@article {pmid40162168,
year = {2025},
author = {Yang, H and Jiang, L},
title = {Regulating neural data processing in the age of BCIs: Ethical concerns and legal approaches.},
journal = {Digital health},
volume = {11},
number = {},
pages = {20552076251326123},
doi = {10.1177/20552076251326123},
pmid = {40162168},
issn = {2055-2076},
abstract = {Brain-computer interfaces (BCIs) have seen increasingly fast growth under the help from AI, algorithms, and cloud computing. While providing great benefits for both medical and educational purposes, BCIs involve processing of neural data which are uniquely sensitive due to their most intimate nature, posing unique risks and ethical concerns especially related to privacy and safe control of our neural data. In furtherance of human right protection such as mental privacy, data laws provide more detailed and enforceable rules for processing neural data which may balance the tension between privacy protection and need of the public for wellness promotion and scientific progress through data sharing. This article notes that most of the current data laws like GDPR have not covered neural data clearly, incapable of providing full protection in response to its specialty. The new legislative reforms in the U.S. states of Colorado and California made pioneering advances to incorporate neural data into data privacy laws. Yet regulatory gaps remain as such reforms have not provided special additional rules for neural data processing. Potential problems such as static consent, vague research exceptions, and loopholes in regulating non-personal neural data need to be further addressed. We recommend relevant improved measures taken through amending data laws or making special data acts.},
}

@article {pmid40161643,
year = {2025},
author = {Haro, S and Beauchene, C and Quatieri, TF and Smalt, CJ},
title = {A Brain-Computer Interface for Improving Auditory Attention in Multi-Talker Environments.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.03.13.641661},
pmid = {40161643},
issn = {2692-8205},
abstract = {OBJECTIVE: There is significant research in accurately determining the focus of a listener's attention in a multi-talker environment using auditory attention decoding (AAD) algorithms. These algorithms rely on neural signals to identify the intended speaker, assuming that these signals consistently reflect the listener's focus. However, some listeners struggle with this competing talkers task, leading to suboptimal tracking of the desired speaker due to potential interference from distractors. The goal of this study was to enhance a listener's attention to the target speaker in real time and investigate the underlying neural bases of this improvement.

APPROACH: This paper describes a closed-loop neurofeedback system that decodes the auditory attention of the listener in real time, utilizing data from a non-invasive, wet electroencephalography (EEG) brain-computer interface (BCI). Fluctuations in the listener's real-time attention decoding accuracy was used to provide acoustic feedback. As accuracy improved, the ignored talker in the two-talker listening scenario was attenuated; making the desired talker easier to attend to due to the improved attended talker signal-to-noise ratio (SNR). A one-hour session was divided into a 10-minute decoder training phase, with the rest of the session allocated to observing changes in neural decoding.

RESULTS: In this study, we found evidence of suppression of (i.e., reduction in) neural tracking of the unattended talker when comparing the first and second half of the neurofeedback session (p = 0.012). We did not find a statistically significant increase in the neural tracking of the attended talker.

SIGNIFICANCE: These results establish a single session performance benchmark for a time-invariant, non-adaptive attended talker linear decoder utilized to extract attention from a listener integrated within a closed-loop neurofeedback system. This research lays the engineering and scientific foundation for prospective multi-session clinical trials of an auditory attention training paradigm.},
}

@article {pmid40161457,
year = {2025},
author = {Ren, X and Wang, Y and Li, X and Wang, X and Liu, Z and Yang, J and Wang, L and Zheng, C},
title = {Attenuated heterogeneity of hippocampal neuron subsets in response to novelty induced by amyloid-β.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {56},
doi = {10.1007/s11571-025-10237-x},
pmid = {40161457},
issn = {1871-4080},
abstract = {Alzheimer's disease (AD) patients exhibited episodic memory impairments including location-object recognition in a spatial environment, which was also presented in animal models with amyloid-β (Aβ) accumulation. A potential cellular mechanism was the unstable representation of spatial information and lack of discrimination ability of novel stimulus in the hippocampal place cells. However, how the firing characteristics of different hippocampal subsets responding to diverse spatial information were interrupted by Aβ accumulation remains unclear. In this study, we observed impaired novel object-location recognition in Aβ-treated Long-Evans rats, with larger receptive fields of place cells in hippocampal CA1, compared with those in the saline-treated group. We identified two subsets of place cells coding object information (ObjCell) and global environment (EnvCell) during the task, with firing heterogeneity in response to introduced novel information. ObjCells displayed a dynamic representation responding to the introduction of novel information, while EnvCells exhibited a stable representation to support the recognition of the familiar environment. However, the dynamic firing patterns of these two subsets of cells were disrupted to present attenuated heterogeneity under Aβ accumulation. The impaired spatial representation novelty information could be due to the disturbed gamma modulation of neural activities. Taken together, these findings provide new evidence for novelty recognition impairments of AD rats with spatial representation dysfunctions of hippocampal subsets.},
}

@article {pmid40159374,
year = {2025},
author = {Phang, CR and Hirata, A},
title = {Shared autonomy between human electroencephalography and TD3 deep reinforcement learning: A multi-agent copilot approach.},
journal = {Annals of the New York Academy of Sciences},
volume = {},
number = {},
pages = {},
doi = {10.1111/nyas.15322},
pmid = {40159374},
issn = {1749-6632},
support = {KAKENHI 21H04956//Japan Society for the Promotion of Science/ ; },
abstract = {Deep reinforcement learning (RL) algorithms enable the development of fully autonomous agents that can interact with the environment. Brain-computer interface (BCI) systems decipher human implicit brain signals regardless of the explicit environment. We proposed a novel integration technique between deep RL and BCI to improve beneficial human interventions in autonomous systems and the performance in decoding brain activities by considering environmental factors. Shared autonomy was allowed between the action command decoded from the electroencephalography (EEG) of the human agent and the action generated from the twin delayed DDPG (TD3) agent for a given complex environment. Our proposed copilot control scheme with a full blocker (Co-FB) significantly outperformed the individual EEG (EEG-NB) or TD3 control. The Co-FB model achieved a higher target-approaching score, lower failure rate, and lower human workload than the EEG-NB model. We also proposed a disparity d $d$ -index to evaluate the effect of contradicting agent decisions on the control accuracy and authority of the copilot model. We observed that shifting control authority to the TD3 agent improved performance when BCI decoding was not optimal. These findings indicate that the copilot system can effectively handle complex environments and that BCI performance can be improved by considering environmental factors.},
}

@article {pmid40153908,
year = {2025},
author = {Liu, J and Yang, X and Musmar, B and Hasan, DM},
title = {Trans-arterial approach for neural recording and stimulation: Present and future.},
journal = {Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia},
volume = {135},
number = {},
pages = {111180},
doi = {10.1016/j.jocn.2025.111180},
pmid = {40153908},
issn = {1532-2653},
abstract = {Neural recording and stimulation are fundamental techniques used for brain computer interfaces (BCIs). BCIs have significant potential for use in a range of brain disorders. However, for most BCIs, electrode implantation requires invasive craniotomy procedures, which have a risk of infection, hematoma, and immune responses. Such drawbacks may limit the extensive application of BCIs. There has been a rapid increase in the development of endovascular technologies and devices. Indeed, in a clinical trial, stent electrodes have been endovascularly implanted via a venous approach and provided an effective endovascular BCI to help disabled patients. Several authors have reviewed the use of endovascular recordings or endovascular BCIs. However, there is limited information on the use of trans-arterial BCIs. Herein, we reviewed the literature on the use of trans-arterial neural recording and stimulation for BCIs, and discuss their potential in terms of anatomical features, device innovations, and clinical applications. Although the use of trans-arterial recording and stimulation in the brain remains challenging, we believe it has high potential for both scientists and physicians.},
}

@article {pmid40152578,
year = {2025},
author = {Li, K and Cui, Y},
title = {The Emerging Role of Astrocytes in Learning and Memory Recall.},
journal = {Journal of integrative neuroscience},
volume = {24},
number = {3},
pages = {38721},
doi = {10.31083/JIN38721},
pmid = {40152578},
issn = {0219-6352},
support = {2022ZD0211700//STI2030-Major projects/ ; 32371057//National Natural Science Foundation of China/ ; },
}

@article {pmid40151645,
year = {2025},
author = {Iwama, S and Ueno, T and Fujimaki, T and Ushiba, J},
title = {Enhanced human sensorimotor integration via self-modulation of the somatosensory activity.},
journal = {iScience},
volume = {28},
number = {4},
pages = {112145},
pmid = {40151645},
issn = {2589-0042},
abstract = {Motor performance improvement through self-modulation of brain activity has been demonstrated through neurofeedback. However, the sensorimotor plasticity induced through the training remains unclear. Here, we combined individually tailored closed-loop neurofeedback, neurophysiology, and behavioral assessment to characterize how the training can modulate the somatosensory system and improve performance. The real-time neurofeedback of human electroencephalogram (EEG) signals enhanced participants' self-modulation ability of intrinsic neural oscillations in the primary somatosensory cortex (S1) within 30 min. Further, the short-term reorganization in S1 was corroborated by the post-training changes in somatosensory evoked potential (SEP) amplitude of the early component from S1. Meanwhile those derived from peripheral and spinal sensory fibers were maintained (N9 and N13 components), suggesting that the training manipulated S1 activities. Behavioral evaluation demonstrated improved performance during keyboard touch-typing indexed by resolved speed-accuracy trade-off. Collectively, our results provide evidence that neurofeedback training induces functional reorganization of S1 and sensorimotor function.},
}

@article {pmid40149743,
year = {2025},
author = {Zheng, J and Li, Y and Chen, L and Wang, F and Gu, B and Sun, Q and Gao, X and Zhou, F},
title = {Effects of Packet Loss on Neural Decoding Effectiveness in Wireless Transmission.},
journal = {Brain sciences},
volume = {15},
number = {3},
pages = {},
doi = {10.3390/brainsci15030221},
pmid = {40149743},
issn = {2076-3425},
support = {2021ZD0200405//National Key R&D Program of China/ ; 2024M752811//China Postdoctoral Science Foundation under Grant Number/ ; 202204A09//Key Agricultural and Social Development Projects of Hangzhou/ ; },
abstract = {BACKGROUND: In brain-computer interfaces, neural decoding plays a central role in translating neural signals into meaningful physical actions. These signals are transmitted to processors for decoding via wired or wireless channels; however, they are often subject to data loss, commonly referred to as "packet loss". Despite their importance, the effects of different types and degrees of packet loss on neural decoding have not yet been comprehensively studied. Understanding these effects is critical for advancing neural signal processing.

METHODS: This study addresses this gap by constructing four distinct packet loss models that simulate the congestion, distribution, and burst loss scenarios. Using macaque superior arm movement decoding experiments, we analyzed the effects of the aforementioned packet loss types on decoding performance across six parameters (position, velocity, and acceleration in the x and y dimensions). The performance was assessed using the R2 metric and statistical comparisons across different loss scenarios.

RESULTS: Our results indicate that sudden, consecutive packet loss significantly degraded decoding performance. For the same packet loss probability, burst loss led to the largest decrease in the R2 value. Notably, when the packet loss rate reached 10%, the decoding performance for acceleration dropped to 73% of the original R2 value. On the other hand, when the packet loss rate was within 2%, the neural signal decoding results across all packet loss models remained largely unaffected. However, as the packet loss rate increased, the impact became more pronounced. These findings highlight the varying degrees to which different packet loss models affect decoding outcomes.

CONCLUSIONS: This study quantitatively evaluated the relationship between packet loss and neural decoding outcomes, highlighting the differential effects of loss patterns on decoding parameters, and it proposed some methods and devices to solve the problem of packet loss. These findings offer valuable insights for the development of resilient neural signal acquisition and processing systems capable of mitigating the impact of packet loss.},
}

@article {pmid40149575,
year = {2025},
author = {Calderone, A and Manuli, A and Arcadi, FA and Militi, A and Cammaroto, S and Maggio, MG and Pizzocaro, S and Quartarone, A and De Nunzio, AM and Calabrò, RS},
title = {The Impact of Visualization on Stroke Rehabilitation in Adults: A Systematic Review of Randomized Controlled Trials on Guided and Motor Imagery.},
journal = {Biomedicines},
volume = {13},
number = {3},
pages = {},
doi = {10.3390/biomedicines13030599},
pmid = {40149575},
issn = {2227-9059},
abstract = {Background/Objectives: Guided imagery techniques, which include mentally picturing motions or activities to help motor recovery, are an important part of neuroplasticity-based motor therapy in stroke patients. Motor imagery (MI) is a kind of guided imagery in neurorehabilitation that focuses on mentally rehearsing certain motor actions in order to improve performance. This systematic review aims to evaluate the current evidence on guided imagery techniques and identify their therapeutic potential in stroke motor rehabilitation. Methods: Randomized controlled trials (RCTs) published in the English language were identified from an online search of PubMed, Web of Science, Embase, EBSCOhost, and Scopus databases without a specific search time frame. The inclusion criteria take into account guided imagery interventions and evaluate their impact on motor recovery through validated clinical, neurophysiological, or functional assessments. This review has been registered on Open OSF with the following number: DOI 10.17605/OSF.IO/3D7MF. Results: This review synthesized 41 RCTs on MI in stroke rehabilitation, with 996 participants in the intervention group and 757 in the control group (average age 50-70, 35% female). MI showed advantages for gait, balance, and upper limb function; however, the RoB 2 evaluation revealed 'some concerns' related to allocation concealment, blinding, and selective reporting issues. Integrating MI with gait training or action observation (AO) seems to improve motor recovery, especially in balance and walking. Technological methods like brain-computer interfaces (BCIs) and hybrid models that combine MI with circuit training hold potential for enhancing functional mobility and motor results. Conclusions: Guided imagery shows promise as a beneficial adjunct in stroke rehabilitation, with the potential to improve motor recovery across several domains such as gait, upper limb function, and balance.},
}

@article {pmid40148520,
year = {2025},
author = {Li, J and Shi, J and Yu, P and Yan, X and Lin, Y},
title = {Feature-aware domain invariant representation learning for EEG motor imagery decoding.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {10664},
pmid = {40148520},
issn = {2045-2322},
support = {2023QN06002//Inner Mongolia Natural Science Foundation project of China/ ; JJ230408//Inner Mongolia University high-level talent research project/ ; },
mesh = {Humans ; *Electroencephalography/methods ; Signal-To-Noise Ratio ; Brain-Computer Interfaces ; Signal Processing, Computer-Assisted ; Algorithms ; Imagination/physiology ; Machine Learning ; Movement/physiology ; },
abstract = {Electroencephalography (EEG)-based motor imagery (MI) is extensively utilized in clinical rehabilitation and virtual reality-based movement control. Decoding EEG-based MI signals is challenging because of the inherent spatio-temporal variability of the original signal representation, coupled with a low signal-to-noise ratio (SNR), which impedes the extraction of clean and robust features. To address this issue, we propose a multi-scale spatio-temporal domain-invariant representation learning method, termed MSDI. By decomposing the original signal into spatial and temporal components, the proposed method extracts invariant features at multiple scales from both components. To further constrain the representation to invariant domains, we introduce a feature-aware shift operation that resamples the representation based on its feature statistics and feature measure, thereby projecting the features into a domain-invariant space. We evaluate our proposed method via two publicly available datasets, BNCI2014-001 and BNCI2014-004, demonstrating state-of-the-art performance on both datasets. Furthermore, our method exhibits superior time efficiency and noise resistance.},
}

Humans
*Electroencephalography/methods
Signal-To-Noise Ratio
Brain-Computer Interfaces
Signal Processing, Computer-Assisted
Algorithms
Imagination/physiology
Machine Learning
Movement/physiology

@article {pmid40147158,
year = {2025},
author = {Tang, J and Xi, X and Wang, T and Li, L and Yang, J},
title = {Evaluation of the impacts of neuromuscular electrical stimulation based on cortico-muscular-cortical functional network.},
journal = {Computer methods and programs in biomedicine},
volume = {265},
number = {},
pages = {108735},
doi = {10.1016/j.cmpb.2025.108735},
pmid = {40147158},
issn = {1872-7565},
abstract = {BACKGROUND AND OBJECTIVE: Neuromuscular electrical stimulation (NMES) has been extensively applied for recovery of motor functions. However, its impact on the cortical network changes related to muscle activity remains unclear, which is crucial for understanding the changes in the collaborative working patterns within the sensory-motor control system post-stroke.

METHODS: In this research, we have integrated cortico-muscular interactions, intercortical interactions, and intramuscular interactions to propose a novel closed-loop network structure, namely the cortico-muscular-cortical functional network (CMCFN). The framework is endowed with the capability to distinguish the directionality of causal interactions and local frequency band characteristics through transfer spectral entropy (TSE). Subsequently, the CMCFN is applied to stroke patients to elucidate the potential influence of NMES on cortical physiological function changes during motor induction.

RESULTS: The results indicate that short-term modulation by NMES significantly enhanced the cortico-muscular interactions of the contralateral cerebral hemisphere and the affected upper limb (p < 0.001), while coexistence of facilitatory and inhibitory effects is observed in the intermuscular coupling across different electromyography (EMG) signals. Furthermore, following NMES treatment, the connectivity of the brain functional network is significantly strengthened, particularly in the γ frequency band (30-45 Hz), with marked improvements in the clustering coefficient and shortest path length (p < 0.001).

CONCLUSIONS: As a new framework, CMCFN offers a novel perspective for studying motor cortical networks related to muscle activity.},
}

@article {pmid40145943,
year = {2025},
author = {Jilderda, MF and Zhang, Y and Rebattu, V and Salunga, R and Mesker, W and Wong, J and de Munck, L and Fornander, T and Nordenskjöld, B and Stål, O and Anderson, AKL and Bastiaannet, E and Treuner, K and Liefers, GJ},
title = {Identification of Early-Stage Breast Cancer with Minimal Risk of Recurrence by Breast Cancer Index.},
journal = {Clinical cancer research : an official journal of the American Association for Cancer Research},
volume = {},
number = {},
pages = {},
doi = {10.1158/1078-0432.CCR-24-3836},
pmid = {40145943},
issn = {1557-3265},
abstract = {PURPOSE: This study assessed the prognostic ability of Breast Cancer Index (BCI) to identify patients at minimal risk (<5%) of 10-year distant recurrence (DR) who are unlikely to benefit from adjuvant endocrine therapy.

EXPERIMENTAL DESIGN: This prospective translational study included postmenopausal patients with early-stage, HR+ N0 breast cancer from the Stockholm (STO-3) trial who underwent surgery alone ("untreated") or surgery plus adjuvant tamoxifen ("treated") and the Netherlands Cancer Registry (NCR; surgery alone). The primary endpoint was time to DR. An adjusted BCI model with an additional cut-point was developed that stratified patients into 4 prognostic risk groups.

RESULTS: Across cohorts, 16%-22% of patients were classified as minimal risk of 10-year DR. In the Stockholm untreated cohort (n = 283), risks in the minimal, low, intermediate, and high risk groups were 2.3%, 15.5% (hazard ratio, 4.71 [95% CI, 1.09-20.29] versus minimal risk), 19.8% (6.97 [1.61-30.18]), and 35.9% (13.21 [3.07-56.76]), respectively (P < .001). In the Stockholm treated cohort (n = 317), risks were 4.3%, 5.0% (1.16 [0.35-3.85]), 11.7% (2.45 [0.74-8.14]), and 21.1% (5.27 [1.72-16.16]; P < .001). In the NCR cohort (n = 1245), risks were 4.5%, 7.5% (sub-distribution hazard ratio, 1.67 [95% CI, 0.81-3.45]), 10.3% (2.40 [1.14-5.03]), and 13.1% (3.13 [1.50-6.55]; P = .005). BCI risk scores provided additional independent information over standard prognostic factors (likelihood ratio, c2 = 7.98; P = .004).

CONCLUSIONS: The adjusted BCI model identified women with early-stage, HR+ N0 breast cancer at minimal risk of DR who may consider de-escalating adjuvant endocrine therapy.},
}

@article {pmid40144587,
year = {2025},
author = {Marzulli, M and Bleuzé, A and Saad, J and Martel, F and Ciuciu, P and Aksenova, T and Struber, L},
title = {Classifying mental motor tasks from chronic ECoG-BCI recordings using phase-amplitude coupling features.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1521491},
pmid = {40144587},
issn = {1662-5161},
abstract = {INTRODUCTION: Phase-amplitude coupling (PAC), the modulation of high-frequency neural oscillations by the phase of slower oscillations, is increasingly recognized as a marker of goal-directed motor behavior. Despite this interest, its specific role and potential value in decoding attempted motor movements remain unclear.

METHODS: This study investigates whether PAC-derived features can be leveraged to classify different motor behaviors from ECoG signals within Brain-Computer Interface (BCI) systems. ECoG data were collected using the WIMAGINE implant during BCI experiments with a tetraplegic patient performing mental motor tasks. The data underwent preprocessing to extract complex neural oscillation features (amplitude, phase) through spectral decomposition techniques. These features were then used to quantify PAC by calculating different coupling indices. PAC metrics served as input features in a machine learning pipeline to evaluate their effectiveness in predicting mental tasks (idle state, right-hand movement, left-hand movement) in both offline and pseudo-online modes.

RESULTS: The PAC features demonstrated high accuracy in distinguishing among motor tasks, with key classification features highlighting the coupling of theta/low-gamma and beta/high-gamma frequency bands.

DISCUSSION: These preliminary findings hold significant potential for advancing our understanding of motor behavior and for developing optimized BCI systems.},
}

@article {pmid40144585,
year = {2025},
author = {Saad, J and Evans, A and Jaoui, I and Roux-Sibillon, V and Hardy, E and Anghel, L},
title = {Comparison metrics and power trade-offs for BCI motor decoding circuit design.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1547074},
pmid = {40144585},
issn = {1662-5161},
abstract = {Brain signal decoders are increasingly being used in early clinical trials for rehabilitation and assistive applications such as motor control and speech decoding. As many Brain-Computer Interfaces (BCIs) need to be deployed in battery-powered or implantable devices, signal decoding must be performed using low-power circuits. This paper reviews existing hardware systems for BCIs, with a focus on motor decoding, to better understand the factors influencing the power and algorithmic performance of such systems. We propose metrics to compare the energy efficiency of a broad range of on-chip decoding systems covering Electroencephalography (EEG), Electrocorticography (ECoG), and Microelectrode Array (MEA) signals. Our analysis shows that achieving a given classification rate requires an Input Data Rate (IDR) that can be empirically estimated, a finding that is helpful for sizing new BCI systems. Counter-intuitively, our findings show a negative correlation between the power consumption per channel (PpC) and the Information Transfer Rate (ITR). This suggests that increasing the number of channels can simultaneously reduce the PpC through hardware sharing and increase the ITR by providing new input data. In fact, for EEG and ECoG decoding circuits, the power consumption is dominated by the complexity of signal processing. To better understand how to minimize this power consumption, we review the optimizations used in state-of-the-art decoding circuits.},
}

@article {pmid40143846,
year = {2025},
author = {Andronache, C and Curǎvale, D and Nicolae, IE and Neacşu, AA and Nicolae, G and Ivanovici, M},
title = {Tackling the possibility of extracting a brain digital fingerprint based on personal hobbies predilection.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1487175},
pmid = {40143846},
issn = {1662-4548},
abstract = {In an attempt to create a more familiar brain-machine interaction for biometric authentication applications, we investigated the efficiency of using the users' personal hobbies, interests, and memory collections. This approach creates a unique and pleasant experience that can be later utilized within an authentication protocol. This paper presents a new EEG dataset recorded while subjects watch images of popular hobbies, pictures with no point of interest and images with great personal significance. In addition, we propose several applications that can be tackled with our newly collected dataset. Namely, our study showcases 4 types of applications and we obtain state-of-the-art level results for all of them. The tackled tasks are: emotion classification, category classification, authorization process, and person identification. Our experiments show great potential for using EEG response to hobby visualization for people authentication. In our study, we show preliminary results for using predilection for personal hobbies, as measured by EEG, for identifying people. Also, we propose a novel authorization process paradigm using electroencephalograms. Code and dataset are available here.},
}

@article {pmid40141951,
year = {2025},
author = {Aydin, S and Melek, M and Gökrem, L},
title = {A Safe and Efficient Brain-Computer Interface Using Moving Object Trajectories and LED-Controlled Activation.},
journal = {Micromachines},
volume = {16},
number = {3},
pages = {},
doi = {10.3390/mi16030340},
pmid = {40141951},
issn = {2072-666X},
support = {2023/86//Tokat Gaziosmanpaşa Üniversitesi/ ; },
abstract = {Nowadays, brain-computer interface (BCI) systems are frequently used to connect individuals who have lost their mobility with the outside world. These BCI systems enable individuals to control external devices using brain signals. However, these systems have certain disadvantages for users. This paper proposes a novel approach to minimize the disadvantages of visual stimuli on the eye health of system users in BCI systems employing visual evoked potential (VEP) and P300 methods. The approach employs moving objects with different trajectories instead of visual stimuli. It uses a light-emitting diode (LED) with a frequency of 7 Hz as a condition for the BCI system to be active. The LED is assigned to the system to prevent it from being triggered by any involuntary or independent eye movements of the user. Thus, the system user will be able to use a safe BCI system with a single visual stimulus that blinks on the side without needing to focus on any visual stimulus through moving balls. Data were recorded in two phases: when the LED was on and when the LED was off. The recorded data were processed using a Butterworth filter and the power spectral density (PSD) method. In the first classification phase, which was performed for the system to detect the LED in the background, the highest accuracy rate of 99.57% was achieved with the random forest (RF) classification algorithm. In the second classification phase, which involves classifying moving objects within the proposed approach, the highest accuracy rate of 97.89% and an information transfer rate (ITR) value of 36.75 (bits/min) were achieved using the RF classifier.},
}

@article {pmid40140571,
year = {2025},
author = {Gazit Shimoni, N and Tose, AJ and Seng, C and Jin, Y and Lukacsovich, T and Yang, H and Verharen, JPH and Liu, C and Tanios, M and Hu, E and Read, J and Tang, LW and Lim, BK and Tian, L and Földy, C and Lammel, S},
title = {Changes in neurotensin signalling drive hedonic devaluation in obesity.},
journal = {Nature},
volume = {},
number = {},
pages = {},
pmid = {40140571},
issn = {1476-4687},
abstract = {Calorie-rich foods, particularly those that are high in fat and sugar, evoke pleasure in both humans and animals[1]. However, prolonged consumption of such foods may reduce their hedonic value, potentially contributing to obesity[2-4]. Here we investigated this phenomenon in mice on a chronic high-fat diet (HFD). Although these mice preferred high-fat food over regular chow in their home cages, they showed reduced interest in calorie-rich foods in a no-effort setting. This paradoxical decrease in hedonic feeding has been reported previously[3-7], but its neurobiological basis remains unclear. We found that in mice on regular diet, neurons in the lateral nucleus accumbens (NAcLat) projecting to the ventral tegmental area (VTA) encoded hedonic feeding behaviours. In HFD mice, this behaviour was reduced and uncoupled from neural activity. Optogenetic stimulation of the NAcLat→VTA pathway increased hedonic feeding in mice on regular diet but not in HFD mice, though this behaviour was restored when HFD mice returned to a regular diet. HFD mice exhibited reduced neurotensin expression and release in the NAcLat→VTA pathway. Furthermore, neurotensin knockout in the NAcLat and neurotensin receptor blockade in the VTA each abolished optogenetically induced hedonic feeding behaviour. Enhancing neurotensin signalling via overexpression normalized aspects of diet-induced obesity, including weight gain and hedonic feeding. Together, our findings identify a neural circuit mechanism that links the devaluation of hedonic foods with obesity.},
}

@article {pmid40139011,
year = {2025},
author = {Luo, C and Zhu, X and Zhang, Y and Wen, Y and Wan, L and Qian, Z},
title = {Competitive electrochemical immunosensor for trace phosphorylated Tau181 analysis in plasma: Toward point-of-care technologies of Alzheimer's disease.},
journal = {Talanta},
volume = {292},
number = {},
pages = {128009},
doi = {10.1016/j.talanta.2025.128009},
pmid = {40139011},
issn = {1873-3573},
abstract = {Accurate detection of core Alzheimer's disease (AD) biomarkers in biofluids is crucial for identifying preclinical AD and predicting disease progression. Phosphorylated tau 181 (p-tau181), a key biomarker, holds promise for early diagnosis. This work presents a sensitive and rapid electrochemical immunosensor (EC-iSensor) based on screen-printed electrodes (SPEs) for p-tau181 quantification. Employing a competitive immunoassay format, the EC-iSensor utilizes biotinylated p-tau181 as a competitor against the target analyte for binding to immobilized capture antibodies. Signal transduction is achieved via horseradish peroxidase (HRP) and tetramethylbenzidine (TMB) substrate. The EC-iSensor exhibits a low detection limit of 1.91 fg/mL and a wide dynamic range spanning 6.97 fg/mL to 100 ng/mL in PBS. Furthermore, successful detection of p-tau181 in blood samples from AD patients demonstrated its practical applicability. This cost-effective SPE-based EC-iSensor offers a simple and highly sensitive platform for p-tau181 detection, presenting potential for point-of-care technologies (POCT) of AD.},
}

@article {pmid40138736,
year = {2025},
author = {Yang, W and Wang, X and Qi, W and Wang, W},
title = {LGFormer: Integrating local and global representations for EEG decoding.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adc5a3},
pmid = {40138736},
issn = {1741-2552},
abstract = {OBJECTIVE: Electroencephalography (EEG) decoding is challenging because of its temporal variability and low signal-to-noise ratio, which complicate the extraction of meaningful information from signals. Although convolutional neural networks (CNNs) effectively extract local features from EEG signals, they are constrained by restricted receptive fields. In contrast, transformers excel at capturing global dependencies through self-attention mechanisms but often require extensive training data and computational resources, which limits their efficiency on EEG datasets with limited samples.

APPROACH: In this paper, we propose LGFormer, a hybrid network designed to efficiently learn both local and global representations for EEG decoding. LGFormer employs a deep attention module to extract global information from EEG signals, dynamically adjusting the focus of CNNs. Subsequently, LGFormer incorporates a local-enhanced transformer, combining the strengths of CNNs and transformers to achieve multiscale perception from local to global. Despite integrating multiple advanced techniques, LGFormer maintains a lightweight design and training efficiency.

MAIN RESULTS: LGFormer achieves state-of-the-art performance within 200 training epochs across four public datasets, including motor imagery, cognitive workload, and error-related negativity decoding tasks. Additionally, we propose a novel spatial and temporal attention visualization method, revealing that LGFormer captures discriminative spatial and temporal features, enhancing model interpretability and providing insights into its decision-making process.

SIGNIFICANCE: In summary, LGFormer demonstrates superior performance while maintaining high training efficiency across different tasks, highlighting its potential as a versatile and practical model for EEG decoding.},
}

@article {pmid40136841,
year = {2025},
author = {Mohamed, AK and Aharonson, V},
title = {Single-Trial Electroencephalography Discrimination of Real, Regulated, Isometric Wrist Extension and Wrist Flexion.},
journal = {Biomimetics (Basel, Switzerland)},
volume = {10},
number = {3},
pages = {},
pmid = {40136841},
issn = {2313-7673},
support = {99207 and 117965//National Research Foundation of South Africa/ ; },
abstract = {Improved interpretation of electroencephalography (EEG) associated with the neural control of essential hand movements, including wrist extension (WE) and wrist flexion (WF), could improve the performance of brain-computer interfaces (BCIs). These BCIs could control a prosthetic or orthotic hand to enable motor-impaired individuals to regain the performance of activities of daily living. This study investigated the interpretation of neural signal patterns associated with kinematic differences between real, regulated, isometric WE and WF movements from recorded EEG data. We used 128-channel EEG data recorded from 14 participants performing repetitions of the wrist movements, where the force, speed, and range of motion were regulated. The data were filtered into four frequency bands: delta and theta, mu and beta, low gamma, and high gamma. Within each frequency band, independent component analysis was used to isolate signals originating from seven cortical regions of interest. Features were extracted from these signals using a time-frequency algorithm and classified using Mahalanobis distance clustering. We successfully classified bilateral and unilateral WE and WF movements, with respective accuracies of 90.68% and 69.80%. The results also demonstrated that all frequency bands and regions of interest contained motor-related discriminatory information. Bilateral discrimination relied more on the mu and beta bands, while unilateral discrimination favoured the gamma bands. These results suggest that EEG-based BCIs could benefit from the extraction of features from multiple frequencies and cortical regions.},
}

@article {pmid40136836,
year = {2025},
author = {Rusev, G and Yordanov, S and Nedelcheva, S and Banderov, A and Sauter-Starace, F and Koprinkova-Hristova, P and Kasabov, N},
title = {Decoding Brain Signals in a Neuromorphic Framework for a Personalized Adaptive Control of Human Prosthetics.},
journal = {Biomimetics (Basel, Switzerland)},
volume = {10},
number = {3},
pages = {},
pmid = {40136836},
issn = {2313-7673},
support = {101070891/01.10.2022//European Commission under the HORIZON-EIC action/ ; },
abstract = {Current technological solutions for Brain-machine Interfaces (BMI) achieve reasonable accuracy, but most systems are large in size, power consuming and not auto-adaptive. This work addresses the question whether current neuromorphic technologies could resolve these problems? The paper proposes a novel neuromorphic framework of a BMI system for prosthetics control via decoding Electro Cortico-Graphic (ECoG) brain signals. It includes a three-dimensional spike timing neural network (3D-SNN) for brain signals features extraction and an on-line trainable recurrent reservoir structure (Echo state network (ESN)) for Motor Control Decoding (MCD). A software system, written in Python using NEST Simulator SNN library is described. It is able to adapt continuously in real time in supervised or unsupervised mode. The proposed approach was tested on several experimental data sets acquired from a tetraplegic person. First simulation results are encouraging, showing also the need for a further improvement via multiple hyper-parameters tuning. Its future implementation on a neuromorphic hardware platform that is smaller in size and significantly less power consuming is discussed too.},
}

@article {pmid40136825,
year = {2025},
author = {Li, H and Wang, Y and Fu, P},
title = {A Novel Multi-Dynamic Coupled Neural Mass Model of SSVEP.},
journal = {Biomimetics (Basel, Switzerland)},
volume = {10},
number = {3},
pages = {},
pmid = {40136825},
issn = {2313-7673},
support = {2024JC-YBON-0659//Natural Science Basic Research Program of Shaanxi Province/ ; TC2023JC16//Basic Research Programs of Taicang/ ; },
abstract = {Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) leverage high-speed neural synchronization to visual flicker stimuli for efficient device control. While SSVEP-BCIs minimize user training requirements, their dependence on physical EEG recordings introduces challenges, such as inter-subject variability, signal instability, and experimental complexity. To overcome these limitations, this study proposes a novel neural mass model for SSVEP simulation by integrating frequency response characteristics with dual-region coupling mechanisms. Specific parallel linear transformation functions were designed based on SSVEP frequency responses, and weight coefficient matrices were determined according to the frequency band energy distribution under different visual stimulation frequencies in the pre-recorded SSVEP signals. A coupled neural mass model was constructed by establishing connections between occipital and parietal regions, with parameters optimized through particle swarm optimization to accommodate individual differences and neuronal density variations. Experimental results demonstrate that the model achieved a high-precision simulation of real SSVEP signals across multiple stimulation frequencies (10 Hz, 11 Hz, and 12 Hz), with maximum errors decreasing from 2.2861 to 0.8430 as frequency increased. The effectiveness of the model was further validated through the real-time control of an Arduino car, where simulated SSVEP signals were successfully classified by the advanced FPF-net model and mapped to control commands. This research not only advances our understanding of SSVEP neural mechanisms but also releases the user from the brain-controlled coupling system, thus providing a practical framework for developing more efficient and reliable BCI-based systems.},
}

@article {pmid40134759,
year = {2025},
author = {Sakel, M and Ozolins, CA and Saunders, K and Biswas, R},
title = {A home-based EEG neurofeedback treatment for chronic neuropathic pain-a pilot study.},
journal = {Frontiers in pain research (Lausanne, Switzerland)},
volume = {6},
number = {},
pages = {1479914},
pmid = {40134759},
issn = {2673-561X},
abstract = {OBJECTIVE: This study assessed the effect of an 8-week home-based neurofeedback intervention in chronic neuropathic pain patients.

SUBJECTS/PATIENTS: A cohort of eleven individuals with chronic neuropathic pain receiving treatment within the NHS framework.

METHODS: Participants were trained to operate a home-based neurofeedback system. Each received a portable Axon system for one week of electroencephalogram (EEG) baselines, followed by an 8-week neurofeedback intervention, and subsequent 12 weeks of follow-up EEG baselines. Primary outcome measures included changes in the Brief Pain Inventory and Visual Analogue Pain Scale at post-intervention, and follow-ups compared with the baseline. Secondary outcomes included changes in depression, anxiety, stress, pain catastrophizing, central sensitization, sleep quality, and quality of life. EEG activities were monitored throughout the trial.

RESULTS: Significant improvements were noted in pain scores, with all participants experiencing overall pain reduction. Clinically significant pain improvement (≥30%) was reported by 5 participants (56%). Mood scores showed a significant decrease in depression (p < 0.05), and pain catastrophizing (p < 0.05) scores improved significantly at post-intervention, with continued improvement at the first-month follow-up.

CONCLUSION: The findings indicate that an 8-week home-based neurofeedback intervention improved pain and psychological well-being in this sample of chronic neuropathic pain patients. A randomized controlled trial is required to replicate these results in a larger cohort. Clinical Trial Registration: https://clinicaltrials.gov/study/NCT05464199, identifier: (NCT05464199).},
}

@article {pmid40133571,
year = {2025},
author = {Xu, X and Sha, L and Basang, S and Peng, A and Zhou, X and Liu, Y and Li, Y and Chen, L},
title = {Mortality in patients with epilepsy: a systematic review.},
journal = {Journal of neurology},
volume = {272},
number = {4},
pages = {291},
pmid = {40133571},
issn = {1432-1459},
support = {Grant Recipient//Science and Technology Major Project of Sichuan Province/ ; },
mesh = {Humans ; *Epilepsy/mortality/epidemiology ; Sudden Unexpected Death in Epilepsy/epidemiology ; },
abstract = {BACKGROUND: Epilepsy is linked to a significantly higher risk of death, yet public awareness remains low. This study aims to investigate mortality characteristics, to reduce epilepsy-related deaths and improve prevention strategies.

METHODS: This study systematically reviews mortality data in relevant literature from PubMed and Embase up until June 2024. Data quality is assessed using the Newcastle-Ottawa Scale, and analysis includes trends, regional differences, and the economic impact of premature death. Global Burden of Disease (GBD) data are used to validate trends. In addition, a review of guidelines and expert statements on sudden unexpected death in epilepsy (SUDEP) is included to explore intervention strategies and recommendations.

RESULTS: Annual mortality rates of epilepsy have gradually declined, mainly due to improvements in low-income countries, while high-income regions have experienced an upward trend. Male patients exhibit higher mortality rates than females. Age-based analysis shows that the elderly contributes most to this increase due to chronic conditions such as cardiovascular disease and pneumonia related to epilepsy. This may be a key factor contributing to the increased mortality among epilepsy patients in aging high-income regions. Accidents and suicides are more prevalent in low-income regions. The highest mortality risks occur in the early years post-diagnosis and during prolonged, uncontrolled epilepsy. SUDEP remains a leading cause of death.

CONCLUSION: This study highlights the impact of gender, region, and disease duration on epilepsy mortality. Future research should focus on elderly epilepsy patients mortality characteristics and personalized interventions for SUDEP.},
}

Humans
*Epilepsy/mortality/epidemiology
Sudden Unexpected Death in Epilepsy/epidemiology

@article {pmid40135785,
year = {2025},
author = {Chen, J and Liu, B and Peng, G and Zhou, L and Tan, C and Qin, J and Li, J and Hong, Z and Wu, Y and Lu, M and Cai, F and Huang, Y},
title = {Achieving High-Performance Transcranial Ultrasound Transmission Through Mie and Fano Resonance in Flexible Metamaterials.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {},
number = {},
pages = {e2500170},
doi = {10.1002/advs.202500170},
pmid = {40135785},
issn = {2198-3844},
support = {2021YFB3801802//National Key R&D Program of China/ ; 2022YFB3204300//National Key R&D Program of China/ ; 226-2024-00120//Fundamental Research Funds for the Central Universities/ ; 2023R5231//Special Support Plan for High Level Talents in Zhejiang Province/ ; },
abstract = {Transcranial ultrasound holds great potential in medical applications. However, the effective transmission of ultrasound through the skull remains challenging due to the acoustic impedance mismatch, as well as the non-uniform thickness, and the curved surface. To overcome these challenges, this work introduces an innovative Mie-resonance flexible metamaterial (MRFM), which consists of periodically arranged low-speed micropillars embedded within a high-speed flexible substrate. The MRFM generates Mie-resonance, which couples with the skull to form Fano resonance, thereby enhancing ultrasound transmittance through the skull. Simulation results demonstrate that the proposed resonance solution significantly increases transcranial ultrasound transmittance from 33.7% to 75.2% at 0.309 MHz. For the fabrication of the MRFM, porous nickel foam is used as the Mie micropillars, and agarose hydrogel serves as the flexible substrate. Experimental results demonstrate enhanced ultrasound transmittance from 20.6% to 73.3% at 0.33 MHz with the MRFM, which shows good agreement with the simulation results, further validating the effectiveness of the design. The simplicity, tunability, and flexibility of the MRFM represent a significant breakthrough, addressing the limitations of conventional rigid metamaterials. This work lays a solid theoretical and experimental foundation for advancing the clinical application of transcranial ultrasound stimulation and neuromodulation.},
}

@article {pmid40131365,
year = {2025},
author = {Almanna, MA and Elkaim, LM and Alvi, MA and Levett, JJ and Li, B and Mamdani, M and Al-Omran, M and Alotaibi, NM},
title = {Public Perception of the Brain-Computer Interface: Insights from a Decade of Data on X.},
journal = {JMIR formative research},
volume = {},
number = {},
pages = {},
doi = {10.2196/60859},
pmid = {40131365},
issn = {2561-326X},
abstract = {BACKGROUND: Given the recent evolution and achievements in Brain-Computer interface (BCI) technologies, understanding public perception and sentiments towards such novel technologies is important for guiding their communication strategies in marketing and education.

OBJECTIVE: This study aims to explore the public perception of BCI technology by examining posts on X (Twitter), utilizing Natural Language Processing (NLP) methods.

METHODS: A mixed-methods study was conducted on BCI-related posts from January 2010 to December 2021. The dataset included 65,340 posts from 38,926 unique users. This dataset was subject to a detailed NLP analysis including VADER, TextBlob, and NRCLex libraries, focusing on quantifying the sentiment (positive, neutral, and negative), the degree of subjectivity, and the range of emotions expressed in the posts. The temporal dynamics of sentiments were examined using the Mann-Kendall trend test to identify significant trends or shifts in public interest over time, based on monthly incidence. We utilized the Sentiment.ai tool to infer users' demographics by matching pre-defined attributes in users' profile biographies to certain demographic groups. We used the BERTopic tool for semantic understanding of discussions related to BCI.

RESULTS: The analysis showed a significant rise in BCI discussions in 2017, coinciding with Elon Musk's announcement of Neuralink. Sentiment analysis revealed that 59.38% of posts were neutral, 32.75% were positive, and 7.85% were negative. The average polarity score demonstrated a generally positive trend over the course of the study (Mann-Kendall Statistic = 0.266, tau = 0.266, P<.001). Most posts were objective (77.81%), with a smaller proportion being subjective (22.02%). Biographic analysis showed that the 'Broadcasting' group contributed the most to BCI discussions (30.67%), but the 'Scientific' group, which contributed 27.58% of the discussions, had the highest overall engagement metrics. Emotional analysis identified anticipation (20.56%), trust (17.59%), and fear (13.98%) as the most prominent emotions in BCI discussions. Key topics included Neuralink and Elon Musk, practical applications of BCIs, and the potential for gamification.

CONCLUSIONS: This NLP-assisted study provides a decade-long analysis of public perception of BCI technology based on data from X. Overall, sentiments were neutral yet cautiously apprehensive, with anticipation, trust, and fear as the dominant emotions. The presence of fear underscores the need to address ethical concerns, particularly around data privacy, safety, and transparency. Transparent communication and ethical considerations are essential for building public trust and reducing apprehension. Influential figures and positive clinical outcomes, such as advancements in neuroprosthetics, could enhance favorable perceptions. The gamification of BCI, particularly in gaming and entertainment, also offers potential for wider public engagement and adoption. However, public perceptions on X may differ from other platforms, affecting the broader interpretation of results. Despite these limitations, the findings provide valuable insights for guiding future BCI developments, policy-making, and communication strategies.},
}

@article {pmid40129720,
year = {2025},
author = {Daly, I and Matran-Fernandez, A and Lebedev, MA and Kübler, A and Valeriani, D},
title = {Editorial: Datasets for brain-computer interface applications, volume II.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1569216},
pmid = {40129720},
issn = {1662-4548},
}

@article {pmid40129499,
year = {2025},
author = {Maltezou-Papastylianou, C and Scherer, R and Paulmann, S},
title = {How do voice acoustics affect the perceived trustworthiness of a speaker? A systematic review.},
journal = {Frontiers in psychology},
volume = {16},
number = {},
pages = {1495456},
pmid = {40129499},
issn = {1664-1078},
abstract = {Trust is a multidimensional and dynamic social and cognitive construct, considered the glue of society. Gauging someone's perceived trustworthiness is essential for forming and maintaining healthy relationships across various domains. Humans have become adept at inferring such traits from speech for survival and sustainability. This skill has extended to the technological space, giving rise to humanlike voice technologies. The inclination to assign personality traits to these technologies suggests that machines may be processed along similar social and vocal dimensions as human voices. Given the increasing prevalence of voice technology in everyday tasks, this systematic review examines the factors in the psychology of voice acoustics that influence listeners' trustworthiness perception of speakers, be they human or machine. Overall, this systematic review has revealed that voice acoustics impact perceptions of trustworthiness in both humans and machines. Specifically, combining multiple acoustic features through multivariate methods enhances interpretability and yields more balanced findings compared to univariate approaches. Focusing solely on isolated features like pitch often yields inconclusive results when viewed collectively across studies without considering other factors. Crucially, situational, or contextual factors should be utilised for enhanced interpretation as they tend to offer more balanced findings across studies. Moreover, this review has highlighted the significance of cross-examining speaker-listener demographic diversity, such as ethnicity and age groups; yet, the scarcity of such efforts accentuates the need for increased attention in this area. Lastly, future work should involve listeners' own trust predispositions and personality traits with ratings of trustworthiness perceptions.},
}

@article {pmid40128831,
year = {2025},
author = {Liu, XY and Wang, WL and Liu, M and Chen, MY and Pereira, T and Doda, DY and Ke, YF and Wang, SY and Wen, D and Tong, XG and Li, WG and Yang, Y and Han, XD and Sun, YL and Song, X and Hao, CY and Zhang, ZH and Liu, XY and Li, CY and Peng, R and Song, XX and Yasi, A and Pang, MJ and Zhang, K and He, RN and Wu, L and Chen, SG and Chen, WJ and Chao, YG and Hu, CG and Zhang, H and Zhou, M and Wang, K and Liu, PF and Chen, C and Geng, XY and Qin, Y and Gao, DR and Song, EM and Cheng, LL and Chen, X and Ming, D},
title = {Recent applications of EEG-based brain-computer-interface in the medical field.},
journal = {Military Medical Research},
volume = {12},
number = {1},
pages = {14},
pmid = {40128831},
issn = {2054-9369},
support = {2021YFF1200602//The National Key R&D Program of China/ ; 0401260011//The National Science Fund for Excellent Overseas Scholars/ ; c02022088//The National Defense Science and Technology Innovation Fund of Chinese Academy of Sciences/ ; 82472098//National Natural Science Foundation of China (General Program)/ ; 82202798//The National Natural Science Foundation of China/ ; 22YF1404200//The Shanghai Sailing Program/ ; },
mesh = {Humans ; *Brain-Computer Interfaces/trends ; *Electroencephalography/methods/trends/instrumentation ; Epilepsy/physiopathology ; },
abstract = {Brain-computer interfaces (BCIs) represent an emerging technology that facilitates direct communication between the brain and external devices. In recent years, numerous review articles have explored various aspects of BCIs, including their fundamental principles, technical advancements, and applications in specific domains. However, these reviews often focus on signal processing, hardware development, or limited applications such as motor rehabilitation or communication. This paper aims to offer a comprehensive review of recent electroencephalogram (EEG)-based BCI applications in the medical field across 8 critical areas, encompassing rehabilitation, daily communication, epilepsy, cerebral resuscitation, sleep, neurodegenerative diseases, anesthesiology, and emotion recognition. Moreover, the current challenges and future trends of BCIs were also discussed, including personal privacy and ethical concerns, network security vulnerabilities, safety issues, and biocompatibility.},
}

Humans
*Brain-Computer Interfaces/trends
*Electroencephalography/methods/trends/instrumentation
Epilepsy/physiopathology

@article {pmid40127544,
year = {2025},
author = {Davis, KC and Wyse-Sookoo, K and Raza, F and Meschede-Krasa, B and Prins, NW and Fisher, L and Brown, EN and Cajigas, I and Ivan, ME and Jagid, JR and Prasad, A},
title = {5-year follow-up of a fully implanted brain-computer interface in a spinal cord injury patient.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adc48c},
pmid = {40127544},
issn = {1741-2552},
abstract = {INTRODUCTION: Spinal cord injury (SCI) affects over 250,000 individuals in the US. Brain-computer interfaces (BCIs) may improve quality of life by controlling external devices. Invasive intracortical BCIs have shown promise in clinical trials but degrade in the chronic period and tether patients to acquisition hardware. Alternatively, electrocorticography (ECoG) records data from electrodes on the cortex, and studies evaluating fully implanted BCI-ECoG systems are scarce. Thus, we seek to address this need using a fully implanted ECoG-based BCI that allows for home use in SCI.

METHOD: The patient used a long-term BCI system, initially controlling an FES orthosis in the lab and later using an external mechanical orthosis at home. To evaluate its long-term viability, electrode contact impedance, signal quality, and decoder performance were measured. Signal quality was assessed using signal-to-noise ratio and maximum bandwidth of the signal. Decoder performance was monitored using the area under the receiver operator characteristic curve (AUROC).

RESULTS: The study analyzed data from the patient's home environment over 54 months, revealing that the device was used at home for 38 ± 24 minutes on average daily. After six months, we observed stable event-related desynchronization that aided in determining the onset of motor intention. The decoder's average AUROC across months was 0.959. Importantly, 40 months of the data collected was gather from the subject's home or community environment. The results indicate long-term ECoG recordings were stable for motor-imagery classification and motor control in the environment in a case of an individual with SCI.

CONCLUSION: This study presents the long-term feasibility and viability of an ECoG-based BCI system that persists in the home environment in a case of SCI. Future research should explore larger electrode counts with more participants to confirm this stability. Understanding these trends is crucial for clinical utility and chronic viability in broader patient populations.},
}

@article {pmid40127541,
year = {2025},
author = {Marissens Cueva, V and Bougrain, L and Lotte, F and Rimbert, S},
title = {Reliable predictor of BCI motor imagery performance using median nerve stimulation.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adc48d},
pmid = {40127541},
issn = {1741-2552},
abstract = {OBJECTIVE: Predicting performance in Brain-Computer Interfaces (BCI) is crucial for enhancing user experience, optimizing training and identifying the most efficient BCI approach for each individual.

APPROACH: This study explores the use of Median Nerve Stimulation (MNS) as a predictor of Motor Imagery (MI)-BCI performance. MNS induces Event Related (De)Synchronization (ERD/ERS) patterns in the brain that are similar to those generated during MI tasks, providing a non-invasive, user-independent, and easy-to-setup method for performance prediction.

MAIN RESULTS: Our proposed predictor, based on the minimum value of the ERD induced by the MNS, not only exhibits a robust correlation with the MI-BCI performance accuracy (rho = -0.71, p < 0.001), but also effectively predicts this performance with a significant correlation (rho = 0.61, mean absolute error = 9.0, p < 0.01). These results demonstrate its validity as a reliable predictor of MI-BCI performance.

SIGNIFICANCE: By systematically analyzing patterns induced by MNS and correlating them with subsequent MI-BCI task performance, we aim to establish a robust predictive method of motor activity to each individual only based on MNS, making it possible, among other things, to passively predict BCI deficiency or proficiency, and to potentially adapt BCI parameters for an efficient BCI experience or BCI-based recovery.},
}

@article {pmid40127535,
year = {2025},
author = {Avin, O and Almagor, O and Yoav, N and Rosipal, R and Shriki, O},
title = {Supervised autoencoder denoiser for non-stationarity in multi-session EEG-based BCI.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adc48e},
pmid = {40127535},
issn = {1741-2552},
abstract = {Non-stationarity in EEG signals poses significant challenges for the performance and implementation of brain computer- interfaces (BCIs). In this study, we propose a novel method for cross-session BCI tasks that employs a supervised autoencoder to reduce session-specific information while preserving task-related signals. Our approach compresses high-dimensional EEG inputs and reconstructs them, thereby mitigating non-stationary variability in the data. In addition to unsupervised minimization of the reconstruction error, the objective function of the network includes two supervised terms to ensure that the latent representations exclude session identity information and are optimized for subsequent classification. Evaluation across three different motor imagery datasets demonstrates that our approach effectively addresses domain adaptation challenges, outperforming both naïve cross-session and within-session methods. Our method eliminates the need for data from new sessions, making it fully unsupervised concerning new session data and reducing the necessity for recalibration with each session. Furthermore, the reduction of session-specific information in the reconstructed signals indicates that our approach effectively denoises non-stationary signals, thereby enhancing the accuracy of BCI models. Future applications could extend this model to a broader range of BCI tasks and explore the residual signals to investigate sources of non-stationary brain components and other cognitive processes. .},
}

@article {pmid40125566,
year = {2025},
author = {Hong, W and Mao, L and Lin, K and Huang, C and Su, Y and Zhang, S and Wang, C and Wang, D and Song, J and Chen, Z},
title = {Accurate and Noninvasive Dysphagia Assessment via a Soft High-Density sEMG Electrode Array Conformal to the Submental and Infrahyoid Muscles.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {},
number = {},
pages = {e2500472},
doi = {10.1002/advs.202500472},
pmid = {40125566},
issn = {2198-3844},
support = {12402202//National Natural Science Foundation of China/ ; 12302223//National Natural Science Foundation of China/ ; 12225209//National Natural Science Foundation of China/ ; 12321002//National Natural Science Foundation of China/ ; 2021C03050//Zhejiang Key Research and Development Program of China/ ; },
abstract = {Accurate, noninvasive dysphagia assessment is important for rehabilitation therapy but current clinical diagnostic methods are either invasive or subjective. Surface electromyography (sEMG) that monitors muscle activity during swallowing, offers a promising alternative. However, existing sEMG electrode arrays for dysphagia assessment remain challenging in combining the advantages of a large coverage area and strong compliance to the entire swallowing muscles. Here, we report a stretchable, breathable, large-area high-density sEMG (HD-sEMG) electrode array, which enables intimate contact to complex surface of the submental and infrahyoid muscles to detect high-fidelity HD-sEMG signals during swallowing. The electrode array features a 64-channel soft on-skin sensing array for comprehensive data capture, and a stiff connector for simple and reliable connection to an external acquisition setup. Systemically experimental studies revealed the easy operability of the soft HD-sEMG electrode array for effortless integration with the skin, as well as the excellent mechanical and electrical characteristics even subject to substantial skin deformations. By comparing HD-sEMG signals collected from 38 participants, three objective indicators for quantitative dysphagia evaluation were discussed. Finally, a machine learning model was developed to accurately and automatically classify the severity of dysphagia, and the factors affecting the recognition accuracy of the model were discussed in depth.},
}

@article {pmid40124055,
year = {2025},
author = {Lee, H and Lee, S and Lee, S and Lee, J and Chou, N and Shin, H},
title = {A Highly Efficient Low-Cost Flexible Neural Probe for Scalable Mass Fabrication.},
journal = {ACS omega},
volume = {10},
number = {10},
pages = {10733-10740},
pmid = {40124055},
issn = {2470-1343},
abstract = {Neural probes capable of the precise recording and control of brain signals are essential tools for brain-computer interfaces and neuroscience research. However, conventional neural probes have not been widely adopted due to the high costs associated with semiconductor fabrication and complex packaging procedures. Herein, we present a breakthrough in this area in the form of a highly efficient flexible neural probe with a production cost of only 1.5 dollars per unit that can be mass-produced (1000 units within 3 days). The probe design is based on a standardized flexible printed circuit board (PCB) process that ensures large-scale producibility and minimizes device performance variation. The device features four independent neural probes that enable flexible targeting of multiple brain regions and a reusable interface PCB that minimizes packaging complexity. The neural signal recording performance of the fabricated probe is comparable to that of traditional silicon-based probes and is scalable with eight electrodes capable of simultaneous measurements. We anticipate that our innovative device will significantly improve the accessibility of neuroscience research.},
}

@article {pmid40122923,
year = {2025},
author = {Yang, B and Rong, F and Xie, Y and Li, D and Zhang, J and Li, F and Shi, G and Gao, X},
title = {A multi-day and high-quality EEG dataset for motor imagery brain-computer interface.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {488},
pmid = {40122923},
issn = {2052-4463},
support = {62376149//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography ; Imagination ; Male ; Adult ; },
abstract = {A key challenge in developing a robust electroencephalography (EEG)-based brain-computer interface (BCI) is obtaining reliable classification performance across multiple days. In particular, EEG-based motor imagery (MI) BCI faces large variability and low signal-to-noise ratio. To address these issues, collecting a large and reliable dataset is critical for learning of cross-session and cross-subject patterns while mitigating EEG signals inherent instability. In this study, we obtained a comprehensive MI dataset from the 2019 World Robot Conference Contest-BCI Robot Contest. We collected EEG data from 62 healthy participants across three recording sessions. This experiment includes two paradigms: (1) two-class tasks: left and right hand-grasping, (2) three-class tasks: left and right hand-grasping, and foot-hooking. The dataset comprises raw data, and preprocessed data. For the two-class data, an average classification accuracy of 85.32% was achieved using EEGNet, while the three-class data achieved an accuracy of 76.90% using deepConvNet. Different researchers can reuse the dataset according to their needs. We hope that this dataset will significantly advance MI-BCI research, particularly in addressing cross-session and cross-subject challenges.},
}

Humans
*Brain-Computer Interfaces
*Electroencephalography
Imagination
Male
Adult

@article {pmid40122080,
year = {2025},
author = {Branco, MP and Verberne, MSW and van Balen, BJ and Bekius, A and Leinders, S and Ketelaar, M and Geytenbeek, J and van Driel-Boerrigter, M and Willems-Op Het Veld, M and Rabbie-Baauw, K and Vansteensel, MJ},
title = {Stakeholder's perspective on brain-computer interfaces for children and young adults with cerebral palsy.},
journal = {Disability and rehabilitation. Assistive technology},
volume = {},
number = {},
pages = {1-11},
doi = {10.1080/17483107.2025.2481426},
pmid = {40122080},
issn = {1748-3115},
abstract = {Communication Brain-Computer Interfaces (cBCIs) are a promising tool for people with motor and speech impairment, in particular for children and young adults with communication impairments, for example due to cerebral palsy (CP). Here we aimed to create a solid basis for the user-centered design of cBCIs for children and young adults with severe CP by investigating the perspectives of their parents/caregivers and health care professionals on communication and cBCIs. We conducted an online survey on 1) current communication problems and usability of used aids, 2) interest in cBCIs, and 3) preference for specific types of cBCIs. A total of 19 parents/caregivers and 36 health care professionals who interacted directly with children and young adults (8-25 years old) with severe CP, corresponding to Gross Motor Function Classification System level IV or V, participated. Both groups of respondents indicated that motor impairment occurred the most frequently and had the greatest impact on communication. The currently used communication aids included mainly no/low-tech aids and high-tech aids. The majority of health care professionals and parents/caregivers reported an interest in cBCIs, with a slight preference for implanted electrodes over non-implanted ones, and no preference for either of the two proposed mental BCI control strategies. Results indicate that cBCIs should be considered for a subpopulation of children and young adults with severe CP, and that in the development of cBCIs for this group both visual stimuli and sensorimotor rhythms, as well as the use of implanted electrodes, should be considered.},
}

@article {pmid40121857,
year = {2025},
author = {Zhou, H and Qiao, K and Rao, L and Zhai, HJ},
title = {Nanosilica cross-linked polyurethane hybrid hydrogels to stabilize the silicone rubber based invasive electrode-neural tissue interface.},
journal = {Colloids and surfaces. B, Biointerfaces},
volume = {251},
number = {},
pages = {114643},
doi = {10.1016/j.colsurfb.2025.114643},
pmid = {40121857},
issn = {1873-4367},
abstract = {An unstable electrode-neural tissue interface induced by tissue inflammatory response hinders the application of invasive brain-computer interfaces (BCIs). In this work, nanosilica cross-linked polyurethane (SiO2/PU) hybrid hydrogels were prepared to serve as the coatings and to modify silicone rubber (SR), which is a commonly used encapsulation material of invasive electrodes for neural recording/stimulation. The hydrophilicity, swelling ratio, and bulk ionic conductivity of SiO2/PU hybrid hydrogels were tailored by incorporating different amount of SiO2 serving as the cross-linking agent. Correspondingly, the optimized SiO2/PU hybrid hydrogel coatings have less impact on the electrochemical properties of invasive electrodes relative to PU hydrogel. Cell affinity assays with rat pheochromocytoma cells reveal that coatings made of SiO2/PU hybrid hydrogels are more effective in enhancing their adhesion and neurite outgrowth than those made of PU hydrogels. The adsorption amount of non-specific proteins on SR is significantly reduced by 81.6 % and 92.6 % upon coating with PU hydrogels and SiO2/PU hybrid hydrogels, respectively. Histological assessment indicates that the SR implants with a SiO2/PU hybrid hydrogel coating provoke the mildest tissue response. Collectively, the SiO2/PU hybrid hydrogel is highly promising for the stabilization of electrode-neural tissue interface, which is crucial for the development of invasive BCIs.},
}

@article {pmid40119207,
year = {2025},
author = {Cheng, M and Lu, D and Li, K and Wang, Y and Tong, X and Qi, X and Yan, C and Ji, K and Wang, J and Wang, W and Lv, H and Zhang, X and Kong, W and Zhang, J and Ma, J and Li, K and Wang, Y and Feng, J and Wei, P and Li, Q and Shen, C and Fu, XD and Ma, Y and Zhang, X},
title = {Author Correction: Mitochondrial respiratory complex IV deficiency recapitulates amyotrophic lateral sclerosis.},
journal = {Nature neuroscience},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41593-025-01941-2},
pmid = {40119207},
issn = {1546-1726},
}

@article {pmid40118966,
year = {2025},
author = {Hussain, SAH and Raza, I and Hussain, SA and Jamal, MH and Gulrez, T and Zia, A},
title = {A mental state aware brain computer interface for adaptive control of electric powered wheelchair.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {9880},
pmid = {40118966},
issn = {2045-2322},
mesh = {*Brain-Computer Interfaces ; Humans ; *Wheelchairs ; *Electroencephalography ; Male ; Machine Learning ; Persons with Disabilities/psychology ; Adult ; Signal Processing, Computer-Assisted ; },
abstract = {Brain-computer interfaces (BCI) provide a mobility solution for patients with various disabilities. However, BCI systems require further research to enhance their performance while incorporating the physical and behavioral states of patients into the system. As the principal users of a BCI system, patients with disabilities are emotionally sensitive, so a BCI device that adaptively adjusts to the psychological effects of the patient could provide the foundation for refining BCI applications. This paper focuses on the collection and realization of human electroencephalogram (EEG) signals data, obtained as a response to different psychological effects of sound stimuli. Filtration and pre-processing of the data set are achieved using the frequency-based distribution of EEG signals. Different machine learning tools and techniques are evaluated and applied to abstracted powerbands of psychological signals. The experimental results show that the proposed system predicts mental states with an average accuracy of 74.26%. In addition, an automated BCI system is developed to control an electric wheelchair (EPW) while responding to the mental state of the user with a contingency mechanism. The results show that such a system could be designed to make BCI systems more reliable, safe, adaptable, and responsive to emotions for sensitive paralytic patients. The system also shows a satisfactory True Positive Rate (TPR) and False Positive Rate (FPR) with an average time of 8.4 s to generate the interpretable brain signal from the user.},
}

*Brain-Computer Interfaces
Humans
*Wheelchairs
*Electroencephalography
Male
Machine Learning
Persons with Disabilities/psychology
Adult
Signal Processing, Computer-Assisted

@article {pmid40118477,
year = {2025},
author = {Ren, Y and Kang, YN and Cao, SY and Meng, F and Zhang, J and Liao, R and Li, X and Chen, Y and Wen, Y and Wu, J and Xia, W and Xu, L and Wen, S and Liu, H and Li, Y and Gu, J and Lv, Q},
title = {Evaluating the performance of large language models in health education for patients with ankylosing spondylitis/spondyloarthritis: a cross-sectional, single-blind study in China.},
journal = {BMJ open},
volume = {15},
number = {3},
pages = {e097528},
doi = {10.1136/bmjopen-2024-097528},
pmid = {40118477},
issn = {2044-6055},
mesh = {Humans ; Cross-Sectional Studies ; *Spondylitis, Ankylosing ; China ; Male ; Single-Blind Method ; Female ; Adult ; Middle Aged ; Patient Education as Topic/methods ; Language ; Spondylarthritis ; Health Knowledge, Attitudes, Practice ; },
abstract = {OBJECTIVES: To evaluate the potential of large language models (LLMs) in health education for patients with ankylosing spondylitis (AS)/spondyloarthritis (SpA), focusing on the accuracy of information transmission, patient acceptance and performance differences between different models.

DESIGN: Cross-sectional, single-blind study.

SETTING: Multiple centres in China.

PARTICIPANTS: 182 volunteers, including 4 rheumatologists and 178 patients with AS/SpA.

Scientificity, precision and accessibility of the content of the answers provided by LLMs; patient acceptance of the answers.

RESULTS: LLMs performed well in terms of scientificity, precision and accessibility, with ChatGPT-4o and Kimi models outperforming traditional guidelines. Most patients with AS/SpA showed a higher level of understanding and acceptance of the responses from LLMs.

CONCLUSIONS: LLMs have significant potential in medical knowledge transmission and patient education, making them promising tools for future medical practice.},
}

Humans
Cross-Sectional Studies
*Spondylitis, Ankylosing
China
Male
Single-Blind Method
Female
Adult
Middle Aged
Patient Education as Topic/methods
Language
Spondylarthritis
Health Knowledge, Attitudes, Practice

@article {pmid40117671,
year = {2025},
author = {Revechkis, B and Aflalo, TN and Pouratian, N and Rosario, ER and Ouellette, DS and Zhang, C and Pejsa, K},
title = {Effector specificity in human posterior parietal neurons and local field potentials during movement in virtual reality and online brain control.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adc3ca},
pmid = {40117671},
issn = {1741-2552},
abstract = {Objective Neural prosthetics represent a significant opportunity for control of external effectors like artificial limbs and computer devices as well as a means for interacting with virtual reality. Prior studies have shown posterior parietal cortex to be a viable source of signals for the purposes of decoding motor intentions given its representation of both visual inputs and motor outputs. Additionally, signals in parietal cortex have been shown known to be associated with tool use the body schema. We investigated if more realistic movement effectors in virtual reality might elicit stronger signals at the single neuron level in parietal cortex. Approach A quadriplegic human subject was implanted with multi-electrode recording arrays in the posterior parietal cortex. Neural spiking recorded during attempted movement in a computer-rendered, stereoscopic, 3D virtual environment. Tuning to different movement effectors was examined using a first-person, movement generation task in addition to closed loop control performance. Results We found single neurons and simultaneously recorded field potentials in a quadriplegic patient exhibited enhanced responses during attempted (rather than passively observed) movement of a realistic and "attached" 3D arm relative to either a visually similar but "detached" 2D arm or a non-anthropomorphic abstract effector. These preferences were found despite the patient having lost motor function years prior. These differences did not effect performance during closed loop brain control of the movement effectors. Significance In human parietal cortex, these signals responded preferentially to visually guided attempted movement of a realistic arm rather than abstract effector. However, by choosing a text-only training paradigm, this tuning did not seem to effect closed loop brain control in a virtual reality environment. Additionally, single-unit driven brain control of a body in virtual reality is reported here for the first time.},
}

@article {pmid40117159,
year = {2025},
author = {Xia, Y and Chen, J and Li, J and Gong, T and Vidal-Rosas, EE and Loureiro, R and Cooper, RJ and Zhao, H},
title = {A Deep-Learning Empowered, Real-Time Processing Platform of fNIRS/DOT for Brain Computer Interfaces and Neurofeedback.},
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.2025.3553794},
pmid = {40117159},
issn = {1558-0210},
abstract = {Brain-Computer Interfaces (BCI) and Neurofeedback (NFB) approaches, which both rely on real-time monitoring of brain activity, are increasingly being applied in rehabilitation, assistive technology, neurological diseases and behavioral disorders. Functional near-infrared spectroscopy (fNIRS) and diffuse optical tomography (DOT) are promising techniques for these applications due to their non-invasiveness, portability, low cost, and relatively high spatial resolution. However, real-time processing of fNIRS/DOT data remains a significant challenge as it requires establishing a baseline of the measurement, simultaneously performing real-time motion artifact (MA) correction across all channels, and (in the case of DOT) addressing the time-consuming process of image reconstruction. This study proposes a real-time processing system for fNIRS/DOT that integrates baseline calibration, denoising autoencoder (DAE) based MA correction model with a sliding window strategy, and a pre-calculated inverse Jacobian matrix to streamline the reconstructed 3D brain hemodynamics. The DAE model was trained on an extensive whole-head high-density DOT (HD-DOT) dataset and tested on separate motor imagery dataset augmented with artificial MA. The system demonstrated the capability to simultaneously process approximately 750 channels in real-time. Our results show that the DAE-based MA correction method outperformed traditional MA correction in terms of mean squared error and correlation to the known MA-free data while maintaining low latency, which is critical for effective BCI and NFB applications. The system's high-channel, real-time processing capability provides channel-wise oxygenation information and functional 3D imaging, making it well-suited for fNIRS/DOT applications in BCI and NFB, particularly in movement-intensive scenarios such as motor rehabilitation and assistive technology for mobility support.},
}

@article {pmid40115973,
year = {2025},
author = {Chen, W and Chen, H and Ruan, H and Jiang, W and Chen, C and Xu, M and Xu, Y and Chen, H and Yu, Z and Chen, S},
title = {Identification of Adolescents With Major Depressive Disorder Using Random Forest Based on Nocturnal Heart Rate Variability.},
journal = {Psychophysiology},
volume = {62},
number = {3},
pages = {e70049},
doi = {10.1111/psyp.70049},
pmid = {40115973},
issn = {1469-8986},
support = {A20240472//Hangzhou Municipal General Medical and Health Plan/ ; },
mesh = {Humans ; *Heart Rate/physiology ; *Depressive Disorder, Major/physiopathology/diagnosis ; Adolescent ; Male ; Female ; *Electrocardiography ; Bayes Theorem ; Polysomnography ; Random Forest ; },
abstract = {Major depressive disorder (MDD) in adolescents is often underdiagnosed, with the current diagnosis predominantly relying on subjective assessment. Sleep disturbance and reduced heart rate variability (HRV) have been typically observed in adolescents with MDD. This study aimed to develop an automatic classification model based on nocturnal HRV features to identify adolescent MDD. Sixty-three subjects, including depressed adolescents and healthy controls, participated in the study and completed a three-night sleep electrocardiogram (ECG) monitoring, yielding 160 overnight RR interval time series and 7520 5-min short-term segments for analysis. Nineteen HRV features were extracted from the time domain, frequency domain, and nonlinear dynamics. The Bayesian-optimized random forest (BO-RF) algorithm was applied as the classifier, with performance evaluated using ten-fold cross-validation. The impact of data accumulation on the reliability of identification using short-term data and the importance of features were also examined. The BO-RF classifier based on long-term features achieved a noteworthy predictive accuracy of 80.6%, and the performance of the classifier using short-term data showed a significant improvement when more segment outcomes from the same night were included, ultimately achieving an accuracy of 75.0%. The Poincaré plot-derived features, especially heart rate asymmetry (HRA) features such as C1d, significantly contributed to distinguishing depressed adolescents from healthy subjects. Nocturnal HRV features can effectively differentiate adolescents with MDD from healthy controls. This study provides a promising diagnostic approach for adolescent MDD, with the potential to be integrated into wearable devices for broader application.},
}

Humans
*Heart Rate/physiology
*Depressive Disorder, Major/physiopathology/diagnosis
Adolescent
Male
Female
*Electrocardiography
Bayes Theorem
Polysomnography
Random Forest

@article {pmid40115889,
year = {2025},
author = {Li, J and Hu, B and Guan, ZH},
title = {AM-MTEEG: multi-task EEG classification based on impulsive associative memory.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1557287},
pmid = {40115889},
issn = {1662-4548},
abstract = {Electroencephalogram-based brain-computer interfaces (BCIs) hold promise for healthcare applications but are hindered by cross-subject variability and limited data. This article proposes a multi-task (MT) classification model, AM-MTEEG, which integrates deep learning-based convolutional and impulsive networks with bidirectional associative memory (AM) for cross-subject EEG classification. AM-MTEEG deals with the EEG classification of each subject as an independent task and utilizes common features across subjects. The model is built with a convolutional encoder-decoder and a population of impulsive neurons to extract shared features across subjects, as well as a Hebbian-learned bidirectional associative memory matrix to classify EEG within one subject. Experimental results on two BCI competition datasets demonstrate that AM-MTEEG improves average accuracy over state-of-the-art methods and reduces performance variance across subjects. Visualization of neuronal impulses in the bidirectional associative memory network reveal a precise mapping between hidden-layer neuron activities and specific movements. Given four motor imagery categories, the reconstructed waveforms resemble the real event-related potentials, highlighting the biological interpretability of the model beyond classification.},
}

@article {pmid40115888,
year = {2025},
author = {Liu, J and Xie, J and Zhang, H and Yang, H and Shao, Y and Chen, Y},
title = {Improvement of BCI performance with bimodal SSMVEPs: enhancing response intensity and reducing fatigue.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1506104},
pmid = {40115888},
issn = {1662-4548},
abstract = {Steady-state visual evoked potential (SSVEP) is a widely used brain-computer interface (BCI) paradigm, valued for its multi-target capability and limited EEG electrode requirements. Conventional SSVEP methods frequently lead to visual fatigue and decreased recognition accuracy because of the flickering light stimulation. To address these issues, we developed an innovative steady-state motion visual evoked potential (SSMVEP) paradigm that integrated motion and color stimuli, designed specifically for augmented reality (AR) glasses. Our study aimed to enhance SSMVEP response intensity and reduce visual fatigue. Experiments were conducted under controlled laboratory conditions. EEG data were analyzed using the deep learning algorithm of EEGNet and fast Fourier transform (FFT) to calculate the classification accuracy and assess the response intensity. Experimental results showed that the bimodal motion-color integrated paradigm significantly outperformed single-motion SSMVEP and single-color SSVEP paradigms, respectively, achieving the highest accuracy of 83.81% ± 6.52% under the medium brightness (M) and area ratio of C of 0.6. Enhanced signal-to-noise ratio (SNR) and reduced visual fatigue were also observed, as confirmed by objective measures and subjective reports. The findings verified the bimodal paradigm as a novel application in SSVEP-based BCIs, enhancing both brain response intensity and user comfort.},
}

@article {pmid40115887,
year = {2025},
author = {Schreiner, L and Wipprecht, A and Olyanasab, A and Sieghartsleitner, S and Pretl, H and Guger, C},
title = {Brain-computer-interface-driven artistic expression: real-time cognitive visualization in the pangolin scales animatronic dress and screen dress.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1516776},
pmid = {40115887},
issn = {1662-5161},
abstract = {This paper explores the intersection of brain-computer interfaces (BCIs) and artistic expression, showcasing two innovative projects that merge neuroscience with interactive wearable technology. BCIs, traditionally applied in clinical settings, have expanded into creative domains, enabling real-time monitoring and representation of cognitive states. The first project showcases a low-channel BCI Screen Dress, utilizing a 4-channel electroencephalography (EEG) headband to extract an engagement biomarker. The engagement is visualized through animated eyes on small screens embedded in a 3D-printed dress, which dynamically responds to the wearer's cognitive state. This system offers an accessible approach to cognitive visualization, leveraging real-time engagement estimation and demonstrating the effectiveness of low-channel BCIs in artistic applications. In contrast, the second project involves an ultra-high-density EEG (uHD EEG) system integrated into an animatronic dress inspired by pangolin scales. The uHD EEG system drives physical movements and lighting, visually and kinetically expressing different EEG frequency bands. Results show that both projects have successfully transformed brain signals into interactive, wearable art, offering a multisensory experience for both wearers and audiences. These projects highlight the vast potential of BCIs beyond traditional clinical applications, extending into fields such as entertainment, fashion, and education. These innovative wearable systems underscore the ability of BCIs to expand the boundaries of creative expression, turning the wearer's cognitive processes into art. The combination of neuroscience and fashion tech, from simplified EEG headsets to uHD EEG systems, demonstrates the scalability of BCI applications in artistic domains.},
}

@article {pmid40115885,
year = {2025},
author = {Liu, J and Li, Y and Zhao, D and Zhong, L and Wang, Y and Hao, M and Ma, J},
title = {Efficacy and safety of brain-computer interface for stroke rehabilitation: an overview of systematic review.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1525293},
pmid = {40115885},
issn = {1662-5161},
abstract = {BACKGROUND: Stroke is a major global health challenge that significantly influences public health. In stroke rehabilitation, brain-computer interfaces (BCI) offer distinct advantages over traditional training programs, including improved motor recovery and greater neuroplasticity. Here, we provide a first re-evaluation of systematic reviews and meta-analyses to further explore the safety and clinical efficacy of BCI in stroke rehabilitation.

METHODS: A standardized search was conducted in major databases up to October 2024. We assessed the quality of the literature based on the following aspects: AMSTAR-2, PRISMA, publication year, study design, homogeneity, and publication bias. The data were subsequently visualized as radar plots, enabling a comprehensive and rigorous evaluation of the literature.

RESULTS: We initially identified 908 articles and, after removing duplicates, we screened titles and abstracts of 407 articles. A total of 18 studies satisfied inclusion criteria were included. The re-evaluation showed that the quality of systematic reviews and meta-analyses concerning stroke BCI training is moderate, which can provide relatively good evidence.

CONCLUSION: It has been proven that BCI-combined treatment can improve upper limb motor function and the quality of daily life for stroke patients, especially those in the subacute phase, demonstrating good safety. However, its effects on improving speech function, lower limb motor function, and long-term outcomes require further evidence. Multicenter, long-term follow-up studies are needed to increase the reliability of the results.

CLINICAL TRIAL REGISTRATION: https://www.crd.york.ac.uk/PROSPERO/view/CRD42024562114, CRD42023407720.},
}

@article {pmid40112995,
year = {2025},
author = {Huang, T and Ma, Y and Chen, H and Zhang, S and Liu, L and Chen, M and Jia, R and Lin, L and Ullah, MW and Fan, Y},
title = {A silk nanofiber and hyaluronic acid composite hemostatic sponge for compressible hemostasis.},
journal = {International journal of biological macromolecules},
volume = {},
number = {},
pages = {142262},
doi = {10.1016/j.ijbiomac.2025.142262},
pmid = {40112995},
issn = {1879-0003},
abstract = {Uncontrolled traumatic blood loss is a leading cause of hemorrhagic shock and death, highlighting the critical need for compressible and rapid hemostatic first-aid materials. In this study, silk nanofibers (MA-SNFs) were prepared through maleic acid (MA) hydrolysis decorated with enriched carboxyl groups. The MA-SNFs were then combined with hyaluronic acid (HA) through EDC/NHS crosslinking to form a porous sponge (i.e., MA-SNF/HA) through freeze-drying. The fabricated MA-SNF/HA sponges demonstrated excellent blood compatibility (hemolysis ratio < 5 %), outstanding hemocompatibility (blood clotting index (BCI) < 35 % within 60 s), and good cytocompatibility (cell viability >85 %). Among the different sponges prepared, M4-H6 (MA-SNFs: HA = 4:6) exhibited the best liquid reabsorption capacity after 80 % compression, outperforming M6-H4 and M5-H5 sponges. Furthermore, M4-H6 sponge absorbed liquid rapidly (~30 s) while matching the liquid absorption capacity of commercial gelatin sponge (GS), which require over 5 min for similar absorption (2232.84 ± 141.69 %). These findings suggest that M4-H6 sponge is highly suitable for compressible hemostasis applications and provide further insights into its potential hemostatic mechanism.},
}

@article {pmid40112909,
year = {2025},
author = {Wang, J and Guo, M and Zhang, J and Bai, Y and Ni, G},
title = {Early audiovisual integration in target processing under continuous noise: Behavioral and EEG evidence.},
journal = {Neuropsychologia},
volume = {211},
number = {},
pages = {109128},
doi = {10.1016/j.neuropsychologia.2025.109128},
pmid = {40112909},
issn = {1873-3514},
abstract = {Multisensory integration is interconnected across various information reception. The stage and mechanism of brain response to audiovisual integration have not been fully understood. In this study, we designed audiovisual and unisensory experiments to investigate task performance and electrophysiological characteristics associated with audiovisual integration in a continuous background interference environment using materials collected from the underwater environment. Behavioral results showed that the reaction time (RT) was shorter, and the accuracy was higher in the audiovisual experiment. The cumulative distribution function (CDF) results of RT indicated that audiovisual integration supported the co-activation model. Event-related potential (ERP) results revealed shorter latency of the P1 and N1 components in the audiovisual experiment. Microstate analysis indicated that the parietal-occipital area may play a key role in audiovisual integration. Moreover, event-related spectral perturbation (ERSP) results demonstrated the critical role of low-frequency oscillation in audiovisual integration at the early stage. Our findings support the view that the beneficial effect of audiovisual integration is predominantly upon the early stage of neural information processing, including task-independent information.},
}

@article {pmid40112768,
year = {2025},
author = {Yang, HR and Han, MR and Oh, EY and Choi, JY and Choi, JY and Kim, Y and Kim, YT and Kang, H and Kim, JG},
title = {Role of cold-inducible RNA-binding protein in hypothalamic regulation of feeding behavior during fasting and cold exposure.},
journal = {Biochemical and biophysical research communications},
volume = {757},
number = {},
pages = {151616},
doi = {10.1016/j.bbrc.2025.151616},
pmid = {40112768},
issn = {1090-2104},
abstract = {Appetite regulation is a complex process that is critical for maintaining energy balance and is governed by intricate molecular and cellular mechanisms in the hypothalamus. RNA-binding proteins play vital roles in the post-transcriptional regulation of mRNA and influence feeding behavior and energy metabolism. This study explored the role of cold-inducible RNA-binding protein (Cirbp) in hypothalamic neurons under metabolic stress conditions, such as fasting and cold exposure. Next-generation sequencing (NGS) of the hypothalami from fasted mice identified 67 differentially expressed RNA-binding proteins, with Cirbp and RNA-binding motif protein 3 (Rbm3) being significantly upregulated. Immunohistochemical analysis confirmed increased Cirbp expression in the arcuate nucleus (ARC) and dorsomedial hypothalamus during fasting, indicating responsiveness to metabolic cues. Ribo-Tag analysis of agouti-related protein (AgRP) neurons demonstrated elevated Cirbp expression levels in response to fasting, linking it to hunger-regulating pathways. Intracerebroventricular injection of Cirbp antisense oligodeoxynucleotides (AS ODN) reduced Cirbp expression, leading to a decrease in food intake and a reduction in body weight, highlighting the functional role of Cirbp in appetite regulation. Cold exposure also induced Cirbp expression in the ARC, which correlated with an increase in food intake. Blockade of Cirbp by AS ODN treatment attenuated cold-induced food intake, indicating that Cirbp plays a specific role in regulating feeding behavior during cold stress. This suggests that Cirbp is a key mediator in hypothalamic responses to metabolic stress, influencing feeding behavior through its regulatory functions in AgRP neurons. Further exploration of Cirbp mechanisms may offer insights into therapeutic strategies for energy balance disorders, such as obesity and anorexia.},
}

@article {pmid40111769,
year = {2025},
author = {Wang, X and Liu, A and Cui, H and Chen, X and Wang, K and Chen, X},
title = {GZSL-Lite: A Lightweight Generalized Zero-Shot Learning Network for SSVEP-Based BCIs.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2025.3553204},
pmid = {40111769},
issn = {1558-2531},
abstract = {Generalized zero-shot learning (GZSL) networks offer promising avenues for the development of user-friendly steady-state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs), aiming to alleviate the training burden on users. These networks only require the user to provide training data from partial classes during training, yet they demonstrate the capability to classify all classes during testing. However, these GZSL networks have a large number of trainable parameters, resulting in long training times and difficulty to practicalize. In this study, we proposed a dual-attention structure to construct a lightweight GZSL network, termed GZSL-Lite. We first embedded the input training data-constructed class templates, manually constructed sine templates, and electroencephalogram (EEG) signals using convolution-based networks. The embedding part uses the same network weights to embed the features across different stimulus frequencies while reducing the depth of the network. After embedding, two branches of the dual-attention use class and sine templates to guide the feature extraction of the EEG signal with the attention mechanism, respectively. Compared to the networks that extract all features equally, dual-attention focuses only on EEG features relative to templates, which helps classification with fewer parameters. Finally, we used depthwise convolutional blocks to output classification results. Experimental evaluations conducted on two publicly available datasets demonstrate the efficacy of the proposed network. Comparative analysis reveals a remarkable reduction in trainable parameters to less than 1% of the SOTA counterpart, concurrently showing significant performance improvement. The code is available for reproducibility at https://github.com/xtwong111/GZSL-Lite-for-SSVEP-Based-BCIs.},
}

@article {pmid40111392,
year = {2025},
author = {Tang, P and Jing, P and Luo, Z and Liu, K and Tan, W and Yao, Q and Qiu, Z and Liu, Y and Dou, Q and Yan, X},
title = {Modulating Ionic Hysteresis to Selective Interaction Mechanism toward Transition from Supercapacitor-Memristor to Supercapacitor-Diode.},
journal = {Nano letters},
volume = {},
number = {},
pages = {},
doi = {10.1021/acs.nanolett.5c00596},
pmid = {40111392},
issn = {1530-6992},
abstract = {The emerging ion-confined transport supercapacitors, including supercapacitor-diodes (CAPodes) and supercapacitor-memristors (CAPistors), offer potential for neuromorphic computing, brain-computer interface, signal propagation, and logic operations. This study reports a novel transition from CAPistor to CAPode via electrochemical cycling of a ZIF-7 electrode. X-ray absorption fine structure (XAFS) and electrochemical analyses reveal a shift from "ionic hysteresis" to "ionic selective interaction" in an alkaline electrolyte, elucidating the evolution of ionic devices. The CAPodes exhibit high rectification ratios, long cycling stability, and effective current blocking in reverse bias. Additionally, they are demonstrated in ionic logic circuits ("AND" and "OR" gates), with comparisons to traditional electronic diodes. This work advances the development of functional supercapacitors and iontronic devices for future capacitive computing architectures.},
}

@article {pmid40110612,
year = {2024},
author = {Jain, S and Srivastava, R},
title = {Multi-modality NDE fusion using encoder-decoder networks for identify multiple neurological disorders from EEG signals.},
journal = {Technology and health care : official journal of the European Society for Engineering and Medicine},
volume = {},
number = {},
pages = {9287329241291334},
doi = {10.1177/09287329241291334},
pmid = {40110612},
issn = {1878-7401},
abstract = {BackgroundThe complexity and diversity of brain activity patterns make it difficult to accurately diagnose neurological disorders such epilepsy, Parkinson's disease, schizophrenia, stroke, and Alzheimer's disease. Integrated and effective analysis of multiple data sources is often beyond the scope of traditional diagnostic procedures. With the use of multi-modal data, recent developments in neural network approaches present encouraging opportunities for raising diagnostic accuracy.ObjectivesA novel approach has been proposed toward the integration of different Nondestructive Evaluation data with EEG signals for improving the diagnosis of neurological disorders such as stroke, epilepsy, Parkinson's disease, and schizophrenia, by leveraging advanced neural network techniques in order to improve the identification and correlation of shared latent features across heterogeneous NDE datasets.MethodsWe determined the 2D scalogram images using a specific encoder-decoder neural network after transforming the EEG signals using wavelet signal processing. Several NDE data types can be easily integrated for thorough analysis due to this network's ability to extract and correlate important aspects from each form of data. Aiming to uncover common patterns indicating of neurological disorders, the technique was evaluated on datasets containing EEG signals and corresponding NDE data.ResultsOur method demonstrated a significant improvement in diagnostic accuracy and efficiency. The encoder-decoder network effectively identified shared latent features across the heterogeneous NDE datasets, leading to more precise and reliable diagnoses. The fusion of multi-modality NDE data with EEG signals provided a robust framework for the automatic identification of multiple neurological disorders.ConclusionsThis innovative approach represents a substantial advancement in the field of neurological disorder diagnosis. By integrating diverse NDE data with EEG signals through advanced neural network techniques, we have developed a method that enhances the accuracy and efficiency of diagnosing multiple neurological conditions. This fusion of multi-modality data has the potential to revolutionize current diagnostic practices in neurology, paving the way for more precise and automated identification of neurological disorders.},
}

@article {pmid40110537,
year = {2025},
author = {Zhou, Y and Xu, X and Zhang, D},
title = {Cognitive load recognition in simulated flight missions: an EEG study.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1542774},
pmid = {40110537},
issn = {1662-5161},
abstract = {Cognitive load recognition (CLR) utilizing EEG signals has experienced significant advancement in recent years. However, current load-eliciting paradigms often rely on simplistic cognitive tasks such as arithmetic calculations, failing to adequately replicate real-world scenarios and lacking applicability. This study explores simulated flight missions over time to better reflect operational environments and investigate temporal dynamics of multiple load states. Thirty-six participants were recruited to perform simulated flight tasks with varying cognitive load levels of low, medium, and high. Throughout the experiments, we collected EEG load data from three sessions, pre- and post-task resting-state EEG data, subjective ratings, and objective performance metrics. Then, we employed several deep convolutional neural network (CNN) models, utilizing raw EEG data as model input, to assess cognitive load levels with six classification designs. Key findings from the study include (1) a notable distinction between resting-state and post-fatigue EEG data; (2) superior performance of shallow CNN models compared to more complex ones; and (3) temporal dynamics decline in CLR as the missions progressed. This paper establishes a potential foundation for assessing cognitive states during intricate simulated tasks across different individuals.},
}

@article {pmid40109751,
year = {2025},
author = {Wang, X and Lin, C and Wang, X},
title = {Psychedelics and Pro-Social Behaviors: A Perspective on Autism Spectrum Disorders.},
journal = {ACS pharmacology & translational science},
volume = {8},
number = {3},
pages = {903-906},
pmid = {40109751},
issn = {2575-9108},
abstract = {Autism Spectrum Disorders (ASD) are complex neurodevelopmental conditions characterized by deficits in social interaction, communication, and repetitive behaviors. This viewpoint explores the potential mechanisms through which psychedelics such as lysergic acid diethylamide (LSD), psilocybin, and 3,4-methylenedioxymethamphetamine (MDMA) may positively influence pro-social behaviors, focusing on their implications for individuals with ASD.},
}

@article {pmid40109747,
year = {2025},
author = {Li, H and Wang, H and Wang, X},
title = {Psychedelics and the Autonomic Nervous System: A Perspective on Their Interplay and Therapeutic Potential.},
journal = {ACS pharmacology & translational science},
volume = {8},
number = {3},
pages = {899-902},
pmid = {40109747},
issn = {2575-9108},
abstract = {Psychedelics, known for their therapeutic potential in psychiatric disorders, interact with the autonomic nervous system in ways that are not well understood. This viewpoint examines the complex relationships between psychedelics and autonomic functions, focusing on sympathetic and parasympathetic modulation. We propose a research framework to elucidate how these interactions influence cardiovascular health and contribute to therapeutic outcomes.},
}

@article {pmid40109736,
year = {2025},
author = {Li, H and Wang, X},
title = {Exploring End-of-Life Experiences and Consciousness through the Lens of Psychedelics.},
journal = {ACS pharmacology & translational science},
volume = {8},
number = {3},
pages = {907-909},
pmid = {40109736},
issn = {2575-9108},
abstract = {Exploring dying through the lens of psychedelic experiences offers transformative perspectives on the end-of-life process, potentially alleviating existential distress and enriching the quality of life for those nearing death. Their potential in palliative care, therapy, and spiritual exploration is increasingly recognized, promising to revolutionize end-of-life understanding and care.},
}

@article {pmid40109381,
year = {2025},
author = {Lloyd, S and Bonventre, C},
title = {Habilitation beyond the Bionic Metaphor: Producing Deafnesses of the Future.},
journal = {Science, technology & human values},
volume = {50},
number = {2},
pages = {336-363},
pmid = {40109381},
issn = {0162-2439},
abstract = {In this article, we travel back to the early days of experimental use of cochlear implants (CIs) in the 1970s, when unsettled expectations of the device and broad investigations of its effects began to settle and center on speech outcomes. We describe how this attention to speech outcomes coalesced into specific understandings of what CIs do, and how implicit or explicit understandings of CIs as bionic devices that normalize hearing influenced research on and expectations of CIs into the present. We conclude that accumulated evidence about what is known and unknown about experiences and materialities with CIs calls for a decisive break from the metaphor of the bionic ear. This shift would create a space to reconsider the "deafness of history and the present," as well as experiences of brain-computer interfaces that are inclusive of nonnormative life. This article is based on fieldwork in research and clinical facilities in Australia, Canada, and the United States. It included forty-three interviews with clinical experts and leading researchers in the fields of audiology, psychoacoustics, and neuroscience, among them scientists involved in the development and commercialization of one of the first CIs.},
}

@article {pmid40109135,
year = {2025},
author = {Sheng, T and Li, J and Zheng, L and Du, N and Xie, M and Wang, X and Gao, X and Huang, M and Wen, S and Liu, W and Guo, Y and Yao, Y and Shao, X and Liu, L and Xu, J and Wang, Y and Zhang, M},
title = {An Expandable Brain-Machine Interface Enabled by Origami Materials and Structures for Tracking Epileptic Traveling Waves.},
journal = {Advanced healthcare materials},
volume = {},
number = {},
pages = {e2404947},
doi = {10.1002/adhm.202404947},
pmid = {40109135},
issn = {2192-2659},
support = {2023YFB4705500//National Key Research and Development Program of China/ ; 62350710211//National Natural Science Foundation of China/ ; 1S24080//Beijing Natural Science Foundation/ ; },
abstract = {Tracking neural activities across multiple brain regions remains a daunting challenge due to the non-negligible skull injuries during implantations of large-area electrocorticography (ECoG) grids and the limited spatial accessibility of conventional rectilinear depth probes. Here, a multiregion Brain-machine Interface (BMI) is proposed comprising an expandable bio-inspired origami ECoG electrode covering cortical areas larger than the cranial window, and an expandable origami depth probe capable of reaching multiple deep brain regions beyond a single implantation axis. Using the proposed BMI, it is observed that, in rat models of focal seizures, cortical multiband epileptiform activities mainly manifest as expanding traveling waves outward from a cortical source.},
}

@article {pmid40108144,
year = {2025},
author = {Qi, Z and Liu, H and Jin, F and Wang, Y and Lu, X and Liu, L and Yang, Z and Fan, L and Song, M and Zuo, N and Jiang, T},
title = {A wearable repetitive transcranial magnetic stimulation device.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {2731},
pmid = {40108144},
issn = {2041-1723},
support = {2021ZD0200200//Ministry of Science and Technology of the People's Republic of China (Chinese Ministry of Science and Technology)/ ; 82202253//National Natural Science Foundation of China (National Science Foundation of China)/ ; 31620103905//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Humans ; *Transcranial Magnetic Stimulation/instrumentation/methods ; *Wearable Electronic Devices ; Adult ; Male ; Female ; Walking/physiology ; Equipment Design ; Young Adult ; Motor Cortex/physiology ; },
abstract = {Repetitive transcranial magnetic stimulation (rTMS) is widely used to treat various neuropsychiatric disorders and to explore the brain, but its considerable power consumption and large size limit its potential for broader utility, such as applications in free behaviors and in home and community settings. We addressed this challenge through lightweight magnetic core coil designs and high-power-density, high-voltage pulse driving techniques and successfully developed a battery-powered wearable rTMS device. The combined weight of the stimulator and coil is only 3 kg. The power consumption was reduced to 10% of commercial rTMS devices even though the stimulus intensity and repetition frequency are comparable. We demonstrated the effectiveness of this device during free walking, showing that neural activity associated with the legs can enhance the cortex excitability associated with the arms. This advancement allows for high-frequency rTMS modulation during free behaviors and enables convenient home and community rTMS treatments.},
}

Humans
*Transcranial Magnetic Stimulation/instrumentation/methods
*Wearable Electronic Devices
Adult
Male
Female
Walking/physiology
Equipment Design
Young Adult
Motor Cortex/physiology

@article {pmid40107265,
year = {2025},
author = {Jiang, R and Tian, Y and Yuan, X and Guo, F},
title = {Regulation of pre-dawn arousal in Drosophila by a pair of trissinergic descending neurons of the visual and circadian networks.},
journal = {Current biology : CB},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.cub.2025.02.056},
pmid = {40107265},
issn = {1879-0445},
abstract = {Circadian neurons form a complex neural network that generates circadian oscillations. How the circadian neural network transmits circadian signals to other brain regions, thereby regulating the activity patterns in fruit flies, is not well known. Using the FlyWire database, we identified a cluster of descending neurons, DNp27, which is densely connected with key circadian neurons and the visual circuit, projecting extensively across the brain. DNp27 receives excitatory inputs from the circadian neurons DN3s at night and photo-inhibitory signals predominantly during the day, resulting in calcium oscillations that peak in the early morning and dip at dusk. Experimental manipulation of DNp27 revealed its role in activity regulation: artificial activation of DNp27 decreased flies' activity, while ablation or silencing led to an advance in the morning anticipatory peak. Similar alterations in the morning peak were observed following pan-neuronal knockdown of either Trissin or TrissinR, suggesting the involvement of this neuropeptide signaling pathway in DNp27 function. Moreover, neural circuitry and connectivity analyses indicate that DNp27 may regulate circadian neurons via extra-clock electrical oscillators (xCEOs). Lastly, we found that DNp27 modulates arousal thresholds by inhibiting light-responsive activity in the central brain, thereby promoting sleep stability, particularly in the pre-dawn period. Together, these findings suggest that DNp27 plays a crucial role in maintaining stable sleep patterns.},
}

@article {pmid40106898,
year = {2025},
author = {Hobbs, TG and Greenspon, CM and Verbaarschot, C and Valle, G and Hughes, CL and Boninger, ML and Bensmaia, SJ and Gaunt, RA},
title = {Biomimetic stimulation patterns drive natural artificial touch percepts using intracortical microstimulation in humans.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adc2d4},
pmid = {40106898},
issn = {1741-2552},
abstract = {OBJECTIVE: Intracortical microstimulation (ICMS) of human somatosensory cortex evokes tactile percepts that people describe as originating from their own body, but are not always described as feeling natural. It remains unclear whether stimulation parameters such as amplitude, frequency, and spatiotemporal patterns across electrodes can be chosen to increase the naturalness of these artificial tactile percepts.

APPROACH: In this study, we investigated whether biomimetic stimulation patterns - ICMS patterns that reproduce essential features of natural neural activity - increased the perceived naturalness of ICMS-evoked sensations compared to a non-biomimetic pattern in three people with cervical spinal cord injuries. All participants had electrode arrays implanted in their somatosensory cortices. Rather than qualitatively asking which pattern felt more natural, participants directly compared natural residual percepts, delivered by mechanical indentation on a sensate region of their hand, to artificial percepts evoked by ICMS and were asked whether linear non-biomimetic or biomimetic stimulation felt most like the mechanical indentation.

MAIN RESULTS: We show that simple biomimetic ICMS, which modulated the stimulation amplitude on a single electrode, was perceived as being more like a mechanical indentation reference on 32% of the electrodes. We also tested an advanced biomimetic stimulation scheme that captured more of the spatiotemporal dynamics of cortical activity using co-modulated stimulation amplitudes and frequencies across four electrodes. Here, ICMS felt more like the mechanical reference for 75% of the electrode groups. Finally, biomimetic stimulus trains required less charge than their non-biomimetic counterparts to create an intensity-matched sensation.

SIGNIFICANCE: We conclude that ICMS encoding schemes that mimic naturally occurring neural spatiotemporal activation patterns in the somatosensory cortex feel more like an actual touch than non-biomimetic encoding schemes. This also suggests that using key elements of neuronal activity can be a useful conceptual guide to constrain the large stimulus parameter space when designing future stimulation strategies. This work is a part of Clinical Trial NCT01894802.},
}

@article {pmid40106847,
year = {2025},
author = {Wen, B and Su, L and Zhang, Y and Wang, A and Zhao, H and Wu, J and Gan, Z and Zhang, L and Kang, X},
title = {Fabrication of Micro-Wire Stent Electrode as a Minimally Invasive Endovascular Neural Interface for Vascular Electrocorticography Using Laser Ablation Method.},
journal = {Biomedical physics & engineering express},
volume = {},
number = {},
pages = {},
doi = {10.1088/2057-1976/adc266},
pmid = {40106847},
issn = {2057-1976},
abstract = {Minimally invasive endovascular stent electrode is a currently emerging technology in neural engineering with minimal damage to the neural tissue. However, the typical stent electrode still requires resistive welding and is relatively large, limiting its application mainly on the large animal or thick vessels. In this study, we investigated the feasibility of laser ablation of micro-wire stent electrode with a diameter as small as 25 μm and verified it in the superior sagittal sinus (SSS) of a rat. Approach: We have developed a laser ablation technology to expose the electrode sites of the micro-wire on both sides without damaging the wire itself. During laser ablation, we applied a new method to fix and realign the micro-wires. The micro-wire stent electrode was fabricated by carefully assemble the micro-wire and stent. We tested the electrochemical performances of the electrodes as a neural interface. Finally, we deployed the stent electrode in a rat to verified the feasibility. Main result: Based on the proposed micro-wire stent electrode, we demonstrated that the stent electrode could be successfully deployed in a rat. With the benefit of the smaller design and laser fabrication technology, it can be fitted into a catheter with an inner diameter of 0.6 mm. The vascular electrocorticography can be detected during the acute recording, making it promising in the application of small animals and thin vessels. Significance: The method we proposed combines the advantages of endovascular micro-wire electrode and stent, helping make the electrodes smaller. This study provided an alternative method for deploying micro-wire electrodes into thinner vessels as an endovascular neural interface.},
}

@article {pmid40106436,
year = {2025},
author = {Wang, Y and Chen, Z and Liang, K and Wang, W and Hu, Z and Mao, Y and Liang, X and Jiang, L and Liu, Z and Ma, Z},
title = {AGO2 mediates immunotherapy failure via suppressing tumor IFN-gamma response-dependent CD8[+] T cell immunity.},
journal = {Cell reports},
volume = {44},
number = {4},
pages = {115445},
doi = {10.1016/j.celrep.2025.115445},
pmid = {40106436},
issn = {2211-1247},
abstract = {Interferon-gamma (IFN-γ), a cytokine essential for activating cellular immune responses, plays a crucial role in cancer immunosurveillance and the clinical success of immune checkpoint blockade therapy. In this study, we show that Argonaute 2 (AGO2), a key mediator in small RNA-guided gene regulation, inversely correlates with tumor responsiveness to IFN-γ and the efficacy of immunotherapy. Mechanistically, IFN-γ upregulates miR-1246 expression in tumor cells, enhancing its interaction with AGO2. This miR-1246-AGO2 complex disrupts IFN-γ-mediated signal transducer and activator of transcription 1 (STAT1) phosphorylation by stabilizing protein tyrosine phosphatase non-receptor 6 (PTPN6) mRNA, thereby suppressing the expression of downstream C-X-C motif chemokine ligands (CXCLs), IFN-stimulated genes (ISGs), and human leukocyte antigen (HLA) molecules, which collectively contribute to tumor immune evasion. In preclinical cancer models, inhibiting AGO2 with BCI-137 or targeting miR-1246 with its antagomir re-sensitizes tumor cells to IFN-γ, leading to the enhanced recruitment, activation, and cytotoxicity of CD8[+] T cells and ultimately improving immunotherapy efficacy.},
}

@article {pmid40104767,
year = {2025},
author = {Memmott, T and Klee, D and Smedemark-Margulies, N and Oken, B},
title = {Artifact filtering application to increase online parity in a communication BCI: progress toward use in daily-life.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1551214},
pmid = {40104767},
issn = {1662-5161},
abstract = {A significant challenge in developing reliable Brain-Computer Interfaces (BCIs) is the presence of artifacts in the acquired brain signals. These artifacts may lead to erroneous interpretations, poor fitting of models, and subsequent reduced online performance. Furthermore, BCIs in a home or hospital setting are more susceptible to environmental noise. Artifact handling procedures aim to reduce signal interference by filtering, reconstructing, and/or eliminating unwanted signal contaminants. While straightforward conceptually and largely undisputed as essential, suitable artifact handling application in BCI systems remains unsettled and may reduce performance in some cases. A potential confound that remains unexplored in the majority of BCI studies using these procedures is the lack of parity with online usage (e.g., online parity). This manuscript compares classification performance between frequently used offline digital filtering, using the whole dataset, and an online digital filtering approach where the segmented data epochs that would be used during closed-loop control are filtered instead. In a sample of healthy adults (n = 30) enrolled in a BCI pilot study to integrate new communication interfaces, there were significant benefits to model performance when filtering with online parity. While online simulations indicated similar performance across conditions in this study, there appears to be no drawback to the approach with greater online parity.},
}

@article {pmid40103837,
year = {2024},
author = {Yektaeian Vaziri, A and Makkiabadi, B},
title = {Accelerated algorithms for source orientation detection and spatiotemporal LCMV beamforming in EEG source localization.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1505017},
pmid = {40103837},
issn = {1662-4548},
abstract = {This paper illustrates the development of two efficient source localization algorithms for electroencephalography (EEG) data, aimed at enhancing real-time brain signal reconstruction while addressing the computational challenges of traditional methods. Accurate EEG source localization is crucial for applications in cognitive neuroscience, neurorehabilitation, and brain-computer interfaces (BCIs). To make significant progress toward precise source orientation detection and improved signal reconstruction, we introduce the Accelerated Linear Constrained Minimum Variance (ALCMV) beamforming toolbox and the Accelerated Brain Source Orientation Detection (AORI) toolbox. The ALCMV algorithm speeds up EEG source reconstruction by utilizing recursive covariance matrix calculations, while AORI simplifies source orientation detection from three dimensions to one, reducing computational load by 66% compared to conventional methods. Using both simulated and real EEG data, we demonstrate that these algorithms maintain high accuracy, with orientation errors below 0.2% and signal reconstruction accuracy within 2%. These findings suggest that the proposed toolboxes represent a substantial advancement in the efficiency and speed of EEG source localization, making them well-suited for real-time neurotechnological applications.},
}

@article {pmid40102969,
year = {2025},
author = {Jeong, H and Song, M and Jang, SH and Kim, J},
title = {Investigating the cortical effect of false positive feedback on motor learning in motor imagery based rehabilitative BCI training.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {22},
number = {1},
pages = {61},
pmid = {40102969},
issn = {1743-0003},
support = {2022 R1 A2 C1008150//National Research Foundation of Korea/ ; NRCTR-EX23004//Translational Research Program for Rehabilitation Robots/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; *Stroke Rehabilitation/methods ; Female ; Adult ; *Electroencephalography/methods ; *Learning/physiology ; *Imagination/physiology ; Middle Aged ; Neuronal Plasticity/physiology ; Young Adult ; Motor Cortex/physiology ; Spectroscopy, Near-Infrared/methods ; Neurological Rehabilitation/methods ; },
abstract = {BACKGROUND: Motor imagery-based brain-computer interface (MI-BCI) is a promising solution for neurorehabilitation. Many studies proposed that reducing false positive (FP) feedback is crucial for inducing neural plasticity by BCI technology. However, the effect of FP feedback on cortical plasticity induction during MI-BCI training is yet to be investigated.

OBJECTIVE: This study aims to validate the hypothesis that FP feedback affects the cortical plasticity of the user's MI during MI-BCI training by first comparing two different asynchronous MI-BCI paradigms (with and without FP feedback), and then comparing its effectiveness with that of conventional motor learning methods (passive and active training).

METHODS: Twelve healthy volunteers and four patients with stroke participated in the study. We implemented two electroencephalogram-driven asynchronous MI-BCI systems with different feedback conditions. The feedback was provided by a hand exoskeleton robot performing hand open/close task. We assessed the hemodynamic responses in two different feedback conditions and compared them with two conventional motor learning methods using functional near-infrared spectroscopy with an event-related design. The cortical effects of FP feedback were analyzed in different paradigms, as well as in the same paradigm via statistical analysis.

RESULTS: The MI-BCI without FP feedback paradigm induced higher cortical activation in MI, focusing on the contralateral motor area, compared to the paradigm with FP feedback. Additionally, within the same paradigm providing FP feedback, the task period immediately following FP feedback elicited a lower hemodynamic response in the channel located over the contralateral motor area compared to the MI-BCI paradigm without FP feedback (p = 0.021 for healthy people; p = 0.079 for people with stroke). In contrast, task trials where there was no FP feedback just before showed a higher hemodynamic response, similar to the MI-BCI paradigm without FP feedback (p = 0.099 for healthy people, p = 0.084 for people with stroke).

CONCLUSIONS: FP feedback reduced cortical activation for the users during MI-BCI training, suggesting a potential negative effect on cortical plasticity. Therefore, minimizing FP feedback may enhance the effectiveness of rehabilitative MI-BCI training by promoting stronger cortical activation and plasticity, particularly in the contralateral motor area.},
}

Humans
*Brain-Computer Interfaces
Male
*Stroke Rehabilitation/methods
Female
Adult
*Electroencephalography/methods
*Learning/physiology
*Imagination/physiology
Middle Aged
Neuronal Plasticity/physiology
Young Adult
Motor Cortex/physiology
Spectroscopy, Near-Infrared/methods
Neurological Rehabilitation/methods

@article {pmid40102931,
year = {2025},
author = {Wu, X and Hu, Z and Yue, H and Wang, C and Li, J and Yang, Y and Luan, Z and Wang, L and Shen, Y and Gu, Y},
title = {Enhancing myelinogenesis through LIN28A rescues impaired cognition in PWMI mice.},
journal = {Stem cell research & therapy},
volume = {16},
number = {1},
pages = {141},
pmid = {40102931},
issn = {1757-6512},
support = {2017YFA0104200//National Key Research and Development Program of China/ ; 32071021//National Natural Science Foundation of China/ ; 32225021//China National Funds for Distinguished Young Scientists/ ; },
mesh = {Animals ; *RNA-Binding Proteins/metabolism/genetics ; Mice ; *Myelin Sheath/metabolism ; Cell Differentiation ; White Matter/metabolism/pathology ; Oligodendrocyte Precursor Cells/metabolism ; Mice, Knockout ; Cognition ; Oligodendroglia/metabolism ; Disease Models, Animal ; },
abstract = {BACKGROUND: In premature newborn infants, preterm white matter injury (PWMI) causes motor and cognitive disabilities. Accumulating evidence suggests that PWMI may result from defected differentiation of oligodendrocyte precursor cells (OPCs) and impaired maturation of oligodendrocytes. However, the underlying mechanisms remain unclear.

METHODS: Using RNAscope, we analyzed the expression level of RNA-binding protein LIN28A in individual OPCs. Knockout of one or both alleles of Lin28a in OPCs was achieved by administrating tamoxifen to NG2[CreER]::Ai14::Lin28a[flox/+] or NG2[CreER]::Ai14::Lin28a[flox/flox] mice. Lentivirus expressing FLEX-Lin28a was used in NG2[CreER] mice to overexpress LIN28A in OPCs. A series of behavioral tests were performed to assess the cognitive functions of mice. Two-tailed unpaired t-tests was carried out for statistical analysis between groups.

RESULTS: We found that the expression of Lin28a was decreased in OPCs in a PWMI mouse model. Knockout of one or both alleles of Lin28a in OPCs postnatally resulted in reduced OPC differentiation, decreased myelinogenesis and impaired cognitive functions. Supplementing LIN28A in OPCs postnatally was able to promote OPC differentiation and enhance myelinogenesis, thus rescuing the cognitive functions in PWMI mice.

CONCLUSION: Our study reveals that LIN28A is critical in regulating postnatal myelinogenesis. Overexpression of LIN28A in OPCs rescues cognitive deficits in PWMI mice by promoting myelinogenesis, thus providing a potential strategy for the treatment of PWMI.},
}

Animals
*RNA-Binding Proteins/metabolism/genetics
Mice
*Myelin Sheath/metabolism
Cell Differentiation
White Matter/metabolism/pathology
Oligodendrocyte Precursor Cells/metabolism
Mice, Knockout
Cognition
Oligodendroglia/metabolism
Disease Models, Animal

@article {pmid40101709,
year = {2025},
author = {Chen, Q and Zhu, L and Zhang, S and Qiao, S and Ding, ZJ and Zheng, SJ and Guo, J and Su, N},
title = {Structures and mechanisms of the ABC transporter ABCB1 from Arabidopsis thaliana.},
journal = {Structure (London, England : 1993)},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.str.2025.02.008},
pmid = {40101709},
issn = {1878-4186},
abstract = {The Arabidopsis thaliana auxin transporter ABCB1 plays a fundamental role in the regulation of plant growth and development. While its homolog ABCB19 was previously shown to transport brassinosteroids (BR), another class of essential hormones, the ability of ABCB1 to mediate BR transport has remained unexplored. In this study we show that ABCB1 also transports brassinosteroids with an in vitro brassinolide (BL) transport assay. Using single-particle cryo-electron microscopy, we determined ABCB1 structures in multiple inward-facing conformations in the apo state, ANP-bound state, BL-bound state, and the both BL- and ANP-bound state. BL binds to the large cavity of two transmembrane domains, inducing a slight conformational change. Additionally, we obtained the structure of ABCB1 in an outward-facing conformation. By comparing these different conformations, we elucidated the possible mechanism of hormone transport by ABCB1. These high-resolution structures help us to understand the structural basis for hormone recognition and transport mechanisms of ABCB1.},
}

@article {pmid40101581,
year = {2025},
author = {Abdelaty, MM and Rushdi, MA and Rasmy, ME and Annaby, MH},
title = {Graph vertex and spectral features for EEG-based motor imagery classification.},
journal = {Computers in biology and medicine},
volume = {189},
number = {},
pages = {109944},
doi = {10.1016/j.compbiomed.2025.109944},
pmid = {40101581},
issn = {1879-0534},
abstract = {Motor imagery (MI) patterns play a vital role in brain-computer interface (BCI) systems, enabling control of external devices without relying on peripheral nerves or muscles. These patterns are typically classified by analyzing the associated electroencephalogram (EEG) signals. In this work, we introduce a novel MI classification approach based on multilevel graph-theoretic modeling of multichannel EEG signals. Multivariate autoregressive modeling and coherence analysis are firstly employed to construct directed graph signals to represent the relationships among EEG channels and capture the complex correlations inherent in MI patterns. Spatial graph vertex features are thus extracted as well as graph Fourier transform coefficients. Moreover, multilevel generalizations of vertex-domain features are thus defined where edges of graph signals are pruned according to different thresholds, vertex features are extracted for each threshold level, and then all features are combined into a multilevel hierarchical graph descriptor. These graph-theoretic descriptors could be fused with different variants of common spatial patterns for improved discriminability on MI classification tasks. Different feature combinations are used to train k-nearest neighbor classifiers, support vector machines, and random forests for MI pattern classification. The proposed method demonstrates competitive performance compared to the FWCSP and SCSP methods on Dataset 2a of the BCI Competition IV, as well as robust results on Dataset 1 from the same competition. Overall, the findings highlight the potential of multilevel spatial and spectral graph features in leveraging the correlation among EEG channels towards enhanced MI classification performance.},
}

@article {pmid40101362,
year = {2025},
author = {Arpaia, P and Esposito, A and Galasso, E and Galdieri, F and Natalizio, A},
title = {A wearable brain-computer interface to play an endless runner game by self-paced motor imagery.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adc205},
pmid = {40101362},
issn = {1741-2552},
abstract = {OBJECTIVE: A wearable brain-computer interface is proposed and validated experimentally in relation to the real-time control of an endless runner game by self-paced motor imagery.

APPROACH: Electroencephalographic signals were recorded via eight wet electrodes. The processing pipeline involved a filter-bank common spatial pattern approach and the combination of three binary classifiers exploiting linear discriminant analysis. This enabled the discrimination between imagining lefthand, right-hand, and no movement. Each mental task corresponded to an avatar horizontal motion within the game. Twenty-three healthy subjects participated to the experiments and their data are made publicly available. A custom metric was proposed to assess avatar control performance during the gaming phase. The game consisted of two levels and, after each of them, participants completed a questionnaire to self-assess their engagement and gaming experience.

MAIN RESULTS: The mean classification accuracies resulted 73 %, 73 %, and 67% for left-rest, right-rest, and left-right discrimination, respectively. In the gaming phase, subjects with higher accuracies for left-rest and right-rest pair exhibited higher performance in terms of the custom metric. Correlation of the offline and real-time performance was investigated. The left-right motor imagery did not result correlate to the gaming phase performance due to the poor mean accuracy of the calibration. Finally, the engagement questionnaires revealed that level 1 and level 2 were not perceived as frustrating despite the increasing difficulty.

SIGNIFICANCE: The work contributes to the development of wearable and self-paced interfaces for real-time control. These enhance user experience by guaranteeing a more natural interaction with respect to synchronous neural interfaces. Moving beyond the usage of benchmark datasets, the work paves the way to future applications on mobile devices for everyday use.},
}

@article {pmid40101262,
year = {2025},
author = {Choubey, C and Dhanalakshmi, M and Karunakaran, S and Londhe, GV and Vimal, V and Kirubakaran, MK},
title = {Optimizing Bioimaging: Quantum Computing-Inspired Bald Eagle Search Optimization for Motor Imaging EEG Feature Selection.},
journal = {Clinical EEG and neuroscience},
volume = {},
number = {},
pages = {15500594251325273},
doi = {10.1177/15500594251325273},
pmid = {40101262},
issn = {2169-5202},
abstract = {One of the most important objectives in brain-computer interfaces (BCI) is to identify a subset of characteristics that represents the electroencephalographic (EEG) signal while eliminating elements that are duplicate or irrelevant. Neuroscientific research is advanced by bioimaging, especially in the field of BCI. In this work, a novel quantum computing-inspired bald eagle search optimization (QC-IBESO) method is used to improve the effectiveness of motor imagery EEG feature selection. This method can prevent the dimensionality curse and improve the classification accuracy of the system by lowering the dimensionality of the dataset. The dataset that was used in the assessment is from BCI Competition-III IV-A. To normalize the EEG data, Z-score normalization is used in the preprocessing stage. Principal component analysis reduces dimensionality and preserves important information during feature extraction. In the context of motor imagery, the QC-IBESO approach is utilized to select certain EEG characteristics for bioimaging. This facilitates the exploration of intricate search spaces and improves the detection of critical EEG signals related to motor imagery. The study contrasts the suggested approach with conventional methods like neural networks, support vector machines and logistic regression. To evaluate the efficacy of the suggested strategy in contrast to current techniques, performance measures such as F1-score, precision, accuracy and recall are computed. This work advances the field of feature selection techniques in bioimaging and opens up a novel and intriguing direction for the investigation of quantum-inspired optimization in neuroimaging.},
}

@article {pmid40100843,
year = {2025},
author = {Russo, JS and Mahoney, T and Kokorin, K and Reynolds, A and Lin, CS and John, SE and Grayden, DB},
title = {Towards developing brain-computer interfaces for people with Multiple Sclerosis.},
journal = {PloS one},
volume = {20},
number = {3},
pages = {e0319811},
doi = {10.1371/journal.pone.0319811},
pmid = {40100843},
issn = {1932-6203},
mesh = {Humans ; *Brain-Computer Interfaces ; *Multiple Sclerosis/physiopathology/psychology ; Female ; Male ; Adult ; Middle Aged ; Surveys and Questionnaires ; Electroencephalography ; },
abstract = {BACKGROUND: Multiple Sclerosis (MS) can be a severely disabling condition that leads to various neurological symptoms. A Brain-Computer Interface (BCI) may substitute some lost function; however, there is a lack of BCI research in people with MS. Present BCI designs have also overlooked the unique pathological changes associated with MS and have not considered needs of users within their home environments. To progress this research area effectively and efficiently, we aimed to evaluate user needs and assess the feasibility and user-centric requirements of a BCI for people with MS. We hypothesised that (i) people with MS would be interested in adopting BCI technology and (ii) those with reduced independence would prefer a higher-performing invasive BCI.

METHODS: We conducted an online survey of people with MS to describe user preferences and establish the initial steps of user-centred design. The survey aimed to understand their interest in BCI applications, bionic applications, device preferences, and development considerations and related these to symptoms and assistance needs.

RESULTS: We demonstrated widespread interest for BCI applications in all stages of MS, with a preference for a non-invasive (n = 12) or minimally invasive (n = 15) BCI over carer assistance (n = 6). Descriptive analysis indicated that level of independence did not influence preference towards the higher performing but highly invasive BCI.

CONCLUSIONS: The needs of end users reported in this study are crucial for efficient development of BCI systems that can be effectively translated into the home environment. Considering the potential to enhance independence and quality of life for people living with MS, the results emphasise the importance of user-centred design for future advancement of BCIs that account for the unique pathological changes associated with MS.},
}

Humans
*Brain-Computer Interfaces
*Multiple Sclerosis/physiopathology/psychology
Female
Male
Adult
Middle Aged
Surveys and Questionnaires
Electroencephalography

@article {pmid40100695,
year = {2025},
author = {Guo, Z and Xu, L and Tan, W and Chen, F},
title = {Impact of Generation Rate of Speech Imagery on Neural Activity and BCI Decoding Performance: A fNIRS Study.},
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.2025.3552606},
pmid = {40100695},
issn = {1558-0210},
abstract = {Brain-computer interface (BCI) enables stroke patients to actively modulate neural activity, fostering neuroplasticity and thereby accelerating the recovery process. Due to being portable, non-invasive, and safe, functional near-infrared spectroscopy (fNIRS) has become one of the most widely used neuroimaging techniques. Current BCI research primarily focuses on improving the decoding performance. However, a key aspect of stroke rehabilitation lies in inducing stronger cortical activations in the damaged brain areas, thereby accelerating the recovery of brain functions. This study investigated the regulatory mechanism of the generation rate of speech imagery on neural activity and its impact on BCI decoding performance based on fNIRS. As the generation rate increased from 1 word/4 s to 1 word/2 s, and finally to 1 word/1 s, neural activity in speech-related brain regions steadily enhanced. Correspondingly, the accuracy of detecting speech imagery tasks increased from 83.83% to 85.39%, and ultimately showed a significant improvement, reaching 88.28%. Additionally, the differences in neural activities between the "yes" and "no" speech imagery tasks became more pronounced as the generation rate increased, leading to an improvement in classification performance from 62.81% to 65.78%, and ultimately to 67.50%. This study demonstrates that the neural activity level of most speech-related brain regions during speech imagery enhanced as the generation rate increased. Therefore, accelerating the generation rate of speech imagery induces stronger neural activity and more distinct response patterns between different tasks, which holds the potential to facilitate the development of a BCI feedback system with higher neuroplasticity induction and improved decoding performance.},
}

@article {pmid40100694,
year = {2025},
author = {Yan, W and Lin, Y and Chen, YF and Wang, Y and Wang, J and Zhang, M},
title = {Enhancing Neuroplasticity for Post-Stroke Motor Recovery: Mechanisms, Models, and Neurotechnology.},
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.2025.3551753},
pmid = {40100694},
issn = {1558-0210},
abstract = {Stroke remains a significant global health challenge, imposing substantial socioeconomic burdens. Post-stroke neurorehabilitation aims to maximize functional recovery and mitigate persistent disability through effective neuromodulation, while many patients experience prolonged recovery periods with suboptimal outcomes. This review explores innovative neurotechnologies and therapeutic strategies enhancing neuroplasticity for post-stroke motor recovery, with a particular focus on the subacute and chronic phases. We examine key neuroplasticity mechanisms and rehabilitation models informing neurotechnology use, including the vicariation model, the interhemispheric competition model, and the bimodal balance-recovery model. Building on these theoretical foundations, current neurotechnologies are categorized into endogenous drivers of neuroplasticity (e.g., task-oriented training, brain-computer interfaces) and exogenous drivers (e.g., brain stimulation, muscular electrical stimulation, robot-assisted passive movement). However, most approaches lack tailored adjustments combining volitional behavior with brain neuromodulation. Given the heterogeneous effects of current neurotechnologies, we propose that future directions should focus on personalized rehabilitation strategies and closed-loop neuromodulation. These advanced approaches may provide deeper insights into neuroplasticity and potentially expand recovery possibilities for stroke patients.},
}

@article {pmid40100553,
year = {2025},
author = {Zhou, H and Yan, ZN and Gao, WH and Lv, XX and Luo, R and Hoellwarth, JS and He, L and Yang, JM and Zhang, JY and Wang, HL and Xie, Y and Chen, XL and Xue, MD and Fang, Y and Duan, YY and Li, RY and Wang, XD and Wang, RL and Xie, M and Huang, L and Liu, PR and Ye, ZW},
title = {Construction of a Multimodal 3D Atlas for a Micrometer-Scale Brain-Computer Interface Based on Mixed Reality.},
journal = {Current medical science},
volume = {},
number = {},
pages = {},
pmid = {40100553},
issn = {2523-899X},
support = {No.82172524//the National Natural Science Foundation of China/ ; No.81974355//the National Natural Science Foundation of China/ ; No.2020021105012440//National Innovation Platform Development Program/ ; 2021BEA161//Major Program of Hubei Province/ ; JD2023BAA005//Major Key Project of Hubei Province/ ; No.2024XHYN047//Wuhan Union Hospital Free Innovation Preliminary Research Fund/ ; },
abstract = {OBJECTIVE: To develop a multimodal imaging atlas of a rat brain-computer interface (BCI) that incorporates brain, arterial, bone tissue and a BCI device using mixed reality (MR) for three-dimensional (3D) visualization.

METHODS: An invasive BCI was implanted in the left visual cortex of 4-week-old Sprague-Dawley rats. Multimodal imaging techniques, including micro-CT and 9.0 T MRI, were used to acquire images of the rat cranial bone structure, vascular distribution, brain tissue functional zones, and BCI device before and after implantation. Using 3D-slicer software, the images were fused through spatial transformations, followed by image segmentation and 3D model reconstruction. The HoloLens platform was employed for MR visualization.

RESULTS: This study constructed a multimodal imaging atlas for rats that included the skull, brain tissue, arterial tissue, and BCI device coupled with MR technology to create an interactive 3D anatomical model.

CONCLUSIONS: This multimodal 3D atlas provides an objective and stable reference for exploring complex relationships between brain tissue structure and function, enhancing the understanding of the operational principles of BCIs. This is the first multimodal 3D imaging atlas related to a BCI created using Sprague-Dawley rats.},
}

@article {pmid40100543,
year = {2025},
author = {Gao, J and Tang, H and Wang, Z and Li, Y and Luo, N and Song, M and Xie, S and Shi, W and Yan, H and Lu, L and Yan, J and Li, P and Song, Y and Chen, J and Chen, Y and Wang, H and Liu, W and Li, Z and Guo, H and Wan, P and Lv, L and Yang, Y and Wang, H and Zhang, H and Wu, H and Ning, Y and Zhang, D and Jiang, T},
title = {Graph Neural Networks and Multimodal DTI Features for Schizophrenia Classification: Insights from Brain Network Analysis and Gene Expression.},
journal = {Neuroscience bulletin},
volume = {},
number = {},
pages = {},
pmid = {40100543},
issn = {1995-8218},
abstract = {Schizophrenia (SZ) stands as a severe psychiatric disorder. This study applied diffusion tensor imaging (DTI) data in conjunction with graph neural networks to distinguish SZ patients from normal controls (NCs) and showcases the superior performance of a graph neural network integrating combined fractional anisotropy and fiber number brain network features, achieving an accuracy of 73.79% in distinguishing SZ patients from NCs. Beyond mere discrimination, our study delved deeper into the advantages of utilizing white matter brain network features for identifying SZ patients through interpretable model analysis and gene expression analysis. These analyses uncovered intricate interrelationships between brain imaging markers and genetic biomarkers, providing novel insights into the neuropathological basis of SZ. In summary, our findings underscore the potential of graph neural networks applied to multimodal DTI data for enhancing SZ detection through an integrated analysis of neuroimaging and genetic features.},
}

@article {pmid40099835,
year = {2025},
author = {Jin, J and Xiao, Q and Liu, Y and Xu, T and Shen, Q},
title = {Test-retest reliability of decisions under risk with outcome evaluation: evidence from behavioral and event-related potentials (ERPs) measures in 2 monetary gambling tasks.},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
volume = {35},
number = {3},
pages = {},
doi = {10.1093/cercor/bhaf058},
pmid = {40099835},
issn = {1460-2199},
support = {2022KFKT005//Open Research Fund/ ; 22dz2261100//Shanghai Key Laboratory of Brain-Machine Intelligence for Information Behavior/ ; 23NDJC124YB//Zhejiang Province Philosophy and Social Science Planning Project/ ; 41005067//Fundamental Research Funds for the Central Universities/ ; 72371165//National Natural Science Foundation of China/ ; },
mesh = {Humans ; Male ; Female ; Young Adult ; *Gambling/psychology/physiopathology ; *Electroencephalography ; *Risk-Taking ; *Evoked Potentials/physiology ; *Decision Making/physiology ; Reproducibility of Results ; Adult ; *Reward ; Event-Related Potentials, P300/physiology ; Brain/physiology ; },
abstract = {The balance between potential gains and losses under risk, the stability of risk propensity, the associated reward processing, and the prediction of subsequent risk behaviors over time have become increasingly important topics in recent years. In this study, we asked participants to carry out 2 risk tasks with outcome evaluation-the monetary gambling task and mixed lottery task twice, with simultaneous recording of behavioral and electroencephalography data. Regarding risk behavior, we observed both individual-specific risk attitudes and outcome-contingent risky inclination following a loss outcome, which remained stable across sessions. In terms of event-related potential (ERP) results, low outcomes, compared to high outcomes, induced a larger feedback-related negativity, which was modulated by the magnitude of the outcome. Similarly, high outcomes evoked a larger deflection of the P300 compared to low outcomes, with P300 amplitude also being sensitive to outcome magnitude. Intraclass correlation coefficient analyses indicated that both the feedback-related negativity and P300 exhibited modest to good test-retest reliability across both tasks. Regarding choice prediction, we found that neural responses-especially those following a loss outcome-predicted subsequent risk-taking behavior at the single-trial level for both tasks. Therefore, this study extends our understanding of the reliability of risky preferences in gain-loss trade-offs.},
}

Humans
Male
Female
Young Adult
*Gambling/psychology/physiopathology
*Electroencephalography
*Risk-Taking
*Evoked Potentials/physiology
*Decision Making/physiology
Reproducibility of Results
Adult
*Reward
Event-Related Potentials, P300/physiology
Brain/physiology

@article {pmid40096442,
year = {2025},
author = {Shi, X and Zhai, X and Wang, R and Le, Y and Fu, S and Liu, N},
title = {Task Planning of Multiple Unmanned Aerial Vehicles Based on Minimum Cost and Maximum Flow.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {5},
pages = {},
pmid = {40096442},
issn = {1424-8220},
abstract = {With the rapid development of UAV technology, UAV delivery has gained attention for its potential to reduce labor costs. However, limitations in load capacity and energy restrict UAVs' distribution capabilities. This paper proposes a cooperative delivery scheme combining traditional trucks and UAVs to extend UAV coverage and improve delivery completion rates. For densely distributed depots in wide-area regions, we develop algorithms for task allocation and path planning in a truck-independent UAV system. Specifically, a minimum-cost, maximum-flow model is constructed to obtain sub-paths covering all delivery tasks, and resource tree-based algorithms are used to construct global paths for UAVs and trucks. Simulation results show that our algorithms reduce total energy consumption by 11.53% and 9.15% under different task points, which suggests that our proposed method can significantly enhance delivery efficiency, offering a promising solution for future logistics operations.},
}

@article {pmid40096214,
year = {2025},
author = {Bouchane, M and Guo, W and Yang, S},
title = {Hybrid CNN-GRU Models for Improved EEG Motor Imagery Classification.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {5},
pages = {},
pmid = {40096214},
issn = {1424-8220},
support = {236Z0105G//Hebei Central Leading Local Science and Technology Development Foundation/ ; },
mesh = {*Electroencephalography/methods ; Humans ; *Brain-Computer Interfaces ; *Neural Networks, Computer ; Imagination/physiology ; Movement/physiology ; Signal Processing, Computer-Assisted ; Algorithms ; },
abstract = {Brain-computer interfaces (BCIs) based on electroencephalography (EEG) enable neural activity interpretation for device control, with motor imagery (MI) serving as a key paradigm for decoding imagined movements. Efficient feature extraction from raw EEG signals is essential to improve classification accuracy while minimizing reliance on extensive preprocessing. In this study, we introduce new hybrid architectures to enhance MI classification using data augmentation and a limited number of EEG channels. The first model combines a shallow convolutional neural network and a gated recurrent unit (CNN-GRU), while the second incorporates a convolutional neural network with a bidirectional gated recurrent unit (CNN-Bi-GRU). Evaluated using the publicly available PhysioNet dataset, the CNN-GRU classifier achieved peak mean accuracy rates of 99.71%, 99.73%, 99.61%, and 99.86% for tasks involving left fist (LF), right fist (RF), both fists (LRF), and both feet (BF), respectively. The experimental results provide compelling evidence that our proposed models outperform current state-of-the-art methods, underscoring their efficiency on small-scale EEG datasets. The CNN-GRU and CNN-Bi-GRU architectures exhibit superior predictive reliability, offering a faster, cost-effective solution for user-adaptable MI-BCI applications.},
}

*Electroencephalography/methods
Humans
*Brain-Computer Interfaces
*Neural Networks, Computer
Imagination/physiology
Movement/physiology
Signal Processing, Computer-Assisted
Algorithms

@article {pmid40096116,
year = {2025},
author = {González-España, JJ and Sánchez-Rodríguez, L and Pacheco-Ramírez, MA and Feng, J and Nedley, K and Chang, SH and Francisco, GE and Contreras-Vidal, JL},
title = {At-Home Stroke Neurorehabilitation: Early Findings with the NeuroExo BCI System.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {5},
pages = {},
doi = {10.3390/s25051322},
pmid = {40096116},
issn = {1424-8220},
support = {1827769//National Science Foundation/ ; 2137255//NSF Industry-University Cooperative Research Center for Building Reliable Advances and Innovations in Neurotechnology (IUCRC BRAIN)/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Stroke Rehabilitation/methods/instrumentation ; Male ; Female ; Middle Aged ; *Neurological Rehabilitation/methods/instrumentation ; *Exoskeleton Device ; Aged ; Stroke/physiopathology ; Adult ; Robotics/methods ; Electroencephalography/methods ; },
abstract = {BACKGROUND: Democratized access to safe and effective robotic neurorehabilitation for stroke survivors requires innovative, affordable solutions that can be used not only in clinics but also at home. This requires the high usability of the devices involved to minimize costs associated with support from physical therapists or technicians.

METHODS: This paper describes the early findings of the NeuroExo brain-machine interface (BMI) with an upper-limb robotic exoskeleton for stroke neurorehabilitation. This early feasibility study consisted of a six-week protocol, with an initial training and BMI calibration phase at the clinic followed by 60 sessions of neuromotor therapy at the homes of the participants. Pre- and post-assessments were used to assess users' compliance and system performance.

RESULTS: Participants achieved a compliance rate between 21% and 100%, with an average of 69%, while maintaining adequate signal quality and a positive perceived BMI performance during home usage with an average Likert scale score of four out of five. Moreover, adequate signal quality was maintained for four out of five participants throughout the protocol. These findings provide valuable insights into essential components for comprehensive rehabilitation therapy for stroke survivors. Furthermore, linear mixed-effects statistical models showed a significant reduction in trial duration (p-value < 0.02) and concomitant changes in brain patterns (p-value < 0.02).

CONCLUSIONS: the analysis of these findings suggests that a low-cost, safe, simple-to-use BMI system for at-home stroke rehabilitation is feasible.},
}

Humans
*Brain-Computer Interfaces
*Stroke Rehabilitation/methods/instrumentation
Male
Female
Middle Aged
*Neurological Rehabilitation/methods/instrumentation
*Exoskeleton Device
Aged
Stroke/physiopathology
Adult
Robotics/methods
Electroencephalography/methods

@article {pmid40096108,
year = {2025},
author = {Alexopoulou, A and Pergantis, P and Koutsojannis, C and Triantafillou, V and Drigas, A},
title = {Non-Invasive BCI-VR Applied Protocols as Intervention Paradigms on School-Aged Subjects with ASD: A Systematic Review.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {5},
pages = {},
doi = {10.3390/s25051342},
pmid = {40096108},
issn = {1424-8220},
mesh = {Humans ; *Brain-Computer Interfaces ; Child ; *Autism Spectrum Disorder/therapy ; Adolescent ; Electroencephalography/methods ; Virtual Reality ; User-Computer Interface ; Cognition/physiology ; },
abstract = {This paper aims to highlight non-invasive BCI-VR applied protocols as intervention paradigms on school-aged subjects with ASD. Computer-based interventions are considered appropriate for users with ASD as concentration on a screen reduces other stimuli from the environment that are likely to be distracting or disruptive. Since there are no social conditions for engagement in such processes and the responses of computing systems do not hold surprises for users, as the outputs are fully controlled, they are ideal for ASD subjects. Children and adolescents with ASD, when supported by BCI interventions through virtual reality applications, especially appear to show significant improvements in core symptoms, such as cognitive and social deficits, regardless of their age or IQ. We examined nine protocols applied from 2016 to 2023, focusing on the BCI paradigms, the procedure, and the outcomes. Our study is non-exhaustive but representative of the state of the art in the field. As concluded by the research, BCI-VR applied protocols have no side effects and are rather easy to handle and maintain, and despite the fact that there are research limitations, they hold promise as a tool for improving social and cognitive skills in school-aged individuals with ASD.},
}

Humans
*Brain-Computer Interfaces
Child
*Autism Spectrum Disorder/therapy
Adolescent
Electroencephalography/methods
Virtual Reality
User-Computer Interface
Cognition/physiology

@article {pmid40096020,
year = {2025},
author = {Vafaei, E and Hosseini, M},
title = {Transformers in EEG Analysis: A Review of Architectures and Applications in Motor Imagery, Seizure, and Emotion Classification.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {5},
pages = {},
doi = {10.3390/s25051293},
pmid = {40096020},
issn = {1424-8220},
mesh = {*Electroencephalography/methods ; Humans ; *Emotions/physiology ; *Seizures/physiopathology/diagnosis ; Machine Learning ; Algorithms ; Signal Processing, Computer-Assisted ; },
abstract = {Transformers have rapidly influenced research across various domains. With their superior capability to encode long sequences, they have demonstrated exceptional performance, outperforming existing machine learning methods. There has been a rapid increase in the development of transformer-based models for EEG analysis. The high volumes of recently published papers highlight the need for further studies exploring transformer architectures, key components, and models employed particularly in EEG studies. This paper aims to explore four major transformer architectures: Time Series Transformer, Vision Transformer, Graph Attention Transformer, and hybrid models, along with their variants in recent EEG analysis. We categorize transformer-based EEG studies according to the most frequent applications in motor imagery classification, emotion recognition, and seizure detection. This paper also highlights the challenges of applying transformers to EEG datasets and reviews data augmentation and transfer learning as potential solutions explored in recent years. Finally, we provide a summarized comparison of the most recent reported results. We hope this paper serves as a roadmap for researchers interested in employing transformer architectures in EEG analysis.},
}

*Electroencephalography/methods
Humans
*Emotions/physiology
*Seizures/physiopathology/diagnosis
Machine Learning
Algorithms
Signal Processing, Computer-Assisted

@article {pmid40096019,
year = {2025},
author = {Koo, BH and Siu, HC and Newman, DJ and Roche, ET and Petersen, LG},
title = {Utilization of Classification Learning Algorithms for Upper-Body Non-Cyclic Motion Prediction.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {5},
pages = {},
doi = {10.3390/s25051297},
pmid = {40096019},
issn = {1424-8220},
mesh = {Humans ; *Electromyography/methods ; *Algorithms ; Male ; Adult ; Movement/physiology ; Neural Networks, Computer ; Muscle, Skeletal/physiology ; Female ; Young Adult ; Deep Learning ; Motion ; Signal Processing, Computer-Assisted ; Machine Learning ; },
abstract = {This study explores two methods of predicting non-cyclic upper-body motions using classification algorithms. Exoskeletons currently face challenges with low fluency, hypothesized to be in part caused by the lag in active control innate in many leader-follower paradigms seen in today's systems, leading to energetic inefficiencies and discomfort. To address this, we employ k-nearest neighbor (KNN) and deep learning models to predict motion characteristics, such as magnitude and category, from surface electromyography (sEMG) signals. Data were collected from six muscles located around the elbow. The sEMG signals were processed to identify significant activation changes. Two classification approaches were utilized: a KNN algorithm that categorizes motion based on the slopes of processed sEMG signals at change points and a deep neural network employing continuous categorization. Both methods demonstrated the capability to predict future voluntary non-cyclic motions up to and beyond commonly acknowledged electromechanical delay times, with the deep learning model able to predict, with certainty at or beyond 90%, motion characteristics even prior to myoelectric activation of the muscles involved. Our findings indicate that these classification algorithms can be used to predict upper-body non-cyclic motions to potentially increase machine interfacing fluency. Further exploration into regression-based prediction models could enhance the precision of these predictions, and further work could explore their effects on fluency when utilized in a tandem or wearable robotic application.},
}

Humans
*Electromyography/methods
*Algorithms
Male
Adult
Movement/physiology
Neural Networks, Computer
Muscle, Skeletal/physiology
Female
Young Adult
Deep Learning
Motion
Signal Processing, Computer-Assisted
Machine Learning

@article {pmid40095346,
year = {2025},
author = {Li, J and Shao, N and Zhang, Y and Liu, X and Zhang, H and Tian, L and Piatkevich, KD and Zhang, D and Lee, HJ},
title = {Screening of Vibrational Spectroscopic Voltage Indicator by Stimulated Raman Scattering Microscopy.},
journal = {Small methods},
volume = {},
number = {},
pages = {e2402124},
doi = {10.1002/smtd.202402124},
pmid = {40095346},
issn = {2366-9608},
support = {82372011//National Natural Science Foundation of China/ ; 12074339//National Natural Science Foundation of China/ ; 32050410298//National Natural Science Foundation of China/ ; 32171093//National Natural Science Foundation of China/ ; LZ25H180001//Natural Science Foundation of Zhejiang Province/ ; 2025ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; 2024YFA1408900//National Key Research and Development Program of China/ ; 28961//Brain and Behavior Research Foundation/ ; },
abstract = {Genetically encoded voltage indicators (GEVIs) have significantly advanced voltage imaging, offering spatial details at cellular and subcellular levels not easily accessible with electrophysiology. In addition to fluorescence imaging, certain chemical bond vibrations are sensitive to membrane potential changes, presenting an alternative imaging strategy; however, challenges in signal sensitivity and membrane specificity highlight the need to develop vibrational spectroscopic GEVIs (vGEVIs) in mammalian cells. To address this need, a vGEVI screening approach is developed that employs hyperspectral stimulated Raman scattering (hSRS) imaging synchronized with an induced transmembrane voltage (ITV) stimulation, revealing unique spectroscopic signatures of sensors expressed on membranes. Specifically, by screening various rhodopsin-based voltage sensors in live mammalian cells, a characteristic peak associated with retinal bound to the sensor is identified in one of the GEVIs, Archon, which exhibited a 70 cm[-1] red shift relative to the membrane-bound retinal. Notably, this peak is responsive to changes in membrane potential. Overall, hSRS-ITV presents a promising platform for screening vGEVIs, paving the way for advancements in vibrational spectroscopic voltage imaging.},
}

@article {pmid40093990,
year = {2025},
author = {Yang, KC and Xu, Y and Lin, Q and Tang, LL and Zhong, JW and An, HN and Zeng, YQ and Jia, K and Jin, Y and Yu, G and Gao, F and Zhao, L and Tong, LS},
title = {Explainable deep learning algorithm for identifying cerebral venous sinus thrombosis-related hemorrhage (CVST-ICH) from spontaneous intracerebral hemorrhage using computed tomography.},
journal = {EClinicalMedicine},
volume = {81},
number = {},
pages = {103128},
pmid = {40093990},
issn = {2589-5370},
abstract = {BACKGROUND: Misdiagnosis of hemorrhage secondary to cerebral venous sinus thrombosis (CVST-ICH) as arterial-origin spontaneous intracerebral hemorrhage (sICH) can lead to inappropriate treatment and the potential for severe adverse outcomes. The current practice for identifying CVST-ICH involves venography, which, despite being increasingly utilized in many centers, is not typically used as the initial imaging modality for ICH patients. The study aimed to develop an explainable deep learning model to quickly identify ICH caused by CVST based on non-contrast computed tomography (NCCT).

METHODS: The study population included patients diagnosed with CVST-ICH and other spontaneous ICH from January 2016 to March 2023 at the Second Affiliated Hospital of Zhejiang University, Taizhou First People's Hospital, Taizhou Hospital, Quzhou Second People's Hospital, and Longyan First People's Hospital. A transfer learning-based 3D U-Net with segmentation and classification was proposed and developed only on admission plain CT. Model performance was assessed using the area under the curve (AUC), sensitivity, and specificity metrics. For further evaluation, the average diagnostic performance of nine doctors on plain CT was compared with model assistance. Interpretability methods, including Grad-CAM++, SHAP, IG, and occlusion, were employed to understand the model's attention.

FINDINGS: An internal dataset was constructed using propensity score matching based on age, initially including 102 CVST-ICH patients (median age: 44 [29, 61] years) and 683 sICH patients (median age: 65 [52, 73] years). After matching, 102 CVST-ICH patients and 306 sICH patients (median age: 50 [40, 62] years) were selected. An external dataset consisted of 38 CVST-ICH and 119 sICH patients from four other hospitals. Validation showed AUC 0·94, sensitivity 0·96, and specificity 0·8 for the internal testing subset; AUC 0·85, sensitivity 0·87, and specificity 0·82 for the external dataset, respectively. The discrimination performance of nine doctors interpreting CT images significantly improved with the assistance of the proposed model (accuracy 0·79 vs 0·71, sensitivity 0·88 vs 0·81, specificity 0·75 vs 0·68, p < 0·05). Interpretability methods highlighted the attention of model to the features of hemorrhage edge appearance.

INTERPRETATION: The present model demonstrated high-performing and robust results on discrimination between CVST-ICH and spontaneous ICH, and aided doctors' diagnosis in clinical practice as well. Prospective validation with larger-sample size is required.

FUNDING: The work was funded by the National Key R&D Program of China (2023YFE0118900), National Natural Science Foundation of China (No.81971155 and No.81471168), the Science and Technology Department of Zhejiang Province (LGJ22H180004), Medical and Health Science and Technology Project of Zhejiang Province (No.2022KY174), the 'Pioneer' R&D Program of Zhejiang (No. 2024C03006 and No. 2023C03026) and the MOE Frontier Science Center for Brain Science & Brain-Machine Integration, Zhejiang University.},
}

@article {pmid40092993,
year = {2025},
author = {Wen, J and Li, Y and Deng, W and Li, Z},
title = {Central nervous system and immune cells interactions in cancer: unveiling new therapeutic avenues.},
journal = {Frontiers in immunology},
volume = {16},
number = {},
pages = {1528363},
pmid = {40092993},
issn = {1664-3224},
mesh = {Humans ; *Neoplasms/immunology/therapy/drug therapy ; *Central Nervous System/immunology ; *Tumor Microenvironment/immunology ; Animals ; Cell Communication/immunology ; Signal Transduction ; Neuroimmunomodulation ; Immunotherapy/methods ; Immune Checkpoint Inhibitors/therapeutic use/pharmacology ; },
abstract = {Cancer remains a leading cause of mortality worldwide. Despite significant advancements in cancer research, our understanding of its complex developmental pathways remains inadequate. Recent research has clarified the intricate relationship between the central nervous system (CNS) and cancer, particularly how the CNS influences tumor growth and metastasis via regulating immune cell activity. The interactions between the central nervous system and immune cells regulate the tumor microenvironment via various signaling pathways, cytokines, neuropeptides, and neurotransmitters, while also incorporating processes that alter the tumor immunological landscape. Furthermore, therapeutic strategies targeting neuro-immune cell interactions, such as immune checkpoint inhibitors, alongside advanced technologies like brain-computer interfaces and nanodelivery systems, exhibit promise in improving treatment efficacy. This complex bidirectional regulatory network significantly affects tumor development, metastasis, patient immune status, and therapy responses. Therefore, understanding the mechanisms regulating CNS-immune cell interactions is crucial for developing innovative therapeutic strategies. This work consolidates advancements in CNS-immune cell interactions, evaluates their potential in cancer treatment strategies, and provides innovative insights for future research and therapeutic approaches.},
}

Humans
*Neoplasms/immunology/therapy/drug therapy
*Central Nervous System/immunology
*Tumor Microenvironment/immunology
Animals
Cell Communication/immunology
Signal Transduction
Neuroimmunomodulation
Immunotherapy/methods
Immune Checkpoint Inhibitors/therapeutic use/pharmacology

@article {pmid40092069,
year = {2025},
author = {Sayal, A and Direito, B and Sousa, T and Singer, N and Castelo-Branco, M},
title = {Music in the loop: a systematic review of current neurofeedback methodologies using music.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1515377},
pmid = {40092069},
issn = {1662-4548},
abstract = {Music, a universal element in human societies, possesses a profound ability to evoke emotions and influence mood. This systematic review explores the utilization of music to allow self-control of brain activity and its implications in clinical neuroscience. Focusing on music-based neurofeedback studies, it explores methodological aspects and findings to propose future directions. Three key questions are addressed: the rationale behind using music as a stimulus, its integration into the feedback loop, and the outcomes of such interventions. While studies emphasize the emotional link between music and brain activity, mechanistic explanations are lacking. Additionally, there is no consensus on the imaging or behavioral measures of neurofeedback success. The review suggests considering whole-brain neural correlates of music stimuli and their interaction with target brain networks and reward mechanisms when designing music-neurofeedback studies. Ultimately, this review aims to serve as a valuable resource for researchers, facilitating a deeper understanding of music's role in neurofeedback and guiding future investigations.},
}

@article {pmid40091139,
year = {2025},
author = {Thamaraimanalan, T and Gopal, D and Vignesh, S and Kishore Kumar, K},
title = {Exploiting adaptive neuro-fuzzy inference systems for cognitive patterns in multimodal brain signal analysis.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {9029},
pmid = {40091139},
issn = {2045-2322},
mesh = {Humans ; *Fuzzy Logic ; *Electroencephalography/methods ; *Brain/physiology ; *Cognition/physiology ; *Principal Component Analysis ; Neural Networks, Computer ; Signal Processing, Computer-Assisted ; Algorithms ; },
abstract = {The analysis of cognitive patterns through brain signals offers critical insights into human cognition, including perception, attention, memory, and decision-making. However, accurately classifying these signals remains a challenge due to their inherent complexity and non-linearity. This study introduces a novel method, PCA-ANFIS, which integrates Principal Component Analysis (PCA) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS), to enhance cognitive pattern recognition in multimodal brain signal analysis. PCA reduces the dimensionality of EEG data while retaining salient features, enabling computational efficiency. ANFIS combines the adaptability of neural networks with the interpretability of fuzzy logic, making it well-suited to model the non-linear relationships within brain signals. Performance metrics of our proposed method, such as accuracy, sensitivity, and computational efficiency. These additions highlight the effectiveness of the method and provide a concise summary of the findings. The proposed method achieves superior classification performance, with an unprecedented accuracy of 99.5%, significantly outperforming existing approaches. Comprehensive experiments were conducted using a diverse multimodal EEG dataset, demonstrating the method's robustness and sensitivity. The integration of PCA and ANFIS addresses key challenges in multimodal brain signal analysis, such as EEG artifact contamination and non-stationarity, ensuring reliable feature extraction and classification. This research has significant implications for both cognitive neuroscience and clinical practice. By advancing the understanding of cognitive processes, the PCA-ANFIS method facilitates accurate diagnosis and treatment of cognitive disorders and neurological conditions. Future work will focus on testing the approach with larger and more diverse datasets and exploring its applicability in domains such as neurofeedback, neuromarketing, and brain-computer interfaces. This study establishes PCA-ANFIS as a capable tool for the precise and efficient classification of cognitive patterns in brain signal processing.},
}

Humans
*Fuzzy Logic
*Electroencephalography/methods
*Brain/physiology
*Cognition/physiology
*Principal Component Analysis
Neural Networks, Computer
Signal Processing, Computer-Assisted
Algorithms

@article {pmid40089693,
year = {2025},
author = {Khamisa, N and Madala, S and Fonka, CB},
title = {Burnout among South African nurses during the peak of COVID-19 pandemic: a holistic investigation.},
journal = {BMC nursing},
volume = {24},
number = {1},
pages = {290},
pmid = {40089693},
issn = {1472-6955},
abstract = {BACKGROUND: The wellbeing of health care workers (HCWs) has been an ongoing challenge, especially within low and middle-income countries (LMICs) such as South Africa. Evidence suggesting that HCWs are increasingly stressed and burned out is cause for concern. Nurses in particular have been impacted physically, mentally and psychosocially during the recent COVID-19 pandemic. This may leave a disproportionate consequence, affecting various aspects of their wellbeing, thereby justifying a need for a more holistic investigation of the wellbeing of South African nurses and their coping mechanisms during the peak of the pandemic.

METHODS: This was a cross-sectional study design. Online self-reported questionnaires were administered in six hospitals, sampled purposively and conveniently from three South African provinces. Using STATA 18.0, the Wilcoxon Ranksum test at 5% alpha compared the wellbeing and coping mechanisms of nursing staff and nursing management during COVID-19's peak. Univariable and multivariable linear regression analyses were performed to determine factors associated with burnout in nurses, at a 95% confidence interval (CI). Validated scales measuring burnout, coping, resilience, as well as mental and physical health were utilised.

RESULTS: Of 139 participants, 112(97.4%) were females, with 91(82%) and 20(18%) being nursing staff and management respectively. The median age of the participants was 43.3 years (n = 112), with a practising duration of 12 years (n = 111). There was a significant difference in the burnout score between nursing staff and nursing management (p = 0.028). In the univariable linear regression model, burnout was significantly (p < 0.05) associated with the Brief COPE Inventory (BCI), Conor-Davidson Resilience Scale (CDRS), Global Mental and Health Scale (GMHS), Global Physical and Health Scale (GPHS) and Hospital Anxiety and Depression Scale (HADS), as well as occupation. In the multivariable linear regression model, burnout was significantly associated with the CDRS [Coeff.=0.7, 95%CI 0.4; 0.9], GMHS [Coeff.=-2.4, 95%CI -3.2; -1.6], GPHS [Coeff.2.1, 95%CI 1.3; 2.9], and HADS [Coeff.=0.7, 95%CI 0.2; 1.2].

CONCLUSION: Investigating multiple aspects of wellbeing in this study, it's shown that coping and resilience may not be key factors in promoting the wellbeing of South African nurses. However, effective mental health interventions are crucial and should be prioritised to mitigate burnout during future health emergencies. Future studies examining the associations between general health, coping and resilience may help generate further evidence towards holistic interventions aimed at promoting nurses' wellbeing.

CLINICAL TRIAL NUMBER: Not applicable.},
}

@article {pmid40089573,
year = {2025},
author = {Sivasakthivel, R and Rajagopal, M and Anitha, G and Loganathan, K and Abbas, M and Ksibi, A and Rao, KS},
title = {Simulating online and offline tasks using hybrid cheetah optimization algorithm for patients affected by neurodegenerative diseases.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {8951},
pmid = {40089573},
issn = {2045-2322},
mesh = {Humans ; *Algorithms ; Female ; *Neurodegenerative Diseases/physiopathology ; Male ; *Brain-Computer Interfaces ; Adult ; Neural Networks, Computer ; Electroencephalography/methods ; Middle Aged ; },
abstract = {Brain-Computer Interface (BCI) is a versatile technique to offer better communication system for people affected by the locked-in syndrome (LIS).In the current decade, there has been a growing demand for improved care and services for individuals with neurodegenerative diseases. To address this barrier, the current work is designed with four states of BCI for paralyzed persons using Welch Power Spectral Density (W-PSD). The features extracted from the signals were trained with a hybrid Feed Forward Neural Network Cheetah Optimization Algorithm (FFNNCOA) in both offline and online modes. Totally, eighteen subjects were involved in this study. The study proved that the offline analysis phase outperformed than the online phase in the real-time. The experiment was achieved the accuracies of 95.56% and 93.88% for men and female respectively. Furthermore, the study confirms that the subject's performance in the offline can manage the task more easily than in online mode.},
}

Humans
*Algorithms
Female
*Neurodegenerative Diseases/physiopathology
Male
*Brain-Computer Interfaces
Adult
Neural Networks, Computer
Electroencephalography/methods
Middle Aged

@article {pmid40087270,
year = {2025},
author = {Eby, J and Beutel, M and Koivisto, D and Achituve, I and Fetaya, E and Zariffa, J},
title = {Electromyographic typing gesture classification dataset for neurotechnological human-machine interfaces.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {440},
pmid = {40087270},
issn = {2052-4463},
mesh = {Humans ; *Electromyography ; *Gestures ; Brain-Computer Interfaces ; },
abstract = {Neurotechnological interfaces have the potential to create new forms of human-machine interactions, by allowing devices to interact directly with neurological signals instead of via intermediates such as keystrokes. Surface electromyography (sEMG) has been used extensively in myoelectric control systems, which use bioelectric activity recorded from muscles during contractions to classify actions. This technology has been used primarily for rehabilitation applications. In order to support the development of myoelectric interfaces for a broader range of human-machine interactions, we present an sEMG dataset obtained during key presses in a typing task. This fine-grained classification dataset consists of 16-channel bilateral sEMG recordings and key logs, collected from 19 individuals in two sessions on different days. We report baseline results on intra-session, inter-session and inter-subject evaluations. Our baseline results show that within-session accuracy is relatively high, even with simple learning models. However, the results on between-session and between-participant are much lower, showing that generalizing between sessions and individuals is an open challenge.},
}

Humans
*Electromyography
*Gestures
Brain-Computer Interfaces

@article {pmid40086264,
year = {2025},
author = {Jiang, Y and Zhou, C and Zhao, J and Ren, X and Wang, Q and Ni, P and Li, T},
title = {Derivation of human-derived iPSC line from a male adolescent with first-episode of sporadic schizophrenia.},
journal = {Stem cell research},
volume = {85},
number = {},
pages = {103694},
doi = {10.1016/j.scr.2025.103694},
pmid = {40086264},
issn = {1876-7753},
abstract = {Schizophrenia is considered to be a neurodevelopmental disorder with high heritability. In this study, peripheral blood mononuclear cells (PBMCs) were collected from a male adolescent diagnosed with first-episode of sporadic schizophrenia. Induced pluripotent stem cells (iPSCs) were generated by reprogramming using the factors OCT4, SOX2, NANOG, LIN28, c-MYC, KLF4, and SV40LT. The generated iPSC line was validated by karyotype analysis and expression of pluripotency markers. These iPSCs were capable of differentiating into derivatives of all three germ layers in vivo.},
}

@article {pmid40085468,
year = {2025},
author = {Chen, J and Yang, H and Xia, Y and Gong, T and Thomas, A and Liu, J and Chen, W and Carlson, T and Zhao, H},
title = {Simultaneous Mental Fatigue and Mental Workload Assessment with Wearable High-Density Diffuse Optical Tomography.},
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.2025.3551676},
pmid = {40085468},
issn = {1558-0210},
abstract = {Accurately assessing mental states-such as mental workload and fatigue- is crucial for ensuring the reliability and effectiveness of brain-computer interface (BCI)-based applications. Relying on signals from a limited brain region with low spatial resolution may fail to capture the full scope of relevant information. To address this, high-density diffuse optical tomography (HD-DOT), an emerging form of functional near-infrared spectroscopy (fNIRS) was employed in this study, which provides higher spatial resolution for hemodynamic measurements and enables the reconstruction of 3D brain images. An experiment protocol was designed to investigate both mental workload and fatigue, two critical components of cognitive state that often fluctuate concurrently in real-world scenarios. Machine learning methods were applied for subject-specific classification, achieving 95.14% mean accuracy for fatigue/non-fatigue and 97.93% for four n-back tasks using Random Forest, outperforming Support Vector Machines. These results highlight the transformative potential of HD-DOT in advancing multifaceted cognitive state assessment, paving the way for more precise, adaptable, and powerful BCI applications.},
}

@article {pmid40084138,
year = {2025},
author = {Kaiju, T and Inoue, M and Hirata, M and Suzuki, T},
title = {Compact and low-power wireless headstage for electrocorticography recording of freely moving primates in a home cage.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1491844},
pmid = {40084138},
issn = {1662-4548},
abstract = {OBJECTIVE: Wireless electrocorticography (ECoG) recording from unrestrained nonhuman primates during behavioral tasks is a potent method for investigating higher-order brain functions over extended periods. However, conventional wireless neural recording devices have not been optimized for ECoG recording, and few devices have been tested on freely moving primates engaged in behavioral tasks within their home cages.

METHODS: We developed a compact, low-power, 32-channel wireless ECoG headstage specifically designed for neuroscience research. To evaluate its efficacy, we established a behavioral task setup within a home cage environment.

RESULTS: The developed headstage weighed merely 1.8 g and had compact dimensions of 25 mm × 16 mm × 4 mm. It was efficiently powered by a 100-mAh battery (weighing 3 g), enabling continuous recording for 8.5 h. The device successfully recorded data from an unrestrained monkey performing a center-out joystick task within its home cage.

CONCLUSION: The device demonstrated excellent capability for recording ECoG data from freely moving primates in a home cage environment. This versatile device enhances task design freedom, decrease researchers' workload, and enhances data collection efficiency.},
}

@article {pmid40083893,
year = {2025},
author = {Gordienko, Y and Gordienko, N and Taran, V and Rojbi, A and Telenyk, S and Stirenko, S},
title = {Effect of natural and synthetic noise data augmentation on physical action classification by brain-computer interface and deep learning.},
journal = {Frontiers in neuroinformatics},
volume = {19},
number = {},
pages = {1521805},
pmid = {40083893},
issn = {1662-5196},
abstract = {Analysis of electroencephalography (EEG) signals gathered by brain-computer interface (BCI) recently demonstrated that deep neural networks (DNNs) can be effectively used for investigation of time sequences for physical actions (PA) classification. In this study, the relatively simple DNN with fully connected network (FCN) components and convolutional neural network (CNN) components was considered to classify finger-palm-hand manipulations each from the grasp-and-lift (GAL) dataset. The main aim of this study was to imitate and investigate environmental influence by the proposed noise data augmentation (NDA) of two kinds: (i) natural NDA by inclusion of noise EEG data from neighboring regions by increasing the sampling size N and the different offset values for sample labeling and (ii) synthetic NDA by adding the generated Gaussian noise. The natural NDA by increasing N leads to the higher micro and macro area under the curve (AUC) for receiver operating curve values for the bigger N values than usage of synthetic NDA. The detrended fluctuation analysis (DFA) was applied to investigate the fluctuation properties and calculate the correspondent Hurst exponents H for the quantitative characterization of the fluctuation variability. H values for the low time window scales (< 2 s) are higher in comparison with ones for the bigger time window scales. For example, H more than 2-3 times higher for some PAs, i.e., it means that the shorter EEG fragments (< 2 s) demonstrate the scaling behavior of the higher complexity than the longer fragments. As far as these results were obtained by the relatively small DNN with the low resource requirements, this approach can be promising for porting such models to Edge Computing infrastructures on devices with the very limited computational resources.},
}

@article {pmid40083152,
year = {2025},
author = {Liu, H and Bai, Y and Zheng, Q and Zhao, R and Guo, M and Zhu, J and Ni, G},
title = {Effects of spatial separation and background noise on brain functional connectivity during auditory selective spatial attention.},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
volume = {35},
number = {3},
pages = {},
doi = {10.1093/cercor/bhaf054},
pmid = {40083152},
issn = {1460-2199},
support = {2022BKY056//Tianjin Research Innovation Project for Postgraduate Students/ ; 2023YFF1203500//National Key Research and Development Program of China/ ; },
mesh = {Humans ; Male ; *Attention/physiology ; *Brain/physiology ; Female ; Young Adult ; *Noise ; *Auditory Perception/physiology ; *Magnetic Resonance Imaging ; *Space Perception/physiology ; *Acoustic Stimulation/methods ; Adult ; Neural Pathways/physiology ; Brain Mapping/methods ; Signal-To-Noise Ratio ; },
abstract = {Auditory selective spatial attention (ASSA) plays an important role in "cocktail party" scenes, but the effects of spatial separation between target and distractor sources and background noise on the associated brain responses have not been thoroughly investigated. This study utilized the multilayer time-varying brain network to reveal the effect patterns of different separation degrees and signal-to-noise ratio (SNR) levels on brain functional connectivity during ASSA. Specifically, a multilayer time-varying brain network with six time-windows equally divided by each epoch was constructed to investigate the segregation and integration of brain functional connectivity. The results showed that the inter-layer connectivity strength was consistently lower than the intra-layer connectivity strength for various separation degrees and SNR levels. Moreover, the connectivity strength of the multilayer time-varying brain network increased with decreasing separation degrees and initially increased and subsequently decreased with decreasing SNR levels. The second time-window of the network showed the most significant variation under some conditions and was determined as the core layer. The topology within the core layer was mainly reflected in the connectivity between the frontal and parietal-occipital cortices. In conclusion, these results suggest that spatial separation and background noise significantly modulate brain functional connectivity during ASSA.},
}

Humans
Male
*Attention/physiology
*Brain/physiology
Female
Young Adult
*Noise
*Auditory Perception/physiology
*Magnetic Resonance Imaging
*Space Perception/physiology
*Acoustic Stimulation/methods
Adult
Neural Pathways/physiology
Brain Mapping/methods
Signal-To-Noise Ratio

@article {pmid40083123,
year = {2025},
author = {Meng, M and Chen, G and Chen, S and Ma, Y and Gao, Y and Luo, Z},
title = {DGPDR: discriminative geometric perception dimensionality reduction of SPD matrices on Riemannian manifold for EEG classification.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-11},
doi = {10.1080/10255842.2025.2476184},
pmid = {40083123},
issn = {1476-8259},
abstract = {Manifold learning with Symmetric Positive Definite (SPD) matrices has demonstrated potential for classifying Electroencephalography (EEG) in Brain-Computer Interface (BCI) applications. However, SPD matrices may lead to crucial information loss of EEG signals. This paper proposes a dimensionality reduction method based on discriminative geometric perception on the Riemannian manifold to enhance SPD matrix discriminability. Experiments on BCI Competition IV Dataset 1 and Dataset 2a show the proposed method improves accuracy by 5.0% and 19.38% respectively, demonstrating that applying discriminative geometric perception can effectively maintain robust performance associated with the dimensionality-reduced SPD matrix.},
}

@article {pmid40082907,
year = {2025},
author = {Bonato, P and Reinkensmeyer, D and Manto, M},
title = {Two decades of breakthroughs: charting the future of NeuroEngineering and Rehabilitation.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {22},
number = {1},
pages = {59},
pmid = {40082907},
issn = {1743-0003},
mesh = {Humans ; *Neurological Rehabilitation/instrumentation/trends/methods ; *Biomedical Engineering/trends/methods/instrumentation ; Periodicals as Topic/trends ; Brain-Computer Interfaces/trends ; Forecasting ; Robotics/trends/instrumentation/methods ; },
abstract = {The Journal of NeuroEngineering and Rehabilitation (JNER) has become a major actor for the dissemination of knowledge in the scientific community, bridging the gaps between innovative neuroengineering and rehabilitation. Major fields of innovations have emerged these last 25 years, such as machine learning and the ongoing AI revolution, wearable technologies, human machine interfaces, robotics, advanced prosthetics, functional electrical stimulation and various neuromodulation techniques. With the major burden of disorders impacting on the central/peripheral nervous system and the musculoskeletal system both in adults and in children, successful tailored neurorehabilitation has become a major objective for the research and clinical community at a world scale. JNER contributes to this challenging goal, publishing groundbreaking cutting-edge research using the open access model. The multidisciplinary approaches at the crossroads of biomedical engineering, neuroscience, physical medicine and rehabilitation make of the journal a unique growing platform welcoming breakthrough discoveries to reshape the field and restore function.},
}

Humans
*Neurological Rehabilitation/instrumentation/trends/methods
*Biomedical Engineering/trends/methods/instrumentation
Periodicals as Topic/trends
Brain-Computer Interfaces/trends
Forecasting
Robotics/trends/instrumentation/methods

@article {pmid40082683,
year = {2025},
author = {Verwoert, M and Amigó-Vega, J and Gao, Y and Ottenhoff, MC and Kubben, PL and Herff, C},
title = {Whole-brain dynamics of articulatory, acoustic and semantic speech representations.},
journal = {Communications biology},
volume = {8},
number = {1},
pages = {432},
pmid = {40082683},
issn = {2399-3642},
support = {17619//Nederlandse Organisatie voor Wetenschappelijk Onderzoek (Netherlands Organisation for Scientific Research)/ ; },
mesh = {Humans ; *Speech/physiology ; Male ; *Brain/physiology ; *Semantics ; Female ; Adult ; Young Adult ; Speech Acoustics ; Brain Mapping ; },
abstract = {Speech production is a complex process that traverses several representations, from the meaning of spoken words (semantic), through the movement of articulatory muscles (articulatory) and, ultimately, to the produced audio waveform (acoustic). In this study, we identify how these different representations of speech are spatially and temporally distributed throughout the depth of the brain. Intracranial neural data is recorded from 15 participants, across 1647 electrode contacts, while overtly speaking 100 unique words. We find a bilateral spatial distribution for all three representations, with a more widespread and temporally dynamic distribution in the left compared to the right hemisphere. The articulatory and acoustic representations share a similar spatial distribution surrounding the Sylvian fissure, while the semantic representation is more widely distributed across the brain in a mostly distinct network. These results highlight the distributed nature of the speech production neural process and the potential of non-motor representations for speech brain-computer interfaces.},
}

Humans
*Speech/physiology
Male
*Brain/physiology
*Semantics
Female
Adult
Young Adult
Speech Acoustics
Brain Mapping