) and c-type mannose receptor (MRC2), which facilitate the attachment and cellular uptake of GDYO. Furthermore, GDYO binds to actin and prevents actin polymerization, thus disrupting the actin cytoskeleton and eventually leading to cell apoptosis. Finally, we validate the in vivo safety and therapeutic potential of GDYO against DNMT3A-mutant AML cells. Collectively, these findings demonstrate that GDYO is an efficient and specific drug candidate against DNMT3A-mutant AML.}, }
@article {pmid36162641, year = {2023}, author = {Zhu, Z and Wang, H and Bi, H and Lv, J and Zhang, X and Wang, S and Zou, L}, title = {Dynamic functional connectivity changes of resting-state brain network in attention-deficit/hyperactivity disorder.}, journal = {Behavioural brain research}, volume = {437}, number = {}, pages = {114121}, doi = {10.1016/j.bbr.2022.114121}, pmid = {36162641}, issn = {1872-7549}, mesh = {Humans ; Child ; *Attention Deficit Disorder with Hyperactivity/diagnostic imaging ; Brain Mapping/methods ; Brain/diagnostic imaging ; Magnetic Resonance Imaging/methods ; Neural Pathways/diagnostic imaging ; }, abstract = {Patients with attention-deficit/hyperactivity disorder (ADHD) have shown abnormal functional connectivity and network disruptions at the whole-brain static level. However, the changes in brain networks in ADHD patients from dynamic functional connectivity (DFC) perspective have not been fully understood. Accordingly, we executed DFC analysis on resting-state fMRI data of 25 ADHD patients and 27 typically developing (TD) children. A sliding window and Pearson correlation were used to construct the dynamic brain network of all subjects. The k-means+ + clustering method was used to recognize three recurring DFC states, and finally, the mean dwell time, the fraction of time spent for each state, and graph theory metrics were quantified for further analysis. Our results showed that ADHD patients had abnormally increased mean dwell time and the fraction of time spent in state 2, which reached a significant level (p < 0.05). In addition, a weak correlation between the default mode network was associated in three states, and the positive correlations between visual network and attention network were smaller than TD in three states. Finally, the integration of each network node of ADHD in state 2 is more potent than that of TD, and the degree of node segregation is smaller than that of TD. These findings provide new evidence for the DFC study of ADHD; dynamic changes may better explain the developmental delay of ADHD and have particular significance for studying neurological mechanisms and adjuvant therapy of ADHD.}, }
@article {pmid36161174, year = {2022}, author = {Loriette, C and Amengual, JL and Ben Hamed, S}, title = {Beyond the brain-computer interface: Decoding brain activity as a tool to understand neuronal mechanisms subtending cognition and behavior.}, journal = {Frontiers in neuroscience}, volume = {16}, number = {}, pages = {811736}, pmid = {36161174}, issn = {1662-4548}, abstract = {One of the major challenges in system neurosciences consists in developing techniques for estimating the cognitive information content in brain activity. This has an enormous potential in different domains spanning from clinical applications, cognitive enhancement to a better understanding of the neural bases of cognition. In this context, the inclusion of machine learning techniques to decode different aspects of human cognition and behavior and its use to develop brain-computer interfaces for applications in neuroprosthetics has supported a genuine revolution in the field. However, while these approaches have been shown quite successful for the study of the motor and sensory functions, success is still far from being reached when it comes to covert cognitive functions such as attention, motivation and decision making. While improvement in this field of BCIs is growing fast, a new research focus has emerged from the development of strategies for decoding neural activity. In this review, we aim at exploring how the advanced in decoding of brain activity is becoming a major neuroscience tool moving forward our understanding of brain functions, providing a robust theoretical framework to test predictions on the relationship between brain activity and cognition and behavior.}, }
@article {pmid36158550, year = {2022}, author = {Zhang, X and Jiang, Y and Hou, W and Jiang, N}, title = {Age-related differences in the transient and steady state responses to different visual stimuli.}, journal = {Frontiers in aging neuroscience}, volume = {14}, number = {}, pages = {1004188}, pmid = {36158550}, issn = {1663-4365}, abstract = {OBJECTIVE: Brain-computer interface (BCI) has great potential in geriatric applications. However, most BCI studies in the literature used data from young population, and dedicated studies investigating the feasibility of BCIs among senior population are scarce. The current study, we analyzed the age-related differences in the transient electroencephalogram (EEG) response used in visual BCIs, i.e., visual evoked potential (VEP)/motion onset VEP (mVEP), and steady state-response, SSVEP/SSMVEP, between the younger group (age ranges from 22 to 30) and senior group (age ranges from 60 to 75).<br><br>METHODS: The visual stimulations, including flicker, checkerboard, and action observation (AO), were designed with a periodic frequency. Videos of several hand movement, including grasping, dorsiflexion, the thumb opposition, and pinch were utilized to generate the AO stimuli. Eighteen senior and eighteen younger participants were enrolled in the experiments. Spectral-temporal characteristics of induced EEG were compared. Three EEG algorithms, canonical correlation analysis (CCA), task-related component analysis (TRCA), and extended CCA, were utilized to test the performance of the respective BCI systems.<br><br>RESULTS: In the transient response analysis, the motion checkerboard and AO stimuli were able to elicit prominent mVEP with a specific P1 peak and N2 valley, and the amplitudes of P1 elicited in the senior group were significantly higher than those in the younger group. In the steady-state analysis, SSVEP/SSMVEP could be clearly elicited in both groups. The CCA accuracies of SSVEPs/SSMVEPs in the senior group were slightly lower than those in the younger group in most cases. With extended CCA, the performance of both groups improved significantly. However, for AO targets, the improvement of the senior group (from 63.1 to 71.9%) was lower than that of the younger group (from 63.6 to 83.6%).<br><br>CONCLUSION: Compared with younger subjects, the amplitudes of P1 elicited by motion onset is significantly higher in the senior group, which might be a potential advantage for seniors if mVEP-based BCIs is used. This study also shows for the first time that AO-based BCI is feasible for the senior population. However, new algorithms for senior subjects, especially in identifying AO targets, are needed.}, }
@article {pmid36156934, year = {2022}, author = {Moreno, J and Gross, ML and Becker, J and Hereth, B and Shortland, ND and Evans, NG}, title = {The ethics of AI-assisted warfighter enhancement research and experimentation: Historical perspectives and ethical challenges.}, journal = {Frontiers in big data}, volume = {5}, number = {}, pages = {978734}, pmid = {36156934}, issn = {2624-909X}, abstract = {The military applications of AI raise myriad ethical challenges. Critical among them is how AI integrates with human decision making to enhance cognitive performance on the battlefield. AI applications range from augmented reality devices to assist learning and improve training to implantable Brain-Computer Interfaces (BCI) to create bionic "super soldiers." As these technologies mature, AI-wired warfighters face potential affronts to cognitive liberty, psychological and physiological health risks and obstacles to integrating into military and civil society during their service and upon discharge. Before coming online and operational, however, AI-assisted technologies and neural interfaces require extensive research and human experimentation. Each endeavor raises additional ethical concerns that have been historically ignored thereby leaving military and medical scientists without a cogent ethics protocol for sustainable research. In this way, this paper is a "prequel" to the current debate over enhancement which largely considers neuro-technologies once they are already out the door and operational. To lay the ethics foundation for AI-assisted warfighter enhancement research, we present an historical overview of its technological development followed by a presentation of salient ethics research issues (ICRC, 2006). We begin with a historical survey of AI neuro-enhancement research highlighting the ethics lacunae of its development. We demonstrate the unique ethical problems posed by the convergence of several technologies in the military research setting. Then we address these deficiencies by emphasizing how AI-assisted warfighter enhancement research must pay particular attention to military necessity, and the medical and military cost-benefit tradeoffs of emerging technologies, all attending to the unique status of warfighters as experimental subjects. Finally, our focus is the enhancement of friendly or compatriot warfighters and not, as others have focused, enhancements intended to pacify enemy warfighters.}, }
@article {pmid36155481, year = {2022}, author = {Zhang, S and Ang, KK and Zheng, D and Hui, Q and Chen, X and Li, Y and Tang, N and Chew, E and Lim, RY and Guan, C}, title = {Learning EEG Representations With Weighted Convolutional Siamese Network: A Large Multi-Session Post-Stroke Rehabilitation Study.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {30}, number = {}, pages = {2824-2833}, doi = {10.1109/TNSRE.2022.3209155}, pmid = {36155481}, issn = {1558-0210}, mesh = {Humans ; *Stroke Rehabilitation ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Algorithms ; *Stroke ; }, abstract = {Although brain-computer interface (BCI) shows promising prospects to help post-stroke patients recover their motor function, its decoding accuracy is still highly dependent on feature extraction methods. Most current feature extractors in BCI are classification-based methods, yet very few works from literature use metric learning based methods to learn representations for BCI. To circumvent this shortage, we propose a deep metric learning based method, Weighted Convolutional Siamese Network (WCSN) to learn representations from electroencephalogram (EEG) signal. This approach can enhance the decoding accuracy by learning a low dimensional embedding to extract distance-based representations from pair-wise EEG data. To enhance training efficiency and algorithm performance, a temporal-spectral distance weighted sampling method is proposed to select more informative input samples. In addition, an adaptive training strategy is adopted to address the session-to-session non-stationarity by progressively updating the subject-specific model. The proposed method is applied on both upper limb and lower limb neurorehabilitation datasets acquired from 33 stroke patients, with a total of 358 sessions. Results indicate that using k-Nearest Neighbor as the classification algorithm, the proposed method yielded 72.8% and 66.0% accuracies for the two datasets respectively, significantly better than the other state-of-the-arts (). Without losing generality, we also evaluated the proposed method on two publicly available datasets acquired from healthy subjects, wherein the proposed algorithm demonstrated superior performance at most cases as well. Our results support, for the first time, the use of a metric learning based feature extractor to learn representations from non-stationary EEG signals for BCI-assisted post-stroke rehabilitation.}, }
@article {pmid36153356, year = {2022}, author = {Zippi, EL and You, AK and Ganguly, K and Carmena, JM}, title = {Selective modulation of cortical population dynamics during neuroprosthetic skill learning.}, journal = {Scientific reports}, volume = {12}, number = {1}, pages = {15948}, pmid = {36153356}, issn = {2045-2322}, support = {Award R01NS106094/NH/NIH HHS/United States ; }, mesh = {Animals ; *Brain-Computer Interfaces ; Learning ; Macaca ; *Motor Cortex/physiology ; Neurons/physiology ; Population Dynamics ; }, abstract = {Brain-machine interfaces (BMIs) provide a framework for studying how cortical population dynamics evolve over learning in a task in which the mapping between neural activity and behavior is precisely defined. Learning to control a BMI is associated with the emergence of coordinated neural dynamics in populations of neurons whose activity serves as direct input to the BMI decoder (direct subpopulation). While previous work shows differential modification of firing rate modulation in this population relative to a population whose activity was not directly input to the BMI decoder (indirect subpopulation), little is known about how learning-related changes in cortical population dynamics within these groups compare.To investigate this, we monitored both direct and indirect subpopulations as two macaque monkeys learned to control a BMI. We found that while the combined population increased coordinated neural dynamics, this increase in coordination was primarily driven by changes in the direct subpopulation. These findings suggest that motor cortex refines cortical dynamics by increasing neural variance throughout the entire population during learning, with a more pronounced coordination of firing activity in subpopulations that are causally linked to behavior.}, }
@article {pmid36152398, year = {2023}, author = {Nathwani, JN and Baucom, MR and Salvator, A and Makley, AT and Tsuei, BJ and Droege, CA and Goodman, MD and Nomellini, V}, title = {Evaluating the Utility of High Sensitivity Troponin in Blunt Cardiac Injury.}, journal = {The Journal of surgical research}, volume = {281}, number = {}, pages = {104-111}, doi = {10.1016/j.jss.2022.08.030}, pmid = {36152398}, issn = {1095-8673}, mesh = {Humans ; Troponin I ; Sensitivity and Specificity ; *Thoracic Injuries ; Electrocardiography ; *Myocardial Contusions ; Biomarkers ; }, abstract = {INTRODUCTION: Screening for blunt cardiac injury (BCI) includes obtaining a serum troponin level and an electrocardiogram for patients diagnosed with a sternal fracture. Our institution has transitioned to the use of a high sensitivity troponin I (hsTnI). The aim of this study was to determine whether hsTnI is comparable to troponin I (TnI) in identifying clinically significant BCI.<br><br>MATERIALS AND METHODS: Trauma patients presenting to a level I trauma center over a 24-mo period with the diagnosis of sternal fracture were screened for BCI. Any initial TnI more than 0.04&#xa0;ng/mL or hsTnI more than 18&#xa0;ng/L was considered positive for potential BCI. Clinically significant BCI was defined as a new-bundle branch block, ST wave change, echocardiogram change, or need for cardiac catheterization.<br><br>RESULTS: Two hundred sixty five patients with a sternal fracture were identified, 161 underwent screening with TnI and 104 with hsTnI. For TnI, the sensitivity and specificity for detection of clinically significant BCI was 0.80 and 0.79, respectively. For hsTnI, the sensitivity and specificity for detection of clinically significant BCI was 0.71 and 0.69, respectively. A multivariate analysis demonstrated the odds ratio for significant BCI with a positive TnI was 14.4 (95% confidence interval, 3.9-55.8, P&#xa0;<&#xa0;0.0001) versus an odds ratio of 5.48 (95% confidence interval 1.9-15.7, P&#xa0;=&#xa0;0.002) in the hsTnI group.<br><br>CONCLUSIONS: The sensitivity of hsTnI is comparable to TnI for detection of significant BCI. Additional investigation is needed to determine the necessity and interval for repeat testing and the need for additional diagnostic testing.}, }
@article {pmid36151487, year = {2022}, author = {Kapgate, D}, title = {Effective 2-D cursor control system using hybrid SSVEP + P300 visual brain computer interface.}, journal = {Medical &amp; biological engineering &amp; computing}, volume = {60}, number = {11}, pages = {3243-3254}, pmid = {36151487}, issn = {1741-0444}, mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials, Visual ; Humans ; Photic Stimulation ; User-Computer Interface ; }, abstract = {A cursor control system based on brain-computer interface (BCI) provides efficient computer access. These systems operate without any muscular activity from the user. Conventional BCI-based cursor control systems have several limitations. Therefore, hybrid SSVEP + P300 visual BCI (VBCI)-based cursor control is needed to overcome these limitations. This paper explores the feasibility of using noninvasive hybrid SSVEP + P300 VBCI for cursor control as a universal form of computer access. The proposed cursor control system has a graphical user interface (GUI) design that simultaneously evokes both SSVEP and P300 signals in the human cortex. The performance metrics of the proposed system are compared with conventional SSVEP VBCI and P300 VBCI-based cursor control systems. The proposed hybrid SSVEP + P300 BCI-based cursor control system achieves a maximum accuracy of 97.51% with a 27.15 bit/min information transfer rate (ITR). The results proved that the proposed system performed more efficiently than other systems. The proposed system was tested in a noisy environment and found to be suitable for real-world applications.}, }
@article {pmid36150969, year = {2022}, author = {Elston, TW and Wallis, JD}, title = {Decoding cognition in real-time.}, journal = {Trends in cognitive sciences}, volume = {26}, number = {12}, pages = {1073-1075}, doi = {10.1016/j.tics.2022.08.005}, pmid = {36150969}, issn = {1879-307X}, mesh = {Humans ; *Cognition ; *Cognitive Science ; }, abstract = {How can we study unobservable cognitive processes that cannot be measured directly? This has been an enduring challenge for cognitive scientists. In this essay we discuss advances in neurotechnology that could allow cognitive processes to be decoded in real-time and the implications that this may have for cognitive science and the treatment of neuropsychiatric disease.}, }
@article {pmid36147561, year = {2022}, author = {Wang, TY and Xia, FY and Gong, JW and Xu, XK and Lv, MC and Chatoo, M and Shamsi, BH and Zhang, MC and Liu, QR and Liu, TX and Zhang, DD and Lu, XJ and Zhao, Y and Du, JZ and Chen, XQ}, title = {CRHR1 mediates the transcriptional expression of pituitary hormones and their receptors under hypoxia.}, journal = {Frontiers in endocrinology}, volume = {13}, number = {}, pages = {893238}, pmid = {36147561}, issn = {1664-2392}, mesh = {Animals ; Hormones/metabolism ; *Hypothalamo-Hypophyseal System/metabolism ; Hypoxia/genetics/metabolism ; Pituitary-Adrenal System/metabolism ; Pro-Opiomelanocortin/genetics ; RNA, Messenger/genetics ; Rats ; *Receptors, Corticotropin-Releasing Hormone/genetics/metabolism ; Receptors, Cytokine/metabolism ; Transcription Factors/metabolism ; }, abstract = {Hypothalamus-pituitary-adrenal (HPA) axis plays critical roles in stress responses under challenging conditions such as hypoxia, via regulating gene expression and integrating activities of hypothalamus-pituitary-targets cells. However, the transcriptional regulatory mechanisms and signaling pathways of hypoxic stress in the pituitary remain to be defined. Here, we report that hypoxia induced dynamic changes in the transcription factors, hormones, and their receptors in the adult rat pituitary. Hypoxia-inducible factors (HIFs), oxidative phosphorylation, and cAMP signaling pathways were all differentially enriched in genes induced by hypoxic stress. In the pituitary gene network, hypoxia activated c-Fos and HIFs with specific pituitary transcription factors (Prop1), targeting the promoters of hormones and their receptors. HIF and its related signaling pathways can be a promising biomarker during acute or constant hypoxia. Hypoxia stimulated the transcription of marker genes for microglia, chemokines, and cytokine receptors of the inflammatory response. Corticotropin-releasing hormone receptor 1 (CRHR1) mediated the transcription of Pomc, Sstr2, and Hif2a, and regulated the function of HPA axis. Together with HIF, c-Fos initiated and modulated dynamic changes in the transcription of hormones and their receptors. The receptors were also implicated in the regulation of functions of target cells in the pituitary network under hypoxic stress. CRHR1 played an integrative role in the hypothalamus-pituitary-target axes. This study provides new evidence for CRHR1 involved changes of hormones, receptors, signaling molecules and pathways in the pituitary induced by hypoxia.}, }
@article {pmid36146323, year = {2022}, author = {Shah, U and Alzubaidi, M and Mohsen, F and Abd-Alrazaq, A and Alam, T and Househ, M}, title = {The Role of Artificial Intelligence in Decoding Speech from EEG Signals: A Scoping Review.}, journal = {Sensors (Basel, Switzerland)}, volume = {22}, number = {18}, pages = {}, pmid = {36146323}, issn = {1424-8220}, mesh = {Algorithms ; Artificial Intelligence ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Quality of Life ; Speech ; }, abstract = {Background: Brain traumas, mental disorders, and vocal abuse can result in permanent or temporary speech impairment, significantly impairing one's quality of life and occasionally resulting in social isolation. Brain-computer interfaces (BCI) can support people who have issues with their speech or who have been paralyzed to communicate with their surroundings via brain signals. Therefore, EEG signal-based BCI has received significant attention in the last two decades for multiple reasons: (i) clinical research has capitulated detailed knowledge of EEG signals, (ii) inexpensive EEG devices, and (iii) its application in medical and social fields. Objective: This study explores the existing literature and summarizes EEG data acquisition, feature extraction, and artificial intelligence (AI) techniques for decoding speech from brain signals. Method: We followed the PRISMA-ScR guidelines to conduct this scoping review. We searched six electronic databases: PubMed, IEEE Xplore, the ACM Digital Library, Scopus, arXiv, and Google Scholar. We carefully selected search terms based on target intervention (i.e., imagined speech and AI) and target data (EEG signals), and some of the search terms were derived from previous reviews. The study selection process was carried out in three phases: study identification, study selection, and data extraction. Two reviewers independently carried out study selection and data extraction. A narrative approach was adopted to synthesize the extracted data. Results: A total of 263 studies were evaluated; however, 34 met the eligibility criteria for inclusion in this review. We found 64-electrode EEG signal devices to be the most widely used in the included studies. The most common signal normalization and feature extractions in the included studies were the bandpass filter and wavelet-based feature extraction. We categorized the studies based on AI techniques, such as machine learning and deep learning. The most prominent ML algorithm was a support vector machine, and the DL algorithm was a convolutional neural network. Conclusions: EEG signal-based BCI is a viable technology that can enable people with severe or temporal voice impairment to communicate to the world directly from their brain. However, the development of BCI technology is still in its infancy.}, }
@article {pmid36146175, year = {2022}, author = {Cousens, GA and Fotis, MM and Bradshaw, CM and Ramirez-Alvarado, YM and McKittrick, CR}, title = {Characterization of Retronasal Airflow Patterns during Intraoral Fluid Discrimination Using a Low-Cost, Open-Source Biosensing Platform.}, journal = {Sensors (Basel, Switzerland)}, volume = {22}, number = {18}, pages = {}, pmid = {36146175}, issn = {1424-8220}, mesh = {Humans ; *Odorants ; *Smell/physiology ; }, abstract = {Nasal airflow plays a critical role in olfactory processes, and both retronasal and orthonasal olfaction involve sensorimotor processes that facilitate the delivery of volatiles to the olfactory epithelium during odor sampling. Although methods are readily available for monitoring nasal airflow characteristics in laboratory and clinical settings, our understanding of odor sampling behavior would be enhanced by the development of inexpensive wearable technologies. Thus, we developed a method of monitoring nasal air pressure using a lightweight, open-source brain-computer interface (BCI) system and used the system to characterize patterns of retronasal airflow in human participants performing an oral fluid discrimination task. Participants exhibited relatively sustained low-rate retronasal airflow during sampling punctuated by higher-rate pulses often associated with deglutition. Although characteristics of post-deglutitive pulses did not differ across fluid conditions, the cumulative duration, probability, and estimated volume of retronasal airflow were greater during discrimination of perceptually similar solutions. These findings demonstrate the utility of a consumer-grade BCI system in assessing human olfactory behavior. They suggest further that sensorimotor processes regulate retronasal airflow to optimize the delivery of volatiles to the olfactory epithelium and that discrimination of perceptually similar oral fluids may be accomplished by varying the duration of optimal airflow rate.}, }
@article {pmid36144152, year = {2022}, author = {Ide, K and Takahashi, S}, title = {A Review of Neurologgers for Extracellular Recording of Neuronal Activity in the Brain of Freely Behaving Wild Animals.}, journal = {Micromachines}, volume = {13}, number = {9}, pages = {}, pmid = {36144152}, issn = {2072-666X}, abstract = {Simultaneous monitoring of animal behavior and neuronal activity in the brain enables us to examine the neural underpinnings of behaviors. Conventionally, the neural activity data are buffered, amplified, multiplexed, and then converted from analog to digital in the head-stage amplifier, following which they are transferred to a storage server via a cable. Such tethered recording systems, intended for indoor use, hamper the free movement of animals in three-dimensional (3D) space as well as in large spaces or underwater, making it difficult to target wild animals active under natural conditions; it also presents challenges in realizing its applications to humans, such as the Brain-Machine Interfaces (BMI). Recent advances in micromachine technology have established a wireless logging device called a neurologger, which directly stores neural activity on ultra-compact memory media. The advent of the neurologger has triggered the examination of the neural correlates of 3D flight, underwater swimming of wild animals, and translocation experiments in the wild. Examples of the use of neurologgers will provide an insight into understanding the neural underpinnings of behaviors in the natural environment and contribute to the practical application of BMI. Here we outline the monitoring of the neural underpinnings of flying and swimming behaviors using neurologgers. We then focus on neuroethological findings and end by discussing their future perspectives.}, }
@article {pmid36144108, year = {2022}, author = {Zhang, J and Liu, D and Chen, W and Pei, Z and Wang, J}, title = {Deep Convolutional Neural Network for EEG-Based Motor Decoding.}, journal = {Micromachines}, volume = {13}, number = {9}, pages = {}, pmid = {36144108}, issn = {2072-666X}, abstract = {Brain-machine interfaces (BMIs) have been applied as a pattern recognition system for neuromodulation and neurorehabilitation. Decoding brain signals (e.g., EEG) with high accuracy is a prerequisite to building a reliable and practical BMI. This study presents a deep convolutional neural network (CNN) for EEG-based motor decoding. Both upper-limb and lower-limb motor imagery were detected from this end-to-end learning with four datasets. An average classification accuracy of 93.36 ± 1.68% was yielded on the four datasets. We compared the proposed approach with two other models, i.e., multilayer perceptron and the state-of-the-art framework with common spatial patterns and support vector machine. We observed that the performance of the CNN-based framework was significantly better than the other two models. Feature visualization was further conducted to evaluate the discriminative channels employed for the decoding. We showed the feasibility of the proposed architecture to decode motor imagery from raw EEG data without manually designed features. With the advances in the fields of computer vision and speech recognition, deep learning can not only boost the EEG decoding performance but also help us gain more insight from the data, which may further broaden the knowledge of neuroscience for brain mapping.}, }
@article {pmid36144107, year = {2022}, author = {Wang, M and Zhang, Y and Bin, J and Niu, L and Zhang, J and Liu, L and Wang, A and Tao, J and Liang, J and Zhang, L and Kang, X}, title = {Cold Laser Micro-Machining of PDMS as an Encapsulation Layer for Soft Implantable Neural Interface.}, journal = {Micromachines}, volume = {13}, number = {9}, pages = {}, pmid = {36144107}, issn = {2072-666X}, abstract = {PDMS (polydimethylsiloxane) is an important soft biocompatible material, which has various applications such as an implantable neural interface, a microfluidic chip, a wearable brain-computer interface, etc. However, the selective removal of the PDMS encapsulation layer is still a big challenge due to its chemical inertness and soft mechanical properties. Here, we use an excimer laser as a cold micro-machining tool for the precise removal of the PDMS encapsulation layer which can expose the electrode sites in an implantable neural interface. This study investigated and optimized the effect of excimer laser cutting parameters on the electrochemical impedance of a neural electrode by using orthogonal experiment design. Electrochemical impedance at the representative frequencies is discussed, which helps to construct the equivalent circuit model. Furthermore, the parameters of the equivalent circuit model are fitted, which reveals details about the electrochemical property of neural electrode using PDMS as an encapsulation layer. Our experimental findings suggest the promising application of excimer lasers in the micro-machining of implantable neural interface.}, }
@article {pmid36141685, year = {2022}, author = {Pap, IA and Oniga, S}, title = {A Review of Converging Technologies in eHealth Pertaining to Artificial Intelligence.}, journal = {International journal of environmental research and public health}, volume = {19}, number = {18}, pages = {}, pmid = {36141685}, issn = {1660-4601}, mesh = {Artificial Intelligence ; *COVID-19/epidemiology ; Delivery of Health Care ; Humans ; Pandemics ; *Telemedicine ; }, abstract = {Over the last couple of years, in the context of the COVID-19 pandemic, many healthcare issues have been exacerbated, highlighting the paramount need to provide both reliable and affordable health services to remote locations by using the latest technologies such as video conferencing, data management, the secure transfer of patient information, and efficient data analysis tools such as machine learning algorithms. In the constant struggle to offer healthcare to everyone, many modern technologies find applicability in eHealth, mHealth, telehealth or telemedicine. Through this paper, we attempt to render an overview of what different technologies are used in certain healthcare applications, ranging from remote patient monitoring in the field of cardio-oncology to analyzing EEG signals through machine learning for the prediction of seizures, focusing on the role of artificial intelligence in eHealth.}, }
@article {pmid36141073, year = {2022}, author = {Li, Q and Liu, Y and Shang, Y and Zhang, Q and Yan, F}, title = {Deep Sparse Autoencoder and Recursive Neural Network for EEG Emotion Recognition.}, journal = {Entropy (Basel, Switzerland)}, volume = {24}, number = {9}, pages = {}, pmid = {36141073}, issn = {1099-4300}, abstract = {Recently, emotional electroencephalography (EEG) has been of great importance in brain-computer interfaces, and it is more urgent to realize automatic emotion recognition. The EEG signal has the disadvantages of being non-smooth, non-linear, stochastic, and susceptible to background noise. Additionally, EEG signal processing network models have the disadvantages of a large number of parameters and long training time. To address the above issues, a novel model is presented in this paper. Initially, a deep sparse autoencoder network (DSAE) was used to remove redundant information from the EEG signal and reconstruct its underlying features. Further, combining a convolutional neural network (CNN) with long short-term memory (LSTM) can extract relevant features from task-related features, mine the correlation between the 32 channels of the EEG signal, and integrate contextual information from these frames. The proposed DSAE + CNN + LSTM (DCRNN) model was experimented with on the public dataset DEAP. The classification accuracies of valence and arousal reached 76.70% and 81.43%, respectively. Meanwhile, we conducted experiments with other comparative methods to further demonstrate the effectiveness of the DCRNN method.}, }
@article {pmid36140136, year = {2022}, author = {Muñoz, D and Barria, P and Cifuentes, CA and Aguilar, R and Baleta, K and Azorín, JM and Múnera, M}, title = {EEG Evaluation in a Neuropsychological Intervention Program Based on Virtual Reality in Adults with Parkinson's Disease.}, journal = {Biosensors}, volume = {12}, number = {9}, pages = {}, pmid = {36140136}, issn = {2079-6374}, mesh = {Adult ; Cognition/physiology ; Electroencephalography ; Humans ; *Parkinson Disease ; *Virtual Reality ; }, abstract = {Nowadays, several strategies for treating neuropsychologic function loss in Parkinson's disease (PD) have been proposed, such as physical activity performance and developing games to exercise the mind. However, few studies illustrate the incidence of these therapies in neuronal activity. This work aims to study the feasibility of a virtual reality-based program oriented to the cognitive functions' rehabilitation of PD patients. For this, the study was divided into intervention with the program, acquisition of signals, data processing, and results analysis. The alpha and beta bands' power behavior was determined by evaluating the electroencephalography (EEG) signals obtained during the execution of control tests and games of the "Hand Physics Lab" Software, from which five games related to attention, planning, and sequencing, concentration, and coordination were taken. Results showed the characteristic performance of the cerebral bands during resting states and activity states. In addition, it was determined that the beta band increased its activity in all the cerebral lobes in all the tested games (p-value < 0.05). On the contrary, just one game exhibited an adequate performance of the alpha band activity of the temporal and frontal lobes (p-value < 0.02). Furthermore, the visual attention and the capacity to process and interpret the information given by the surroundings was favored during the execution of trials (p-value < 0.05); thus, the efficacy of the virtual reality program to recover cognitive functions was verified. The study highlights implementing new technologies to rehabilitate people with neurodegenerative diseases.}, }
@article {pmid36139033, year = {2022}, author = {Jiang, L and Ding, X and Wang, W and Yang, X and Li, T and Lei, P}, title = {Head-to-Head Comparison of Different Blood Collecting Tubes for Quantification of Alzheimer's Disease Biomarkers in Plasma.}, journal = {Biomolecules}, volume = {12}, number = {9}, pages = {}, pmid = {36139033}, issn = {2218-273X}, mesh = {Adult ; *Alzheimer Disease/diagnosis ; Amyloid beta-Peptides ; Biomarkers ; Edetic Acid ; Female ; Heparin ; Humans ; Lithium ; Male ; Peptide Fragments ; Young Adult ; tau Proteins ; }, abstract = {To examine whether the type of blood collection tubes affects the quantification of plasma biomarkers for Alzheimer's disease analyzed with a single-molecule array (Simoa), we recruited a healthy cohort (n = 34, 11 males, mean age = 28.7 ± 7.55) and collected plasma in the following tubes: dipotassium ethylenediaminetetraacetic acid (K2-EDTA), heparin lithium (Li-Hep), and heparin sodium (Na-Hep). Plasma tau, phosphorylated tau 181 (p-tau181), amyloid β (1-40) (Aβ40), and amyloid β (1-42) (Aβ42) were quantified using Simoa. We compared the value of plasma analytes, as well as the effects of sex on the measurements. We found that plasma collected in Li-Hep and Na-Hep tubes yielded significantly higher tau and p-tau181 levels compared to plasma collected in K2-EDTA tubes from the same person, but there was no difference in the measured values of the Aβ40, Aβ42, and Aβ42/40 ratio. Therefore, the type of blood collecting tubes should be considered when planning studies that measure plasma tau.}, }
@article {pmid36138969, year = {2022}, author = {Gao, S and Yang, J and Shen, T and Jiang, W}, title = {A Parallel Feature Fusion Network Combining GRU and CNN for Motor Imagery EEG Decoding.}, journal = {Brain sciences}, volume = {12}, number = {9}, pages = {}, pmid = {36138969}, issn = {2076-3425}, abstract = {In recent years, deep-learning-based motor imagery (MI) electroencephalography (EEG) decoding methods have shown great potential in the field of the brain-computer interface (BCI). The existing literature is relatively mature in decoding methods for two classes of MI tasks. However, with the increase in MI task classes, decoding studies for four classes of MI tasks need to be further explored. In addition, it is difficult to obtain large-scale EEG datasets. When the training data are limited, deep-learning-based decoding models are prone to problems such as overfitting and poor robustness. In this study, we design a data augmentation method for MI-EEG. The original EEG is slid along the time axis and reconstructed to expand the size of the dataset. Second, we combine the gated recurrent unit (GRU) and convolutional neural network (CNN) to construct a parallel-structured feature fusion network to decode four classes of MI tasks. The parallel structure can avoid temporal, frequency and spatial features interfering with each other. Experimenting on the well-known four-class MI dataset BCI Competition IV 2a shows a global average classification accuracy of 80.7% and a kappa value of 0.74. The proposed method improves the robustness of deep learning to decode small-scale EEG datasets and alleviates the overfitting phenomenon caused by insufficient data. The method can be applied to BCI systems with a small amount of daily recorded data.}, }
@article {pmid36138897, year = {2022}, author = {Zhao, R and Zhang, T and Zhou, S and Huang, L}, title = {Emotional Brain Network Community Division Study Based on an Improved Immunogenetic Algorithm.}, journal = {Brain sciences}, volume = {12}, number = {9}, pages = {}, pmid = {36138897}, issn = {2076-3425}, abstract = {Emotion analysis has emerged as one of the most prominent study areas in the field of Brain Computer Interface (BCI) due to the critical role that the human brain plays in the creation of human emotions. In this study, a Multi-objective Immunogenetic Community Division Algorithm Based on Memetic Framework (MFMICD) was suggested to study different emotions from the perspective of brain networks. To improve convergence and accuracy, MFMICD incorporates the unique immunity operator based on the traditional genetic algorithm and combines it with the taboo search algorithm. Based on this approach, we examined how the structure of people's brain networks alters in response to different emotions using the electroencephalographic emotion database. The findings revealed that, in positive emotional states, more brain regions are engaged in emotion dominance, the information exchange between local modules is more frequent, and various emotions cause more varied patterns of brain area interactions than in negative brain states. A brief analysis of the connections between different emotions and brain regions shows that MFMICD is reliable in dividing emotional brain functional networks into communities.}, }
@article {pmid36138888, year = {2022}, author = {Liu, K and Yu, Y and Zeng, LL and Liang, X and Liu, Y and Chu, X and Lu, G and Zhou, Z}, title = {Effects of Low Mental Energy from Long Periods of Work on Brain-Computer Interfaces.}, journal = {Brain sciences}, volume = {12}, number = {9}, pages = {}, pmid = {36138888}, issn = {2076-3425}, abstract = {Brain-computer interfaces (BCIs) provide novel hands-free interaction strategies. However, the performance of BCIs is affected by the user's mental energy to some extent. In this study, we aimed to analyze the combined effects of decreased mental energy and lack of sleep on BCI performance and how to reduce these effects. We defined the low-mental-energy (LME) condition as a combined condition of decreased mental energy and lack of sleep. We used a long period of work (>=18 h) to induce the LME condition, and then P300- and SSVEP-based BCI tasks were conducted in LME or normal conditions. Ten subjects were recruited in this study. Each subject participated in the LME- and normal-condition experiments within one week. For the P300-based BCI, we used two decoding algorithms: stepwise linear discriminant (SWLDA) and least square regression (LSR). For the SSVEP-based BCI, we used two decoding algorithms: canonical correlation analysis (CCA) and filter bank canonical correlation analysis (FBCCA). Accuracy and information transfer rate (ITR) were used as performance metrics. The experimental results showed that for the P300-based BCI, the average accuracy was reduced by approximately 35% (with a SWLDA classifier) and approximately 40% (with a LSR classifier); the average ITR was reduced by approximately 6 bits/min (with a SWLDA classifier) and approximately 7 bits/min (with an LSR classifier). For the SSVEP-based BCI, the average accuracy was reduced by approximately 40% (with a CCA classifier) and approximately 40% (with a FBCCA classifier); the average ITR was reduced by approximately 20 bits/min (with a CCA classifier) and approximately 19 bits/min (with a FBCCA classifier). Additionally, the amplitude and signal-to-noise ratio of the evoked electroencephalogram signals were lower in the LME condition, while the degree of fatigue and the task load of each subject were higher. Further experiments suggested that increasing stimulus size, flash duration, and flash number could improve BCI performance in LME conditions to some extent. Our experiments showed that the LME condition reduced BCI performance, the effects of LME on BCI did not rely on specific BCI types and specific decoding algorithms, and optimizing BCI parameters (e.g., stimulus size) can reduce these effects.}, }
@article {pmid36137226, year = {2022}, author = {Xavier Macedo de Azevedo, F and Heimgärtner, R and Nebe, K}, title = {Development of a metric to evaluate the ergonomic principles of assistive systems, based on the DIN 92419.}, journal = {Ergonomics}, volume = {}, number = {}, pages = {1-28}, doi = {10.1080/00140139.2022.2127920}, pmid = {36137226}, issn = {1366-5847}, abstract = {The DIN 92419 defines six principles for assistive systems' ergonomic design. There is, however, a lack of measurement tools to evaluate assistive systems considering these principles. Consequently, this study developed a measurement tool for the quantitative evaluation of the fulfilment of each principle for assistive systems. A systematic literature review was performed to identify dimensions belonging to the principles, identify how previous research evaluated these dimensions, and develop a measurement tool for assistive systems. Findings show that scales commonly used for evaluating assistive systems disregard several aspects highlighted as relevant by research, implying the need for considering the DIN 92419 principles. Based on established scales and theoretical findings, a questionnaire, and a checklist for evaluating assistive systems were developed. The work provides a grounding for measuring relevant aspects of assistive systems. Further development is needed to substantiate the reliability and validity of the proposed questionnaire scales and items. Practitioner Summary: Responding to the gap of a holistic measurement tool to evaluate assistive systems, a systematic literature review was performed considering the DIN 92419 principles. This resulted in a comprehensive summary of relevant aspects of assistive systems that were made numerically measurable, which proposes better criteria to assess assistive systems. Abbreviations: IoT: internet of things; RQ: research question; TAM: technology acceptance model; UTAUT: unified theory of acceptance and use of technology; AaaS: adaptivity as a service; SAR: socially assistive robots; SEEV: salience, effort, expectancy, and value; PRISMA: preferred reporting items for systematic reviews and meta-analyses; HMI: human-machine interaction; HRI: human-robot interaction; BCI: brain-computer interface; QUEST: Quebec user evaluation of satisfaction with assistive technology; SUS: system usability scale; NASA-TLX: NASA task load index; ATD PA: assistive technology device predisposition assessment; Wheel Con: wheelchair use confidence scale; CATOM: caregiver assistive technology outcome measure; CBI: caregiver burden inventory; RoSAS: robotic social attributes scale; WheelCon: wheelchair use confidence scale; IMI: intrinsic motivation inventory; ATD PA: assistive technology device predisposition assessment; UEQ: User experience questionnaire; USEUQ: usefulness satisfaction and ease of use questionnaire; USPW: usability scale for power wheelchairs; UES: user engagement scale; SUTAQ: service user technology acceptability questionnaire; QUEAD: questionnaire for the evaluation of physical assistive devices; FATCAT: functional assessment tool for cognitive assistive technology; SE-HRI: human-robot interaction scale; SART: situation awareness rating technique; TSQ;WT: tele-healthcare satisfaction questionnaire-wearable technology; PAIF: participants' assessment of the intervention's feasibility; SWAT: subjective workload assessment technique; MARS-HA: measure of audiologic rehabilitation self-efficacy for hearing aids; IOI-HA: International outcome inventory for hearing aids; FMA: functional mobility assessment; FBIS: familiarity and behavioural intention survey; CSQ: client satisfaction questionnaire; COPM: canadian occupational performance measure; ATCS: assistive technology confidence scale; ACC: acceptance; SSP: safety, security and privacy; OPT: optimisation of resultant internal load; CTRL: controllability; ADAPT: adaptability; P&amp;I: perceptibility and identifiability; AAL: ambient assisted living; VR: virtual reality; AS: assistive system; WEIRD: Western, educated, industrialised, rich, and democratic; HEART: horizontal european activities of rehabilitation technology; AAATE: advancement of assistive technology in Europe's; GATE: global collaboration on assistive technology; ATA-C: assistive technology assessment toolkit.}, }
@article {pmid36136927, year = {2022}, author = {Chen, X and Liu, B and Wang, Y and Gao, X}, title = {A Spectrally-Dense Encoding Method for Designing a High-Speed SSVEP-BCI With 120 Stimuli.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {30}, number = {}, pages = {2764-2772}, doi = {10.1109/TNSRE.2022.3208717}, pmid = {36136927}, issn = {1558-0210}, mesh = {*Brain-Computer Interfaces ; Electroencephalography/methods ; *Evoked Potentials, Visual ; Humans ; Photic Stimulation/methods ; }, abstract = {The practical functionality of a brain-computer interface (BCI) is critically affected by the number of stimuli, especially for steady-state visual evoked potential based BCI (SSVEP-BCI), which shows promise for the implementation of a multi-target system for real-world applications. Joint frequency-phase modulation (JFPM) is an effective and widely used method in modulating SSVEPs. However, the ability of JFPM to implement an SSVEP-BCI system with a large number of stimuli, e.g., over 100 stimuli, remains unclear. To address this issue, a spectrally-dense JPFM (sJFPM) method is proposed to encode a broad array of stimuli, which modulates the low- and medium-frequency SSVEPs with a frequency interval of 0.1 Hz and triples the number of stimuli in conventional SSVEP-BCI to 120. To validate the effectiveness of the proposed 120-target BCI system, an offline experiment and a subsequent online experiment testing 18 healthy subjects in total were conducted. The offline experiment verified the feasibility of using sJFPM in designing an SSVEP-BCI system with 120 stimuli. Furthermore, the online experiment demonstrated that the proposed system achieved an average performance of 92.47±1.83% in online accuracy and 213.23±6.60 bits/min in online information transfer rate (ITR), where more than 75% of the subjects attained the accuracy above 90% and the ITR above 200 bits/min. This present study demonstrates the effectiveness of sJFPM in elevating the number of stimuli to more than 100 and extends our understanding of encoding a large number of stimuli by means of finer frequency division.}, }
@article {pmid36136926, year = {2022}, author = {Tao, Y and Xu, W and Wang, G and Yuan, Z and Wang, M and Houston, M and Zhang, Y and Chen, B and Yan, X and Wang, G}, title = {Decoding Multi-Class EEG Signals of Hand Movement Using Multivariate Empirical Mode Decomposition and Convolutional Neural Network.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {30}, number = {}, pages = {2754-2763}, doi = {10.1109/TNSRE.2022.3208710}, pmid = {36136926}, issn = {1558-0210}, mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Hand ; Humans ; Imagination ; Movement ; Neural Networks, Computer ; }, abstract = {Brain-computer interface (BCI) is a technology that connects the human brain and external devices. Many studies have shown the possibility of using it to restore motor control in stroke patients. One specific challenge of such BCI is that the classification accuracy is not high enough for multi-class movements. In this study, by using Multivariate Empirical Mode Decomposition (MEMD) and Convolutional Neural Network (CNN), a novel algorithm (MECN) was proposed to decode EEG signals for four kinds of hand movements. Firstly, the MEMD was used to decompose the movement-related electroencephalogram (EEG) signals to obtain the multivariate intrinsic empirical functions (MIMFs). Then, the optimal MIMFs fusion was performed based on sequential forward selection algorithm. Finally, the selected MIMFs were input to the CNN model for discriminating four kinds of hand movements. The average classification accuracy of thirteen subjects over the six-fold cross-validation reached 81.14% for 2s-data before the movement onset and 81.08% for 2s-data after the movement onset. The MECN method achieved statistically significant improvement on the state-of-the-art methods. The results showed that the algorithm proposed in this study can effectively decode four kinds of hand movements based on EEG signals.}, }
@article {pmid36133917, year = {2022}, author = {Tao, S and Zhang, Y and Wang, Q and Qiao, C and Deng, W and Liang, S and Wei, J and Wei, W and Yu, H and Li, X and Li, M and Guo, W and Ma, X and Zhao, L and Li, T}, title = {Identifying transdiagnostic biological subtypes across schizophrenia, bipolar disorder, and major depressive disorder based on lipidomics profiles.}, journal = {Frontiers in cell and developmental biology}, volume = {10}, number = {}, pages = {969575}, pmid = {36133917}, issn = {2296-634X}, abstract = {Emerging evidence has demonstrated overlapping biological abnormalities underlying schizophrenia (SCZ), bipolar disorder (BP), and major depressive disorder (MDD); these overlapping abnormalities help explain the high heterogeneity and the similarity of patients within and among diagnostic categories. This study aimed to identify transdiagnostic subtypes of these psychiatric disorders based on lipidomics abnormalities. We performed discriminant analysis to identify lipids that classified patients (N = 349, 112 with SCZ, 132 with BP, and 105 with MDD) and healthy controls (N = 198). Ten lipids that mainly regulate energy metabolism, inflammation, oxidative stress, and fatty acylation of proteins were identified. We found two subtypes (named Cluster 1 and Cluster 2 subtypes) across patients with SCZ, BP, and MDD by consensus clustering analysis based on the above 10 lipids. The distribution of clinical diagnosis, functional impairment measured by Global Assessment of Functioning (GAF) scales, and brain white matter abnormalities measured by fractional anisotropy (FA) and radial diffusivity (RD) differed in the two subtypes. Patients within the Cluster 2 subtype were mainly SCZ and BP patients and featured significantly elevated RD along the genu of corpus callosum (GCC) region and lower GAF scores than patients within the Cluster 1 subtype. The SCZ and BP patients within the Cluster 2 subtype shared similar biological patterns; that is, these patients had comparable brain white matter abnormalities and functional impairment, which is consistent with previous studies. Our findings indicate that peripheral lipid abnormalities might help identify homogeneous transdiagnostic subtypes across psychiatric disorders.}, }
@article {pmid36131024, year = {2022}, author = {Su, N and Zhu, A and Tao, X and Ding, ZJ and Chang, S and Ye, F and Zhang, Y and Zhao, C and Chen, Q and Wang, J and Zhou, CY and Guo, Y and Jiao, S and Zhang, S and Wen, H and Ma, L and Ye, S and Zheng, SJ and Yang, F and Wu, S and Guo, J}, title = {Publisher Correction: Structures and mechanisms of the Arabidopsis auxin transporter PIN3.}, journal = {Nature}, volume = {610}, number = {7930}, pages = {E2}, doi = {10.1038/s41586-022-05360-2}, pmid = {36131024}, issn = {1476-4687}, }
@article {pmid36130589, year = {2022}, author = {Wen, Y and He, W and Zhang, Y}, title = {A new attention-based 3D densely connected cross-stage-partial network for motor imagery classification in BCI.}, journal = {Journal of neural engineering}, volume = {19}, number = {5}, pages = {}, doi = {10.1088/1741-2552/ac93b4}, pmid = {36130589}, issn = {1741-2552}, mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Imagination ; Neural Networks, Computer ; }, abstract = {Objective. The challenge for motor imagery (MI) in brain-computer interface (BCI) systems is finding a reliable classification model that has high classification accuracy and excellent robustness. Currently, one of the main problems leading to degraded classification performance is the inaccuracy caused by nonstationarities and low signal-to-noise ratio in electroencephalogram (EEG) signals.Approach. This study proposes a novel attention-based 3D densely connected cross-stage-partial network (DCSPNet) model to achieve efficient EEG-based MI classification. This is an end-to-end classification model framework based on the convolutional neural network (CNN) architecture. In this framework, to fully utilize the complementary features in each dimension, the optimal features are extracted adaptively from the EEG signals through the spatial-spectral-temporal (SST) attention mechanism. The 3D DCSPNet is introduced to reduce the gradient loss by segmenting the extracted feature maps to strengthen the network learning capability. Additionally, the design of the densely connected structure increases the robustness of the network.Main results. The performance of the proposed method was evaluated using the BCI competition IV 2a and the high gamma dataset, achieving an average accuracy of 84.45% and 97.88%, respectively. Our method outperformed most state-of-the-art classification algorithms, demonstrating its effectiveness and strong generalization ability.Significance.The experimental results show that our method is promising for improving the performance of MI-BCI. As a general framework based on time-series classification, it can be applied to BCI-related fields.}, }
@article {pmid36129927, year = {2022}, author = {Shimizu, H and Srinivasan, R}, title = {Improving classification and reconstruction of imagined images from EEG signals.}, journal = {PloS one}, volume = {17}, number = {9}, pages = {e0274847}, pmid = {36129927}, issn = {1932-6203}, mesh = {Algorithms ; Attention ; Brain/diagnostic imaging/physiology ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Imagination/physiology ; }, abstract = {Decoding brain activity related to specific tasks, such as imagining something, is important for brain computer interface (BCI) control. While decoding of brain signals, such as functional magnetic resonance imaging (fMRI) signals and electroencephalography (EEG) signals, during observing visual images and while imagining images has been previously reported, further development of methods for improving training, performance, and interpretation of brain data was the goal of this study. We applied a Sinc-EEGNet to decode brain activity during perception and imagination of visual stimuli, and added an attention module to extract the importance of each electrode or frequency band. We also reconstructed images from brain activity by using a generative adversarial network (GAN). By combining the EEG recorded during a visual task (perception) and an imagination task, we have successfully boosted the accuracy of classifying EEG data in the imagination task and improved the quality of reconstruction by GAN. Our result indicates that the brain activity evoked during the visual task is present in the imagination task and can be used for better classification of the imagined image. By using the attention module, we can derive the spatial weights in each frequency band and contrast spatial or frequency importance between tasks from our model. Imagination tasks are classified by low frequency EEG signals over temporal cortex, while perception tasks are classified by high frequency EEG signals over occipital and frontal cortex. Combining data sets in training results in a balanced model improving classification of the imagination task without significantly changing performance in the visual task. Our approach not only improves performance and interpretability but also potentially reduces the burden on training since we can improve the accuracy of classifying a relatively hard task with high variability (imagination) by combining with the data of the relatively easy task, observing visual images.}, }
@article {pmid36129854, year = {2022}, author = {Zhang, W and Song, A and Zeng, H and Xu, B and Miao, M}, title = {The Effects of Bilateral Phase-Dependent Closed-Loop Vibration Stimulation With Motor Imagery Paradigm.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {30}, number = {}, pages = {2732-2742}, doi = {10.1109/TNSRE.2022.3208312}, pmid = {36129854}, issn = {1558-0210}, mesh = {*Brain-Computer Interfaces ; Electroencephalography/methods ; Hand/physiology ; Humans ; Imagination/physiology ; *Stroke Rehabilitation/methods ; Vibration ; }, abstract = {Vibration stimulation has been shown to have the potential to improve the activation pattern of unilateral motor imagery (MI) and to promote motor recovery. However, in the widely used left and right hand MI brain-computer interface (BCI) paradigm, the vibration stimuli cannot be directly applied to the imaginary side due to the spontaneity of imagery. In this study, we proposed a method of phase-dependent closed-loop vibration stimulation to be applied on both hands, and explored the effects of different vibration stimuli on the left and right hand MI-BCI. Eighteen healthy subjects were recruited and asked to perform, in sequence, MI tasks under three different conditions of vibratory feedback, which were no vibration stimulus (MI), phase-dependent closed-loop vibration stimulus (PDS), and continuous vibration stimulus (CS). Then the performance of the left and right hand MI-BCI and the patterns of brain oscillation were compared and analyzed under these different stimulation conditions. The results showed that vibration stimulation effectively boosted the activation of the sensorimotor cortex and enhanced the functional connectivity among sensorimotor-related brain regions during MI. The closed-loop stimulation evoked stronger event-related desynchronization patterns on the contralateral side of the imagined hand compared to continuous stimulation. There was a more obvious distinction between left hand task and right hand task. In addition, phase-dependent closed-loop vibration stimulation increased classification accuracy by approximately 7% (paired t-test, p=0.004, n=18) compared to MI alone, while continuous vibration stimulation only increased it by 4% (paired t-test, p=0.067, n=18). This result further demonstrated the effectiveness of the phase-dependent closed-loop vibration stimulation method in improving the overall performance of the MI paradigm and is expected to be further applied in areas such as stroke rehabilitation in the future.}, }
@article {pmid36126733, year = {2022}, author = {Liang, Z and Wang, X and Zhao, J and Li, X}, title = {Comparative study of attention-related features on attention monitoring systems with a single EEG channel.}, journal = {Journal of neuroscience methods}, volume = {382}, number = {}, pages = {109711}, doi = {10.1016/j.jneumeth.2022.109711}, pmid = {36126733}, issn = {1872-678X}, mesh = {Humans ; *Electroencephalography/methods ; *Attention ; Entropy ; Monitoring, Physiologic ; Computers ; Algorithms ; }, abstract = {The easy-to-use attention monitoring systems usually detect the participant's attentional status via processing electroencephalogram (EEG) data recorded from a single FPz channel. But due to the influence of noises and artifacts, the attention-monitoring performance needs to be further improved to suit different individuals and devices. This paper compared the attention-related features extracted using four state-of-the-art methods including delta/beta1 (D/B1), α + β + δ + θ + R, entropy and optimized complex network (OCN). The classification performance was evaluated using receiver operating characteristic (ROC) curves and area under the ROC curves (AUC) on two EEG data acquisition devices, i.e., a BrainAmp device with high precision and a Sichiray device with low cost, respectively. Considering the varied performance on different individuals and devices, this paper proposed a novel Mutual information-based feature fusion (MIFF) method, selecting the optimal combinations of the attention-related features for classification, to enhance the attention detection performance. The experimental results showed that the proposed MIFF method outperformed the state-of-the-art methods regardless of data length on both devices. Especially, the proposed method with data length of 2.5 s achieved an average AUC of 0.8505 on the low-cost Sichiray device, which is 56.08 % higher than that of D/B1, 27.28 % higher than that of α + β + δ + θ + R, 17.42 % higher than that of entropy, and 15.48 % higher than that of OCN.}, }
@article {pmid36126643, year = {2022}, author = {Qu, T and Jin, J and Xu, R and Wang, X and Cichocki, A}, title = {Riemannian distance based channel selection and feature extraction combining discriminative time-frequency bands and Riemannian tangent space for MI-BCIs.}, journal = {Journal of neural engineering}, volume = {19}, number = {5}, pages = {}, doi = {10.1088/1741-2552/ac9338}, pmid = {36126643}, issn = {1741-2552}, mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Imagery, Psychotherapy ; Imagination/physiology ; Support Vector Machine ; }, abstract = {Objective.Motor imagery-based brain computer interfaces (MI-BCIs) have been widely researched because they do not demand external stimuli and have a high degree of maneuverability. In most scenarios, superabundant selected channels, fixed time windows, and frequency bands would certainly affect the performance of MI-BCIs due to the neurophysiological diversities among different individuals. In this study, we attempt to effectively use the Riemannian geometry of spatial covariance matrix to extract more robust features and thus enhance the decoding efficiency.Approach.First, we utilize a Riemannian distance-based electroencephalography (EEG) channel selection method, which preliminarily reduces the information redundancy in the first stage. Second, we extract discriminative Riemannian tangent space features of EEG signals of selected channels from the most discriminant time-frequency bands to further enhance decoding accuracy for MI-BCIs. Finally, we train a support vector machine model with a linear kernel to classify our extracted discriminative Riemannian features, and evaluate our proposed method using publicly available BCI Competition IV dataset Ⅰ (DS1) and Competition Ⅲ dataset Ⅲa (DS2).Main results.The experimental results show that the average classification accuracy with the selected 16-channel EEG signals of our method is 90.0% and 89.4% in DS1 and DS2 respectively. The average improvements are 20.0% and 21.2% on DS1, 9.4% and 7.2% on DS2 for 8 and 16 selected channels, respectively.Significance.These results show that our proposed method is a promising candidate for the performance improvement of MI-BCIs.}, }
@article {pmid36126617, year = {2022}, author = {Guo, L and Qi, YJ and Tan, H and Dai, D and Balesar, R and Sluiter, A and van Heerikhuize, J and Hu, SH and Swaab, DF and Bao, AM}, title = {Different oxytocin and corticotropin-releasing hormone system changes in bipolar disorder and major depressive disorder patients.}, journal = {EBioMedicine}, volume = {84}, number = {}, pages = {104266}, pmid = {36126617}, issn = {2352-3964}, mesh = {Animals ; *Bipolar Disorder ; Corticotropin-Releasing Hormone/genetics/metabolism ; *Depressive Disorder, Major ; Female ; Male ; Mice ; Oxytocin ; RNA, Messenger/genetics ; }, abstract = {BACKGROUND: Oxytocin (OXT) and corticotropin-releasing hormone (CRH) are both produced in hypothalamic paraventricular nucleus (PVN). Central CRH may cause depression-like symptoms, while peripheral higher OXT plasma levels were proposed to be a trait marker for bipolar disorder (BD). We aimed to investigate differential OXT and CRH expression in the PVN and their receptors in prefrontal cortex of major depressive disorder (MDD) and BD patients. In addition, we investigated mood-related changes by stimulating PVN-OXT in mice.<br><br>METHODS: Quantitative immunocytochemistry and in situ hybridization were performed in the PVN for OXT and CRH on 6 BD and 6 BD-controls, 9 MDD and 9 MDD-controls. mRNA expressions of their receptors (OXTR, CRHR1 and CRHR2) were determined in anterior cingulate cortex and dorsolateral prefrontal cortex (DLPFC) of 30 BD and 34 BD-controls, and 24 MDD and 12 MDD-controls. PVN of 41 OXT-cre mice was short- or long-term activated by chemogenetics, and mood-related behavior was compared with 26 controls.<br><br>FINDINGS: Significantly increased OXT-immunoreactivity (ir), OXT-mRNA in PVN and increased OXTR-mRNA in DLPFC, together with increased ratios of OXT-ir/CRH-ir and OXTR-mRNA/CRHR-mRNA were observed in BD, at least in male BD patients, but not in MDD patients. PVN-OXT stimulation induced depression-like behaviors in male mice, and mixed depression/mania-like behaviors in female mice in a time-dependent way.<br><br>INTERPRETATION: Increased PVN-OXT and DLPFC-OXTR expression are characteristic for BD, at least for male BD patients. Stimulation of PVN-OXT neurons induced mood changes in mice, in a pattern different from BD.<br><br>FUNDING: National Natural Science Foundation of China (81971268, 82101592).}, }
@article {pmid36125443, year = {2022}, author = {Matsukawa, Y and Naito, Y and Ishida, S and Matsuo, K and Majima, T and Gotoh, M}, title = {Two types of detrusor underactivity in men with nonneurogenic lower urinary tract symptoms.}, journal = {Neurourology and urodynamics}, volume = {}, number = {}, pages = {}, doi = {10.1002/nau.25044}, pmid = {36125443}, issn = {1520-6777}, abstract = {AIMS: To clarify the clinical features of men with nonneurogenic detrusor underactivity (DU) by focusing on storage dysfunction (SD).<br><br>METHODS: We retrospectively reviewed the clinical and urodynamic data of men with nonneurogenic DU. Patients were divided into two groups according to the presence or absence of SD, such as detrusor overactivity (DO) and reduced bladder compliance (BC). Patient characteristics, lower urinary tract symptoms (LUTS), and urodynamic parameters were compared. DU was defined as bladder contractility index (BCI)&#x2009;&#x2264;&#x2009;100 and bladder outlet obstruction index&#xa0;(BOOI)&#x2009;&#x2264;&#x2009;40.<br><br>RESULTS: Of 212 men with DU, 123 (58.0%) had concomitant SD (SD&#x2009;+&#x2009;DU group), and 89 (42.0%) had only DU (DU-only group). Age, prostate volume, and severity of storage symptoms were significantly higher in the SD&#x2009;+&#x2009;DU group. Particularly, >80% of men in the SD&#x2009;+&#x2009;DU group met the diagnostic criteria for overactive bladder in Japan, which was significantly higher than the 26% of men in the DU-only group. The frequency of urinary urgency incontinence (UUI) was also significantly higher in the SD&#x2009;+&#x2009;DU group (65% vs. 12% in DU-only group). In contrast, voiding symptoms, including straining, were more severe in the DU-only group. Regarding the urodynamic parameters, compared to the DU-only group, bladder capacity was significantly smaller and BOOI and BCI were significantly higher in the SD&#x2009;+&#x2009;DU group. However, there was no significant difference in the maximum flow rate and bladder voiding efficiency.<br><br>CONCLUSIONS: Approximately 60% of men with DU had SD, such as DO and/or reduced BC, whereas the remaining 40% had increased bladder capacity without an increase in detrusor pressure during the storage phase. There were significant differences in the storage and voiding symptoms between the groups. It is important to divide patients with DU based on SD to accurately clarify the clinical picture of DU.}, }
@article {pmid36125116, year = {2022}, author = {Guan, C and Aflalo, T and Zhang, CY and Amoruso, E and Rosario, ER and Pouratian, N and Andersen, RA}, title = {Stability of motor representations after paralysis.}, journal = {eLife}, volume = {11}, number = {}, pages = {}, pmid = {36125116}, issn = {2050-084X}, support = {R01 EY015545/EY/NEI NIH HHS/United States ; UG1 EY032039/EY/NEI NIH HHS/United States ; }, mesh = {Adult ; *Brain-Computer Interfaces ; Female ; Fingers/physiology ; Humans ; Magnetic Resonance Imaging/methods ; *Motor Cortex/diagnostic imaging/physiology ; Movement/physiology ; Paralysis ; }, abstract = {Neural plasticity allows us to learn skills and incorporate new experiences. What happens when our lived experiences fundamentally change, such as after a severe injury? To address this question, we analyzed intracortical population activity in the posterior parietal cortex (PPC) of a tetraplegic adult as she controlled a virtual hand through a brain-computer interface (BCI). By attempting to move her fingers, she could accurately drive the corresponding virtual fingers. Neural activity during finger movements exhibited robust representational structure similar to fMRI recordings of able-bodied individuals' motor cortex, which is known to reflect able-bodied usage patterns. The finger representational structure was consistent throughout multiple sessions, even though the structure contributed to BCI decoding errors. Within individual BCI movements, the representational structure was dynamic, first resembling muscle activation patterns and then resembling the anticipated sensory consequences. Our results reveal that motor representations in PPC reflect able-bodied motor usage patterns even after paralysis, and BCIs can re-engage these stable representations to restore lost motor functions.}, }
@article {pmid36124975, year = {2022}, author = {Vansteensel, MJ and Branco, MP and Leinders, S and Freudenburg, ZF and Schippers, A and Geukes, SH and Gaytant, MA and Gosselaar, PH and Aarnoutse, EJ and Ramsey, NF}, title = {Methodological Recommendations for Studies on the Daily Life Implementation of Implantable Communication-Brain-Computer Interfaces for Individuals With Locked-in Syndrome.}, journal = {Neurorehabilitation and neural repair}, volume = {36}, number = {10-11}, pages = {666-677}, doi = {10.1177/15459683221125788}, pmid = {36124975}, issn = {1552-6844}, support = {U01 DC016686/DC/NIDCD NIH HHS/United States ; UH3 NS114439/NS/NINDS NIH HHS/United States ; }, mesh = {Humans ; *Brain-Computer Interfaces ; *Locked-In Syndrome ; Communication ; Brain ; Electroencephalography ; }, abstract = {Implantable brain-computer interfaces (BCIs) promise to be a viable means to restore communication in individuals with locked-in syndrome (LIS). In 2016, we presented the world-first fully implantable BCI system that uses subdural electrocorticography electrodes to record brain signals and a subcutaneous amplifier to transmit the signals to the outside world, and that enabled an individual with LIS to communicate via a tablet computer by selecting icons in spelling software. For future clinical implementation of implantable communication-BCIs, however, much work is still needed, for example, to validate these systems in daily life settings with more participants, and to improve the speed of communication. We believe the design and execution of future studies on these and other topics may benefit from the experience we have gained. Therefore, based on relevant literature and our own experiences, we here provide an overview of procedures, as well as recommendations, for recruitment, screening, inclusion, imaging, hospital admission, implantation, training, and support of participants with LIS, for studies on daily life implementation of implantable communication-BCIs. With this article, we not only aim to inform the BCI community about important topics of concern, but also hope to contribute to improved methodological standardization of implantable BCI research.}, }
@article {pmid36124150, year = {2022}, author = {Pabba, K and Widmer, RJ and Nguyen, V and Martinez, MW}, title = {Cardiac Contusion Complicated by Heart Failure in a Young Athlete.}, journal = {JACC. Case reports}, volume = {4}, number = {17}, pages = {1124-1128}, pmid = {36124150}, issn = {2666-0849}, abstract = {Chest trauma is a relatively common injury in athletes. Here, we report a case of a cardiac contusion in a football player that led to hemodynamically significant low-output state. Early invasive management was critical in treatment with imaging playing an important role in diagnosis. (Level of Difficulty: Advanced.).}, }
@article {pmid36122609, year = {2023}, author = {Caglar, HO and Duzgun, Z}, title = {Identification of upregulated genes in glioblastoma and glioblastoma cancer stem cells using bioinformatics analysis.}, journal = {Gene}, volume = {848}, number = {}, pages = {146895}, doi = {10.1016/j.gene.2022.146895}, pmid = {36122609}, issn = {1879-0038}, mesh = {Adult ; *Brain Neoplasms/pathology ; Computational Biology/methods ; ErbB Receptors/genetics ; Gene Expression Profiling/methods ; Gene Expression Regulation, Neoplastic ; *Glioblastoma/metabolism ; Humans ; Molecular Docking Simulation ; Neoplastic Stem Cells/metabolism ; }, abstract = {Glioblastoma (GBM) is the most common malignant brain tumor among adults. Cancer stem cells (CSCs) are known to drive treatment resistance and recurrence. However, a few CSC markers have been identified as therapeutic targets for GBM. This study aimed to show highly coexpressed genes in GBM CSCs and TCGA GBM samples and to identify possible therapeutic targets for GBM. The gene expression profiles of GBM CSCs were obtained from Gene Expression Omnibus database. After the differentially upregulated genes were screened, functional enrichment analyses were performed using DAVID and Reactome databases. For upregulated genes, biological processes were mainly associated with the regulation of transcription. Subsequently, a protein-protein interaction network was constructed for upregulated genes through STRING, in which DUSP6, FGFR3, EGFR, SOX2, NES, and PLP1 were further identified as hub genes via MCC and MNC methods. Expression profiles of hub genes and their association with survival were examined in TCGA GBM dataset using GEPIA2 platform. The expression levels of four hub genes were found to be increased in TCGA GBM samples. Of these, DUSP6 and SOX2 had prognostic value for patients with GBM. Molecular compounds targeting DUSP6 were searched through PubChem database. (E/Z)-BCI and BCI were found to be inhibitors of DUSP6. The molecular docking was performed using Autodock vina 1.02. The compounds showed strong binding capacities by forming various interactions with the ERK2 binding domain of DUSP6. Hence, the current study unravels the potential of (E/Z)-BCI and BCI compounds as possible anti-cancer molecules for GBM treatment.}, }
@article {pmid36121939, year = {2022}, author = {Stuart, M and Lesaja, S and Shih, JJ and Schultz, T and Manic, M and Krusienski, DJ}, title = {An Interpretable Deep Learning Model for Speech Activity Detection Using Electrocorticographic Signals.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {30}, number = {}, pages = {2783-2792}, doi = {10.1109/TNSRE.2022.3207624}, pmid = {36121939}, issn = {1558-0210}, mesh = {Brain ; *Brain-Computer Interfaces ; *Deep Learning ; Electrocorticography ; Humans ; Speech ; }, abstract = {Numerous state-of-the-art solutions for neural speech decoding and synthesis incorporate deep learning into the processing pipeline. These models are typically opaque and can require significant computational resources for training and execution. A deep learning architecture is presented that learns input bandpass filters that capture task-relevant spectral features directly from data. Incorporating such explainable feature extraction into the model furthers the goal of creating end-to-end architectures that enable automated subject-specific parameter tuning while yielding an interpretable result. The model is implemented using intracranial brain data collected during a speech task. Using raw, unprocessed timesamples, the model detects the presence of speech at every timesample in a causal manner, suitable for online application. Model performance is comparable or superior to existing approaches that require substantial signal preprocessing and the learned frequency bands were found to converge to ranges that are supported by previous studies.}, }
@article {pmid36120787, year = {2022}, author = {Li, XY and Bao, YF and Xie, JJ and Qian, SX and Gao, B and Xu, M and Dong, Y and Burgunder, JM and Wu, ZY}, title = {The Chinese Version of UHDRS in Huntington's Disease: Reliability and Validity Assessment.}, journal = {Journal of Huntington's disease}, volume = {11}, number = {4}, pages = {407-413}, doi = {10.3233/JHD-220542}, pmid = {36120787}, issn = {1879-6400}, abstract = {BACKGROUND: The Unified Huntington's Disease Rating Scale (UHDRS) is a universal scale assessing disease severity of Huntington's disease (HD). However, the English version cannot be widely used in China, and the reliability and validity of the Chinese UHDRS have not yet been confirmed.<br><br>OBJECTIVE: To test the reliability and validity of Chinse UHDRS in patients with HD.<br><br>METHODS: Between August 2013 and August 2021, 159 HD patients, 40 premanifest HD, and 64 healthy controls were consecutively recruited from two medical centers in China and assessed by Chinese UHDRS. Internal consistency and interrater reliability of the scale were examined. Intercorrelation was performed to analyze the convergent and divergent validity of the scale. A receiver operating characteristic analysis was conducted to explore the optimal cutoff point of each cognitive test.<br><br>RESULTS: High internal consistency was found in Chinese UHDRS, and its Cronbach's alpha values of the motor, cognitive, behavioral and functional subscales were 0.954, 0.826, 0.804, and 0.954, respectively. The interrater reliability of the total motor score was 0.960. The convergent and divergent validity revealed that motor, cognitive and functional subscales strongly related to each other except for Problem Behavior Assessment. Furthermore, we not only provided the normal level of each cognitive test in controls, but also gave the optimal cutoff points of cognitive tests between controls and HD patients.<br><br>CONCLUSION: We demonstrate for the first time that the translated version of UHDRS is reliable for assessing HD patients in China. This can promote the universal use of UHDRS in clinical practice.}, }
@article {pmid36120085, year = {2022}, author = {Triana-Guzman, N and Orjuela-Cañon, AD and Jutinico, AL and Mendoza-Montoya, O and Antelis, JM}, title = {Decoding EEG rhythms offline and online during motor imagery for standing and sitting based on a brain-computer interface.}, journal = {Frontiers in neuroinformatics}, volume = {16}, number = {}, pages = {961089}, pmid = {36120085}, issn = {1662-5196}, abstract = {Motor imagery (MI)-based brain-computer interface (BCI) systems have shown promising advances for lower limb motor rehabilitation. The purpose of this study was to develop an MI-based BCI for the actions of standing and sitting. Thirty-two healthy subjects participated in the study using 17 active EEG electrodes. We used a combination of the filter bank common spatial pattern (FBCSP) method and the regularized linear discriminant analysis (RLDA) technique for decoding EEG rhythms offline and online during motor imagery for standing and sitting. The offline analysis indicated the classification of motor imagery and idle state provided a mean accuracy of 88.51 ± 1.43% and 85.29 ± 1.83% for the sit-to-stand and stand-to-sit transitions, respectively. The mean accuracies of the sit-to-stand and stand-to-sit online experiments were 94.69 ± 1.29% and 96.56 ± 0.83%, respectively. From these results, we believe that the MI-based BCI may be useful to future brain-controlled standing systems.}, }
@article {pmid36119719, year = {2022}, author = {Mughal, NE and Khan, MJ and Khalil, K and Javed, K and Sajid, H and Naseer, N and Ghafoor, U and Hong, KS}, title = {EEG-fNIRS-based hybrid image construction and classification using CNN-LSTM.}, journal = {Frontiers in neurorobotics}, volume = {16}, number = {}, pages = {873239}, pmid = {36119719}, issn = {1662-5218}, abstract = {The constantly evolving human-machine interaction and advancement in sociotechnical systems have made it essential to analyze vital human factors such as mental workload, vigilance, fatigue, and stress by monitoring brain states for optimum performance and human safety. Similarly, brain signals have become paramount for rehabilitation and assistive purposes in fields such as brain-computer interface (BCI) and closed-loop neuromodulation for neurological disorders and motor disabilities. The complexity, non-stationary nature, and low signal-to-noise ratio of brain signals pose significant challenges for researchers to design robust and reliable BCI systems to accurately detect meaningful changes in brain states outside the laboratory environment. Different neuroimaging modalities are used in hybrid settings to enhance accuracy, increase control commands, and decrease the time required for brain activity detection. Functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) measure the hemodynamic and electrical activity of the brain with a good spatial and temporal resolution, respectively. However, in hybrid settings, where both modalities enhance the output performance of BCI, their data compatibility due to the huge discrepancy between their sampling rate and the number of channels remains a challenge for real-time BCI applications. Traditional methods, such as downsampling and channel selection, result in important information loss while making both modalities compatible. In this study, we present a novel recurrence plot (RP)-based time-distributed convolutional neural network and long short-term memory (CNN-LSTM) algorithm for the integrated classification of fNIRS EEG for hybrid BCI applications. The acquired brain signals are first projected into a non-linear dimension with RPs and fed into the CNN to extract essential features without performing any downsampling. Then, LSTM is used to learn the chronological features and time-dependence relation to detect brain activity. The average accuracies achieved with the proposed model were 78.44% for fNIRS, 86.24% for EEG, and 88.41% for hybrid EEG-fNIRS BCI. Moreover, the maximum accuracies achieved were 85.9, 88.1, and 92.4%, respectively. The results confirm the viability of the RP-based deep-learning algorithm for successful BCI systems.}, }
@article {pmid36118975, year = {2022}, author = {Hasslinger, J and Meregalli, M and Bölte, S}, title = {How standardized are "standard protocols"? Variations in protocol and performance evaluation for slow cortical potential neurofeedback: A systematic review.}, journal = {Frontiers in human neuroscience}, volume = {16}, number = {}, pages = {887504}, pmid = {36118975}, issn = {1662-5161}, abstract = {UNLABELLED: Neurofeedback (NF) aims to alter neural activity by enhancing self-regulation skills. Over the past decade NF has received considerable attention as a potential intervention option for many somatic and mental conditions and ADHD in particular. However, placebo-controlled trials have demonstrated insufficient superiority of NF compared to treatment as usual and sham conditions. It has been argued that the reason for limited NF effects may be attributable to participants' challenges to self-regulate the targeted neural activity. Still, there is support of NF efficacy when only considering so-called "standard protocols," such as Slow Cortical Potential NF training (SCP-NF). This PROSPERO registered systematic review following PRISMA criteria searched literature databases for studies applying SCP-NF protocols. Our review focus concerned the operationalization of self-regulatory success, and protocol-details that could influence the evaluation of self-regulation. Such details included; electrode placement, number of trials, length per trial, proportions of training modalities, handling of artifacts and skill-transfer into daily-life. We identified a total of 63 eligible reports published in the year 2000 or later. SCP-NF protocol-details varied considerably on most variables, except for electrode placement. However, due to the increased availability of commercial systems, there was a trend to more uniform protocol-details. Although, token-systems are popular in SCP-NF for ADHD, only half reported a performance-based component. Also, transfer exercises have become a staple part of SCP-NF. Furthermore, multiple operationalizations of regulatory success were identified, limiting comparability between studies, and perhaps usefulness of so-called transfer-exercises, which purpose is to facilitate the transfer of the self-regulatory skills into every-day life. While studies utilizing SCP as Brain-Computer-Interface mainly focused on the acquisition of successful self-regulation, clinically oriented studies often neglected this. Congruently, rates of successful regulators in clinical studies were mostly low (<50%). The relation between SCP self-regulation and behavior, and how symptoms in different disorders are affected, is complex and not fully understood. Future studies need to report self-regulation based on standardized measures, in order to facilitate both comparability and understanding of the effects on symptoms. When applied as treatment, future SCP-NF studies also need to put greater emphasis on the acquisition of self-regulation (before evaluating symptom outcomes).<br><br>https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021260087, Identifier: CRD42021260087.}, }
@article {pmid36112563, year = {2022}, author = {Zhang, W and Wang, Z and Wu, D}, title = {Multi-Source Decentralized Transfer for Privacy-Preserving BCIs.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {30}, number = {}, pages = {2710-2720}, doi = {10.1109/TNSRE.2022.3207494}, pmid = {36112563}, issn = {1558-0210}, mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagery, Psychotherapy ; Imagination ; Privacy ; }, abstract = {Transfer learning, which utilizes labeled source domains to facilitate the learning in a target model, is effective in alleviating high intra- and inter-subject variations in electroencephalogram (EEG) based brain-computer interfaces (BCIs). Existing transfer learning approaches usually use the source subjects' EEG data directly, leading to privacy concerns. This paper considers a decentralized privacy-preserving transfer learning scenario: there are multiple source subjects, whose data and computations are kept local, and only the parameters or predictions of their pre-trained models can be accessed for privacy-protection; then, how to perform effective cross-subject transfer for a new subject with unlabeled EEG trials? We propose an offline unsupervised multi-source decentralized transfer (MSDT) approach, which first generates a pre-trained model from each source subject, and then performs decentralized transfer using the source model parameters (in gray-box settings) or predictions (in black-box settings). Experiments on two datasets from two BCI paradigms, motor imagery and affective BCI, demonstrated that MSDT outperformed several existing approaches, which do not consider privacy-protection at all. In other words, MSDT achieved both high privacy-protection and better classification performance.}, }
@article {pmid36111058, year = {2022}, author = {Yang, J and Zhao, W and Liao, Y and Wu, S and Li, J and Jin, L and Liu, Q and Huang, F and Liang, L}, title = {Ocular surface disease index questionnaire as a sensitive test for primary screening of chronic ocular graft-versus-host disease.}, journal = {Annals of translational medicine}, volume = {10}, number = {16}, pages = {855}, pmid = {36111058}, issn = {2305-5839}, abstract = {BACKGROUND: After allogeneic hematopoietic stem cell transplantation (allo-HSCT), patients are followed up by transplant clinicians. Finding an effective primary screening method that transplant clinicians or patients can master is essential in the early referral of suspected chronic ocular graft-versus-host disease (coGVHD) to an ophthalmologist. This study investigated if the ocular surface disease index (OSDI) questionnaire could be used for coGVHD primary screening.<br><br>METHODS: This case-controlled, cross-sectional study enrolled 161 allo-HSCT patients. All participants completed an OSDI questionnaire and underwent a silt-lamp examination. Bulbar conjunctival injection (BCI) was assessed using torchlight, while tear volume was measured via the Schirmer test (ST). The receiver operating characteristic curve was used to evaluate the sensitivity, specificity, and cutoff values of OSDI, ST, and BCI grading. Performance comparisons of the 3 tests applied in isolation, parallel, and series were made.<br><br>RESULTS: There were 84 patients with and 77 patients without coGVHD. Compared to those without coGVHD, patients with coGVHD had significantly higher median values of OSDI, corneal fluorescein staining, conjunctival injection, conjunctival fibrosis, and meibum quality, but lower ST scores (All P values <0.001). The cutoff values for OSDI, ST, and BCI grade in the diagnosis of coGVHD were 19.4 points, 7 mm, and grade 0, respectively. The sensitivity and specificity of the tests based on the cutoff values were, respectively, 89.3% and 89.6% for OSDI, 91.7% and 59.7% for ST, and 78.6% and 70.1% for BCI. The area under the curve (AUC) value of OSDI was significantly higher than that of ST (0.931 vs. 0.826; P=0.010) and BCI grade (0.931 vs. 0.781; P<0.001). The AUC values of the combinations were lower than that of OSDI alone.<br><br>CONCLUSIONS: The OSDI questionnaire can be used as a simple screening test for coGVHD as demonstrated by its high sensitivity and specificity in the transplant clinic and patients' self-monitoring. An OSDI greater than 19.4 could be considered an ophthalmology referral criterion.}, }
@article {pmid36110124, year = {2022}, author = {Zhang, W and Yang, H and Gao, M and Zhang, H and Shi, L and Yu, X and Zhao, R and Song, J and Du, G}, title = {Edaravone Dexborneol Alleviates Cerebral Ischemic Injury via MKP-1-Mediated Inhibition of MAPKs and Activation of Nrf2.}, journal = {BioMed research international}, volume = {2022}, number = {}, pages = {4013707}, pmid = {36110124}, issn = {2314-6141}, mesh = {Animals ; *Brain Injuries ; *Dual Specificity Phosphatase 1/metabolism ; *Edaravone/pharmacology ; Extracellular Signal-Regulated MAP Kinases/metabolism ; JNK Mitogen-Activated Protein Kinases/metabolism ; Malondialdehyde ; NF-E2-Related Factor 2 ; *NF-kappa B ; Nitric Oxide ; Peroxidase ; Rats ; p38 Mitogen-Activated Protein Kinases/metabolism ; }, abstract = {The edaravone and dexborneol concentrated solution for injection (edaravone-dexborneol) is a medication used clinically to treat neurological impairment induced by ischemic stroke. This study was aimed at investigating the preventive effects and the underlying mechanisms of edaravone-dexborneol on cerebral ischemic injury. A rat four-vessel occlusion (4-VO) model was established, and the neuronal injury and consequent neurological impairment of rats was investigated. Brain tissue malondialdehyde (MDA), myeloperoxidase (MPO), and nitric oxide (NO) levels were determined. The levels of proteins in mitogen-activated protein kinases (MAPKs), nuclear factor erythroid 2-related factor 2 (Nrf2), and nuclear factor-κB (NF-κB) signaling pathways were determined by western immunoblotting. The function of mitogen-activated protein kinase phosphatase 1 (MKP-1) was investigated using both western blot and immunofluorescence methods, and the effect of the MKP-1 inhibitor, (2E)-2-benzylidene-3-(cyclohexylamino)-3H-inden-1-one (BCI), was investigated. The results indicated that edaravone-dexborneol alleviated neurological deficiency symptoms and decreased apoptosis and neuron damage in the hippocampal CA1 area of the ischemic rats. Edaravone-dexborneol increased the MKP-1 level; decreased the phosphorylation of extracellular signal-regulated kinase (ERK), c-Jun N-terminal kinase (JNK), and p38 mitogen-activated protein kinase (p38 MAPK); inhibited NF-κB p65 activation; and boosted Nrf2 activation, all of which were partially reversed by the MKP-1 inhibitor, BCI. The above results indicated that the upregulation of MKP-1 contributed to the protective effects of edaravone-dexborneol against ischemic brain injury. Our findings support the hypothesis that edaravone-dexborneol can alleviate cerebral ischemic injury via the upregulation of MKP-1, which inhibits MAPKs and activates Nrf2.}, }
@article {pmid36108535, year = {2023}, author = {El-Qawaqzeh, K and Anand, T and Richards, J and Hosseinpour, H and Nelson, A and Akl, MN and Obaid, O and Ditillo, M and Friese, R and Joseph, B}, title = {Predictors of Mortality in Blunt Cardiac Injury: A Nationwide Analysis.}, journal = {The Journal of surgical research}, volume = {281}, number = {}, pages = {22-32}, doi = {10.1016/j.jss.2022.07.047}, pmid = {36108535}, issn = {1095-8673}, mesh = {Adult ; Male ; Humans ; United States/epidemiology ; Middle Aged ; Aged ; Female ; Hemothorax ; *Thoracic Injuries/complications/diagnosis ; *Myocardial Contusions/complications/epidemiology ; *Wounds, Nonpenetrating/complications/diagnosis ; Injury Severity Score ; *Heart Injuries/etiology ; Retrospective Studies ; }, abstract = {INTRODUCTION: Blunt thoracic injury (BTI) is one of the most common causes of trauma admission in the United States and is uncommonly associated with cardiac injuries. Blunt cardiac injury (BCI) after blunt thoracic trauma is infrequent but carries a substantial risk of morbidity and sudden mortality. Our study aims to identify predictors of concomitant cardiac contusion among BTI patients and the predictors of mortality among patients presenting with BCI on a national level.<br><br>MATERIALS AND METHODS: We performed a 1-y (2017) analysis of the American College of Surgeons Trauma Quality Improvement Program. We included all adults (aged &#x2265; 18&#xa0;y) with the diagnosis of BTI. We excluded patients who were transferred, had a penetrating mechanism of injury, and who were dead on arrival. Our primary outcomes were the independent predictors of concomitant cardiac contusions among BTI patients and the predictors of mortality among BCI patients. Our secondary outcome measures were in-hospital complications, differences in injury patterns, and injury severity between the survivors and nonsurvivors of BCI.<br><br>RESULTS: A total of 125,696 patients with BTI were identified, of which 2368 patients had BCI. Mean age was 52&#xa0;&#xb1;&#xa0;20&#xa0;y, 67% were male, and median injury severity score was 14 [9-21]. The most common type of cardiac injury was cardiac contusion (43%). Age &#x2265; 65&#xa0;y, higher 4-h packed red blood cell requirements, motor vehicle collision mechanism of injury, and concomitant thoracic injuries (hemothorax, flail chest, lung contusion, sternal fracture, diaphragmatic injury, and thoracic aortic injuries) were independently associated with concomitant cardiac contusion among BTI patients (P value < 0.05). Age &#x2265; 65&#xa0;y, thoracic aortic injury, diaphragmatic injury, hemothorax, and a history of congestive heart failure were independently associated with mortality in BCI patients (P value < 0.05).<br><br>CONCLUSIONS: Predictors of concomitant cardiac contusion among BTI patients and mortality among BCI patients were identified. Guidelines on the management of BCI should incorporate these predictors for timely identification of high-risk patients.}, }
@article {pmid36108415, year = {2022}, author = {Shoeibi, A and Moridian, P and Khodatars, M and Ghassemi, N and Jafari, M and Alizadehsani, R and Kong, Y and Gorriz, JM and Ramírez, J and Khosravi, A and Nahavandi, S and Acharya, UR}, title = {An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works.}, journal = {Computers in biology and medicine}, volume = {149}, number = {}, pages = {106053}, doi = {10.1016/j.compbiomed.2022.106053}, pmid = {36108415}, issn = {1879-0534}, mesh = {Algorithms ; *Deep Learning ; Electroencephalography/methods ; *Epilepsy/diagnostic imaging ; Humans ; Neuroimaging ; Seizures/diagnostic imaging ; }, abstract = {Epilepsy is a disorder of the brain denoted by frequent seizures. The symptoms of seizure include confusion, abnormal staring, and rapid, sudden, and uncontrollable hand movements. Epileptic seizure detection methods involve neurological exams, blood tests, neuropsychological tests, and neuroimaging modalities. Among these, neuroimaging modalities have received considerable attention from specialist physicians. One method to facilitate the accurate and fast diagnosis of epileptic seizures is to employ computer-aided diagnosis systems (CADS) based on deep learning (DL) and neuroimaging modalities. This paper has studied a comprehensive overview of DL methods employed for epileptic seizures detection and prediction using neuroimaging modalities. First, DL-based CADS for epileptic seizures detection and prediction using neuroimaging modalities are discussed. Also, descriptions of various datasets, preprocessing algorithms, and DL models which have been used for epileptic seizures detection and prediction have been included. Then, research on rehabilitation tools has been presented, which contains brain-computer interface (BCI), cloud computing, internet of things (IoT), hardware implementation of DL techniques on field-programmable gate array (FPGA), etc. In the discussion section, a comparison has been carried out between research on epileptic seizure detection and prediction. The challenges in epileptic seizures detection and prediction using neuroimaging modalities and DL models have been described. In addition, possible directions for future works in this field, specifically for solving challenges in datasets, DL, rehabilitation, and hardware models, have been proposed. The final section is dedicated to the conclusion which summarizes the significant findings of the paper.}, }
@article {pmid36108075, year = {2022}, author = {Keller, L and Stelzle, D and Schmidt, V and Carabin, H and Reinhold, AK and Keller, C and Welte, TM and Richter, V and Amos, A and Boeckman, L and Harrison, W and Winkler, AS}, title = {Community-level prevalence of epilepsy and of neurocysticercosis among people with epilepsy in the Balaka district of Malawi: A cross-sectional study.}, journal = {PLoS neglected tropical diseases}, volume = {16}, number = {9}, pages = {e0010675}, pmid = {36108075}, issn = {1935-2735}, mesh = {Bayes Theorem ; Cross-Sectional Studies ; *Epilepsy/complications/epidemiology ; Humans ; Malawi/epidemiology ; *Neurocysticercosis/complications/diagnosis/epidemiology ; Prevalence ; Seizures/epidemiology ; }, abstract = {BACKGROUND: Epilepsy and neurocysticercosis (NCC) prevalence estimates in sub-Saharan Africa are still scarce but show important variation due to the population studied and different screening and diagnosis strategies used. The aims of this study were to estimate the prevalence of epileptic seizures and epilepsy in the sampled population, and the proportion of NCC among people with epilepsy (PWE) in a large cross-sectional study in a rural district of southern Malawi.<br><br>METHODS: We conducted a community-based door-to-door screening study for epileptic seizures in Balaka, Malawi between October and December 2012. Past epileptic seizures were reported through a 15-item questionnaire answered by at least one person per household generating five major criteria. People who screened positive were further examined by a neurologist to establish diagnosis. Patients diagnosed with epilepsy were examined and offered Taenia solium cyst antigen and antibody serological tests, and a CT scan for the diagnosis of NCC.<br><br>RESULTS: In total, screening information on 69,595 individuals was obtained for lifetime occurrence of epileptic seizures. 3,100 (4.5%) participants screened positive, of whom 1,913 (62%) could be followed-up and underwent further assessment. Lifetime prevalence was 3.0% (95% Bayesian credible interval [CI] 2.8 to 3.1%) and 1.2% (95%BCI 0.9 to 1.6%) for epileptic seizures and epilepsy, respectively. NCC prevalence among PWE was estimated to be 4.4% (95%BCI 0.8 to 8.5%). A diagnosis of epilepsy was ultimately reached for 455 participants.<br><br>CONCLUSION: The results of this large community-based study contribute to the evaluation and understanding of the burden of epilepsy in the population and of NCC among PWE in sub-Saharan Africa.}, }
@article {pmid36106568, year = {2023}, author = {Yu, X and Qi, X and Wei, L and Zhao, L and Deng, W and Guo, W and Wang, Q and Ma, X and Hu, X and Ni, P and Li, T}, title = {Fingolimod ameliorates schizophrenia-like cognitive impairments induced by phencyclidine in male rats.}, journal = {British journal of pharmacology}, volume = {180}, number = {2}, pages = {161-173}, doi = {10.1111/bph.15954}, pmid = {36106568}, issn = {1476-5381}, abstract = {BACKGROUND AND PURPOSE: Improvement of cognitive deficits in schizophrenia remains an unmet need owing to the lack of new therapies and drugs. Recent studies have reported that fingolimod, an immunomodulatory drug for treating multiple sclerosis, demonstrates anti-inflammatory and neuroprotective effects in several neurological disease models. This suggests its usefulness for ameliorating cognitive dysfunction in schizophrenia. Herein, we assessed the efficacy profile and mechanism of fingolimod in a rat model of phencyclidine (PCP)-induced schizophrenia.<br><br>EXPERIMENTAL APPROACH: Male Sprague-Dawley rats were treated with PCP for 14&#x2009;days. The therapeutic effect of fingolimod on cognitive function was assessed using the Morris water maze and fear conditioning tests. Hippocampal neurogenesis and the expression of astrocytes and microglia were evaluated using immunostaining. Cytokine expression was quantified using multiplexed flow cytometry. Brain-derived neurotrophic factor expression and phosphorylation of extracellular signal-regulated kinase were determined using western blot analysis.<br><br>KEY RESULTS: Fingolimod attenuated cognitive deficits and restored hippocampal neurogenesis in a dose-dependent manner in PCP-treated rats. Fingolimod treatment exerted anti-inflammatory effects by inhibiting microglial activation and IL-6 and IL-1&#x3b2; pro-inflammatory cytokine expression. The underlying mechanism involves the upregulation of brain-derived neurotrophic factor protein expression and activation of the ERK signalling pathway.<br><br>CONCLUSION AND IMPLICATIONS: This is the first preclinical assessment of the effects of fingolimod on cognitive function in a model for schizophrenia. Our results suggest the immune system plays an crucial role in cognitive alterations in schizophrenia and highlight the potential of immunomodulatory strategies to improve cognitive deficits in schizophrenia.}, }
@article {pmid36104988, year = {2022}, author = {Zhang, S and Wang, S and Liu, R and Dong, H and Zhang, X and Tai, X}, title = {A bibliometric analysis of research trends of artificial intelligence in the treatment of autistic spectrum disorders.}, journal = {Frontiers in psychiatry}, volume = {13}, number = {}, pages = {967074}, pmid = {36104988}, issn = {1664-0640}, abstract = {OBJECTIVE: Autism Spectrum Disorder (ASD) is a serious neurodevelopmental disorder that has become the leading cause of disability in children. Artificial intelligence (AI) is a potential solution to this issue. This study objectively analyzes the global research situation of AI in the treatment of ASD from 1995 to 2022, aiming to explore the global research status and frontier trends in this field.<br><br>METHODS: Web of Science (WoS) and PubMed databese were searched for Literature related to AI on ASD from 1995 to April 2022. CiteSpace, VOSviewer, Pajek and Scimago Graphica were used to analyze the collaboration between countries/institutions/authors, clusters and bursts of keywords, as well as analyses on references.<br><br>RESULTS: A total of 448 literature were included, the total number of literature has shown an increasing trend. The most productive country and institution were the USA, and Vanderbilt University. The authors with the greatest contributions were Warren, Zachary, Sakar, Nilanjan and Swanson, Amy. the most prolific and cited journal is Journal of Autism and Developmental Disorders, the highest cited and co-cited articles were Dautenhahn (Socially intelligent robots: dimensions of human-robot interaction 2007) and Scassellati B (Robots for Use in Autism Research 2012). "Artificial Intelligence", "Brain Computer Interface" and "Humanoid Robot" were the hotspots and frontier trends of AI on ASD.<br><br>CONCLUSION: The application of AI in the treatment of ASD has attracted the attention of researchers all over the world. The education, social function and joint attention of children with ASD are the most concerned issues for global researchers. Robots shows gratifying advantages in these issues and have become the most commonly used technology. Wearable devices and brain-computer interface (BCI) were emerging AI technologies in recent years, which is the direction of further exploration. Restoring social function in individuals with ASD is the ultimate aim and driving force of research in the future.}, }
@article {pmid36103781, year = {2022}, author = {Savya, SP and Li, F and Lam, S and Wellman, SM and Stieger, KC and Chen, K and Eles, JR and Kozai, TDY}, title = {In vivo spatiotemporal dynamics of astrocyte reactivity following neural electrode implantation.}, journal = {Biomaterials}, volume = {289}, number = {}, pages = {121784}, doi = {10.1016/j.biomaterials.2022.121784}, pmid = {36103781}, issn = {1878-5905}, support = {F99 NS124186/NS/NINDS NIH HHS/United States ; R01 NS094396/NS/NINDS NIH HHS/United States ; R01 NS105691/NS/NINDS NIH HHS/United States ; R01 NS115707/NS/NINDS NIH HHS/United States ; R21 NS108098/NS/NINDS NIH HHS/United States ; }, mesh = {Animals ; *Astrocytes/metabolism ; Electrodes, Implanted ; *Gliosis/metabolism ; Mice ; Microelectrodes ; Microglia ; Neuroglia ; }, abstract = {Brain computer interfaces (BCIs), including penetrating microelectrode arrays, enable both recording and stimulation of neural cells. However, device implantation inevitably causes injury to brain tissue and induces a foreign body response, leading to reduced recording performance and stimulation efficacy. Astrocytes in the healthy brain play multiple roles including regulating energy metabolism, homeostatic balance, transmission of neural signals, and neurovascular coupling. Following an insult to the brain, they are activated and gather around the site of injury. These reactive astrocytes have been regarded as one of the main contributors to the formation of a glial scar which affects the performance of microelectrode arrays. This study investigates the dynamics of astrocytes within the first 2 weeks after implantation of an intracortical microelectrode into the mouse brain using two-photon microscopy. From our observation astrocytes are highly dynamic during this period, exhibiting patterns of process extension, soma migration, morphological activation, and device encapsulation that are spatiotemporally distinct from other glial cells, such as microglia or oligodendrocyte precursor cells. This detailed characterization of astrocyte reactivity will help to better understand the tissue response to intracortical devices and lead to the development of more effective intervention strategies to improve the functional performance of neural interfacing technology.}, }
@article {pmid36099220, year = {2022}, author = {Hou, Y and Jia, S and Lun, X and Hao, Z and Shi, Y and Li, Y and Zeng, R and Lv, J}, title = {GCNs-Net: A Graph Convolutional Neural Network Approach for Decoding Time-Resolved EEG Motor Imagery Signals.}, journal = {IEEE transactions on neural networks and learning systems}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNNLS.2022.3202569}, pmid = {36099220}, issn = {2162-2388}, abstract = {Toward the development of effective and efficient brain-computer interface (BCI) systems, precise decoding of brain activity measured by an electroencephalogram (EEG) is highly demanded. Traditional works classify EEG signals without considering the topological relationship among electrodes. However, neuroscience research has increasingly emphasized network patterns of brain dynamics. Thus, the Euclidean structure of electrodes might not adequately reflect the interaction between signals. To fill the gap, a novel deep learning (DL) framework based on the graph convolutional neural networks (GCNs) is presented to enhance the decoding performance of raw EEG signals during different types of motor imagery (MI) tasks while cooperating with the functional topological relationship of electrodes. Based on the absolute Pearson's matrix of overall signals, the graph Laplacian of EEG electrodes is built up. The GCNs-Net constructed by graph convolutional layers learns the generalized features. The followed pooling layers reduce dimensionality, and the fully-connected (FC) softmax layer derives the final prediction. The introduced approach has been shown to converge for both personalized and groupwise predictions. It has achieved the highest averaged accuracy, 93.06% and 88.57% (PhysioNet dataset), 96.24% and 80.89% (high gamma dataset), at the subject and group level, respectively, compared with existing studies, which suggests adaptability and robustness to individual variability. Moreover, the performance is stably reproducible among repetitive experiments for cross-validation. The excellent performance of our method has shown that it is an important step toward better BCI approaches. To conclude, the GCNs-Net filters EEG signals based on the functional topological relationship, which manages to decode relevant features for brain MI. A DL library for EEG task classification including the code for this study is open source at https://github.com/SuperBruceJia/ EEG-DL for scientific research.}, }
@article {pmid36099009, year = {2022}, author = {Athanasiou, A and Mitsopoulos, K and Praftsiotis, A and Astaras, A and Antoniou, P and Pandria, N and Petronikolou, V and Kasimis, K and Lyssas, G and Terzopoulos, N and Fiska, V and Kartsidis, P and Savvidis, T and Arvanitidis, A and Chasapis, K and Moraitopoulos, A and Nizamis, K and Kalfas, A and Iakovidis, P and Apostolou, T and Magras, I and Bamidis, P}, title = {Neurorehabilitation Through Synergistic Man-Machine Interfaces Promoting Dormant Neuroplasticity in Spinal Cord Injury: Protocol for a Nonrandomized Controlled Trial.}, journal = {JMIR research protocols}, volume = {11}, number = {9}, pages = {e41152}, pmid = {36099009}, issn = {1929-0748}, abstract = {BACKGROUND: Spinal cord injury (SCI) constitutes a major sociomedical problem, impacting approximately 0.32-0.64 million people each year worldwide; particularly, it impacts young individuals, causing long-term, often irreversible disability. While effective rehabilitation of patients with SCI remains a significant challenge, novel neural engineering technologies have emerged to target and promote dormant neuroplasticity in the central nervous system.<br><br>OBJECTIVE: This study aims to develop, pilot test, and optimize a platform based on multiple immersive man-machine interfaces offering rich feedback, including (1) visual motor imagery training under high-density electroencephalographic recording, (2) mountable robotic arms controlled with a wireless brain-computer interface (BCI), (3) a body-machine interface (BMI) consisting of wearable robotics jacket and gloves in combination with a serious game (SG) application, and (4) an augmented reality module. The platform will be used to validate a self-paced neurorehabilitation intervention and to study cortical activity in chronic complete and incomplete SCI at the cervical spine.<br><br>METHODS: A 3-phase pilot study (clinical trial) was designed to evaluate the NeuroSuitUp platform, including patients with chronic cervical SCI with complete and incomplete injury aged over 14 years and age-/sex-matched healthy participants. Outcome measures include BCI control and performance in the BMI-SG module, as well as improvement of functional independence, while also monitoring neuropsychological parameters such as kinesthetic imagery, motivation, self-esteem, depression and anxiety, mental effort, discomfort, and perception of robotics. Participant enrollment into the main clinical trial is estimated to begin in January 2023 and end by December 2023.<br><br>RESULTS: A preliminary analysis of collected data during pilot testing of BMI-SG by healthy participants showed that the platform was easy to use, caused no discomfort, and the robotics were perceived positively by the participants. Analysis of results from the main clinical trial will begin as recruitment progresses and findings from the complete analysis of results are expected in early 2024.<br><br>CONCLUSIONS: Chronic SCI is characterized by irreversible disability impacting functional independence. NeuroSuitUp could provide a valuable complementary platform for training in immersive rehabilitation methods to promote dormant neural plasticity.<br><br>TRIAL REGISTRATION: ClinicalTrials.gov NCT05465486; https://clinicaltrials.gov/ct2/show/NCT05465486.<br><br>PRR1-10.2196/41152.}, }
@article {pmid36097316, year = {2022}, author = {Taniguchi, A and Yunoki, T and Oiwake, T and Hayashi, A}, title = {Association between tear meniscus dimensions and higher-order aberrations in patients with surgically treated lacrimal passage obstruction.}, journal = {International ophthalmology}, volume = {}, number = {}, pages = {}, pmid = {36097316}, issn = {1573-2630}, abstract = {PURPOSE: To analyze the relationship between tear meniscus dimensions and higher-order aberrations (HOAs) in patients with lacrimal passage obstruction using anterior segment optical coherence tomography (AS-OCT).<br><br>METHODS: This study was a retrospective observational study of 71 eyes of 49 patients with lacrimal passage obstruction. These patients received sheath-guided dacryoendoscopic probing and bicanalicular intubation (SG-BCI) at Toyama University Hospital between August 2020 and October 2021. Using AS-OCT, tear meniscus height (TMH), tear meniscus area (TMA), and total corneal HOAs values were measured before and after surgery.<br><br>RESULTS: Surgical success was achieved in 69 eyes (97.1%). At the final observation, 62 eyes showed lacrimal patency (89.8%). The preoperative TMH, TMA, and HOAs values were 1.55&#x2009;&#xb1;&#x2009;0.96&#xa0;mm, 0.11&#x2009;&#xb1;&#x2009;0.14&#xa0;mm[2], and 0.37&#x2009;&#xb1;&#x2009;0.27&#xa0;&#xb5;m, respectively, and the final postoperative TMH, TMA, and HOAs values were 0.97&#x2009;&#xb1;&#x2009;0.74&#xa0;mm (p&#x2009;<&#x2009;0.0001), 0.06&#x2009;&#xb1;&#x2009;0.11&#xa0;mm[2] (p&#x2009;=&#x2009;0.02), and 0.29&#x2009;&#xb1;&#x2009;0.16&#xa0;&#xb5;m (p&#x2009;=&#x2009;0.001), respectively. The results showed a significant improvement. The changes in HOAs before and after surgery were positively correlated with the changes in TMH (r&#x2009;=&#x2009;0.3476, p&#x2009;=&#x2009;0.0241) and TMA (r&#x2009;=&#x2009;0.3653, p&#x2009;=&#x2009;0.0174).<br><br>CONCLUSION: SG-BCI for lacrimal passage obstruction resulted in a significant decrease in measured HOAs. The decrease in HOAs was correlated with decreases in tear meniscus dimensions.}, }
@article {pmid36092985, year = {2022}, author = {Hou, S and Fan, D and Wang, Q}, title = {Regulating absence seizures by tri-phase delay stimulation applied to globus pallidus internal.}, journal = {Applied mathematics and mechanics}, volume = {43}, number = {9}, pages = {1399-1414}, pmid = {36092985}, issn = {1573-2754}, abstract = {In this paper, a reduced globus pallidus internal (GPI)-corticothalamic (GCT) model is developed, and a tri-phase delay stimulation (TPDS) with sequentially applying three pulses on the GPI representing the inputs from the striatal D 1 neurons, subthalamic nucleus (STN), and globus pallidus external (GPE), respectively, is proposed. The GPI is evidenced to control absence seizures characterized by 2 Hz-4 Hz spike and wave discharge (SWD). Hence, based on the basal ganglia-thalamocortical (BGCT) model, we firstly explore the triple effects of D l-GPI, GPE-GPI, and STN-GPI pathways on seizure patterns. Then, using the GCT model, we apply the TPDS on the GPI to potentially investigate the alternative and improved approach if these pathways to the GPI are blocked. The results show that the striatum D 1, GPE, and STN can indeed jointly and significantly affect seizure patterns. In particular, the TPDS can effectively reproduce the seizure pattern if the D 1-GPI, GPE-GPI, and STN-GPI pathways are cut off. In addition, the seizure abatement can be obtained by well tuning the TPDS stimulation parameters. This implies that the TPDS can play the surrogate role similar to the modulation of basal ganglia, which hopefully can be helpful for the development of the brain-computer interface in the clinical application of epilepsy.}, }
@article {pmid36092645, year = {2022}, author = {Pereira, JA and Ray, A and Rana, M and Silva, C and Salinas, C and Zamorano, F and Irani, M and Opazo, P and Sitaram, R and Ruiz, S}, title = {A real-time fMRI neurofeedback system for the clinical alleviation of depression with a subject-independent classification of brain states: A proof of principle study.}, journal = {Frontiers in human neuroscience}, volume = {16}, number = {}, pages = {933559}, pmid = {36092645}, issn = {1662-5161}, abstract = {Most clinical neurofeedback studies based on functional magnetic resonance imaging use the patient's own neural activity as feedback. The objective of this study was to create a subject-independent brain state classifier as part of a real-time fMRI neurofeedback (rt-fMRI NF) system that can guide patients with depression in achieving a healthy brain state, and then to examine subsequent clinical changes. In a first step, a brain classifier based on a support vector machine (SVM) was trained from the neural information of happy autobiographical imagery and motor imagery blocks received from a healthy female participant during an MRI session. In the second step, 7 right-handed female patients with mild or moderate depressive symptoms were trained to match their own neural activity with the neural activity corresponding to the "happiness emotional brain state" of the healthy participant. The training (4 training sessions over 2 weeks) was carried out using the rt-fMRI NF system guided by the brain-state classifier we had created. Thus, the informative voxels previously obtained in the first step, using SVM classification and Effect Mapping, were used to classify the Blood-Oxygen-Level Dependent (BOLD) activity of the patients and converted into real-time visual feedback during the neurofeedback training runs. Improvements in the classifier accuracy toward the end of the training were observed in all the patients [Session 4-1 Median = 6.563%; Range = 4.10-27.34; Wilcoxon Test (0), 2-tailed p = 0.031]. Clinical improvement also was observed in a blind standardized clinical evaluation [HDRS CE2-1 Median = 7; Range 2 to 15; Wilcoxon Test (0), 2-tailed p = 0.016], and in self-report assessments [BDI-II CE2-1 Median = 8; Range 1-15; Wilcoxon Test (0), 2-tailed p = 0.031]. In addition, the clinical improvement was still present 10 days after the intervention [BDI-II CE3-2_Median = 0; Range -1 to 2; Wilcoxon Test (0), 2-tailed p = 0.50/ HDRS CE3-2 Median = 0; Range -1 to 2; Wilcoxon Test (0), 2-tailed p = 0.625]. Although the number of participants needs to be increased and a control group included to confirm these findings, the results suggest a novel option for neural modulation and clinical alleviation in depression using noninvasive stimulation technologies.}, }
@article {pmid36090260, year = {2022}, author = {Wang, J and Zhang, J and Yu, H and Shi, B}, title = {Editorial: Human machine interface-based neuromodulation solutions for neurorehabilitation.}, journal = {Frontiers in neuroscience}, volume = {16}, number = {}, pages = {987455}, doi = {10.3389/fnins.2022.987455}, pmid = {36090260}, issn = {1662-4548}, }
@article {pmid36090185, year = {2022}, author = {Girdler, B and Caldbeck, W and Bae, J}, title = {Neural Decoders Using Reinforcement Learning in Brain Machine Interfaces: A Technical Review.}, journal = {Frontiers in systems neuroscience}, volume = {16}, number = {}, pages = {836778}, pmid = {36090185}, issn = {1662-5137}, abstract = {Creating flexible and robust brain machine interfaces (BMIs) is currently a popular topic of research that has been explored for decades in medicine, engineering, commercial, and machine-learning communities. In particular, the use of techniques using reinforcement learning (RL) has demonstrated impressive results but is under-represented in the BMI community. To shine more light on this promising relationship, this article aims to provide an exhaustive review of RL's applications to BMIs. Our primary focus in this review is to provide a technical summary of various algorithms used in RL-based BMIs to decode neural intention, without emphasizing preprocessing techniques on the neural signals and reward modeling for RL. We first organize the literature based on the type of RL methods used for neural decoding, and then each algorithm's learning strategy is explained along with its application in BMIs. A comparative analysis highlighting the similarities and uniqueness among neural decoders is provided. Finally, we end this review with a discussion about the current stage of RLBMIs including their limitations and promising directions for future research.}, }
@article {pmid36089460, year = {2022}, author = {Shah, S and Shaing, C and Khatib, J and Lodrigues, W and Dreadin-Pulliam, J and Anderson, BB and Unni, N and Farr, D and Li, HC and Sadeghi, N and Syed, S}, title = {The Utility of Breast Cancer Index (BCI) Over Clinical Prognostic Tools for Predicting the Need for Extended Endocrine Therapy: A Safety Net Hospital Experience.}, journal = {Clinical breast cancer}, volume = {22}, number = {8}, pages = {823-827}, doi = {10.1016/j.clbc.2022.08.003}, pmid = {36089460}, issn = {1938-0666}, mesh = {Humans ; Female ; Middle Aged ; Prognosis ; Tamoxifen/therapeutic use ; *Breast Neoplasms/diagnosis/drug therapy/genetics ; Antineoplastic Agents, Hormonal/adverse effects ; Retrospective Studies ; Receptors, Estrogen ; Safety-net Providers ; *Brain-Computer Interfaces ; Neoplasm Recurrence, Local/pathology ; Recurrence ; }, abstract = {INTRODUCTION: Extended endocrine therapy (EET) benefits select patients with early-stage hormone-receptor positive (HR+) breast cancer (BC) but also incurs side effects and cost. The Clinical Treatment Score at Five Years (CTS5) is a free tool that estimates risks of late relapse in estrogen-receptor positive (ER+) BC using clinicopathologic factors. The Breast Cancer Index (BCI) incorporates 2 genomic assays to estimate late relapse risk and likelihood of benefit from EET. This retrospective study assesses the utility of BCI in selecting EET candidates in a safety net hospital.<br><br>MATERIALS AND METHODS: We performed a retrospective chart review on 69 women with early-stage HR+, HER2- BC diagnosed at our institution from December 2009 to February 2016 on whom BCI was submitted. The CTS5 score was also calculated to assess clinical risk of late relapse.<br><br>RESULTS: Median age was 53 years. All patients included in our analysis had early ER+ HER2-negative BC. Roughly half of the patients (55%) were postmenopausal and 61% were of Hispanic origin. A total of 34 patients (49%) were deemed high-risk (>5%) for late relapse by CTS5, compared to 42 (61%) by BCI. BCI identified 31 (45%) patients that would benefit from EET and of those, 74%% were advised EET. 16 (47%) clinical high-risk patients were advised against EET due to low benefit predicted by BCI. In the clinical low risk group, 9 (26%) were recommended EET based on high benefit predicted by BCI.<br><br>CONCLUSION: BCI is reasonable to consider in early-stage HR+ BC and offered clinically relevant information over clinical pathologic information alone.}, }
@article {pmid36086771, year = {2022}, author = {Kim, MY and Park, JY and Leigh, JH and Kim, YJ and Nam, HS and Seo, HG and Oh, BM and Kim, S and Bang, MS}, title = {Exploring user perspectives on a robotic arm with brain-machine interface: A qualitative focus group study.}, journal = {Medicine}, volume = {101}, number = {36}, pages = {e30508}, doi = {10.1097/MD.0000000000030508}, pmid = {36086771}, issn = {1536-5964}, mesh = {Activities of Daily Living ; *Brain-Computer Interfaces ; Focus Groups ; Humans ; Quadriplegia ; *Robotic Surgical Procedures ; }, abstract = {Brain-machine Interface (BMI) is a system that translates neuronal data into an output variable to control external devices such as a robotic arm. A robotic arm can be used as an assistive living device for individuals with tetraplegia. To reflect users' needs in the development process of the BMI robotic arm, our team followed an interactive approach to system development, human-centered design, and Human Activity Assistive Technology model. This study aims to explore the perspectives of people with tetraplegia about activities they want to participate in, their opinions, and the usability of the BMI robotic arm. Eight people with tetraplegia participated in a focus group interview in a semistructured interview format. A general inductive analysis method was used to analyze the qualitative data. The 3 overarching themes that emerged from this analysis were: 1) activities, 2) acceptance, and 3) usability. Activities that the users wanted to do using the robotic arm were categorized into the following 5 activity domains: activities of daily living (ADL), instrumental ADL, health management, education, and leisure. Participants provided their opinions on the needs and acceptance of the BMI technology. Participants answered usability and expected standards of the BMI robotic arm within 7 categories such as accuracy, setup, cost, etc. Participants with tetraplegia have a strong interest in the robotic arm and BMI technology to restore their mobility and independence. Creating BMI features appropriate to users' needs, such as safety and high accuracy, will be the key to acceptance. These findings from the perspectives of potential users should be taken into account when developing the BMI robotic arm.}, }
@article {pmid36086657, year = {2022}, author = {Koorathota, S and Khan, Z and Lapborisuth, P and Sajda, P}, title = {Multimodal Neurophysiological Transformer for Emotion Recognition.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2022}, number = {}, pages = {3563-3567}, doi = {10.1109/EMBC48229.2022.9871421}, pmid = {36086657}, issn = {2694-0604}, mesh = {*Arousal/physiology ; Attention ; *Emotions/physiology ; Endoscopy ; Neurophysiology ; }, abstract = {Understanding neural function often requires multiple modalities of data, including electrophysiogical data, imaging techniques, and demographic surveys. In this paper, we introduce a novel neurophysiological model to tackle major challenges in modeling multimodal data. First, we avoid non-alignment issues between raw signals and extracted, frequency-domain features by addressing the issue of variable sampling rates. Second, we encode modalities through "cross-attention" with other modalities. Lastly, we utilize properties of our parent transformer architecture to model long-range dependencies between segments across modalities and assess intermediary weights to better understand how source signals affect prediction. We apply our Multimodal Neurophysiological Transformer (MNT) to predict valence and arousal in an existing open-source dataset. Experiments on non-aligned multimodal time-series show that our model performs similarly and, in some cases, outperforms existing methods in classification tasks. In addition, qualitative analysis suggests that MNT is able to model neural influences on autonomic activity in predicting arousal. Our architecture has the potential to be fine-tuned to a variety of downstream tasks, including for BCI systems.}, }
@article {pmid36086641, year = {2022}, author = {Lee, KW and Lee, DH and Kim, SJ and Lee, SW}, title = {Decoding Neural Correlation of Language-Specific Imagined Speech using EEG Signals.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2022}, number = {}, pages = {1977-1980}, doi = {10.1109/EMBC48229.2022.9871721}, pmid = {36086641}, issn = {2694-0604}, mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Humans ; Language ; Speech ; Speech Disorders ; *Speech Perception ; }, abstract = {Speech impairments due to cerebral lesions and degenerative disorders can be devastating. For humans with severe speech deficits, imagined speech in the brain-computer interface has been a promising hope for reconstructing the neural signals of speech production. However, studies in the EEG-based imagined speech domain still have some limitations due to high variability in spatial and temporal information and low signal-to-noise ratio. In this paper, we investigated the neural signals for two groups of native speakers with two tasks with different languages, English and Chinese. Our assumption was that English, a non-tonal and phonogram-based language, would have spectral differences in neural computation compared to Chinese, a tonal and ideogram-based language. The results showed the significant difference in the relative power spectral density between English and Chinese in specific frequency band groups. Also, the spatial evaluation of Chinese native speakers in the theta band was distinctive during the imagination task. Hence, this paper would suggest the key spectral and spatial information of word imagination with specialized language while decoding the neural signals of speech. Clinical Relevance- Imagined speech-related studies lead to the development of assistive communication technology especially for patients with speech disorders such as aphasia due to brain damage. This study suggests significant spectral features by analyzing cross-language differences of EEG-based imagined speech using two widely used languages.}, }
@article {pmid36086599, year = {2022}, author = {Liao, G and Wang, S and Wei, Z and Liu, B and Okubo, R and Hernandez, ME}, title = {Online classifier of AMICA model to evaluate state anxiety while standing in virtual reality.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2022}, number = {}, pages = {381-384}, doi = {10.1109/EMBC48229.2022.9871843}, pmid = {36086599}, issn = {2694-0604}, mesh = {Anxiety/diagnosis ; Anxiety Disorders ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; *Virtual Reality ; }, abstract = {Changes in emotional state, such as anxiety, have a significant impact on behavior and mental health. However, the detection of anxiety in individuals requires trained specialists to administer specialized assessments, which often take a significant amount of time and resources. Thus, there is a significant need for objective and real-time anxiety detection methods to aid clinical practice. Recent advances in Adaptive Mixture Independent Component Analysis (AMICA) have demonstrated the ability to detect changes in emotional states using electroencephalographic (EEG) data. However, given that several hours may be need to identify the different models, alternative methods must be sought for future brain-computer-interface applications. This study examines the feasibility of a machine learning classifier using frequency domain features of EEG data to classify individual 500 ms samples of EEG data into different cortical states, as established by multi-model AMICA labels. Using a random forest classifier with 12 input features from EEG data to predict cortical states yielded a 75% accuracy in binary classification. Based on these findings, this work may provide a foundation for real-time anxiety state detection and classification.}, }
@article {pmid36086535, year = {2022}, author = {Musellim, S and Han, DK and Jeong, JH and Lee, SW}, title = {Prototype-based Domain Generalization Framework for Subject-Independent Brain-Computer Interfaces.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference}, volume = {2022}, number = {}, pages = {711-714}, doi = {10.1109/EMBC48229.2022.9871434}, pmid = {36086535}, issn = {2694-0604}, mesh = {*Brain-Computer Interfaces ; Calibration ; Electroencephalography/methods ; Humans ; *Neurofeedback ; }, abstract = {Brain-computer interface (BCI) is challenging to use in practice due to the inter/intra-subject variability of electroencephalography (EEG). The BCI system, in general, necessitates a calibration technique to obtain subject/session-specific data in order to tune the model each time the system is utilized. This issue is acknowledged as a key hindrance to BCI, and a new strategy based on domain generalization has recently evolved to address it. In light of this, we've concentrated on developing an EEG classification framework that can be applied directly to data from unknown domains (i.e. subjects), using only data acquired from separate subjects previously. For this purpose, in this paper, we proposed a framework that employs the open-set recognition technique as an auxiliary task to learn subject-specific style features from the source dataset while helping the shared feature extractor with mapping the features of the unseen target dataset as a new unseen domain. Our aim is to impose cross-instance style in-variance in the same domain and reduce the open space risk on the potential unseen subject in order to improve the generalization ability of the shared feature extractor. Our experiments showed that using the domain information as an auxiliary network increases the generalizatio