@article {pmid40501187,
year = {2025},
author = {Wang, YJ and Jie, Z and Liu, Y and Pan, Y},
title = {Dyad averaged BMI-dependent interbrain synchrony during continuous mutual prediction in social coordination.},
journal = {Social neuroscience},
volume = {},
number = {},
pages = {1-10},
doi = {10.1080/17470919.2025.2517068},
pmid = {40501187},
issn = {1747-0927},
abstract = {Obesity is linked to notable psychological risks, particularly in social interactions where individuals with high body mass index (BMI) often encounter stigmatization and difficulties in forming and maintaining social connections. Although awareness of these issues is growing, there is a lack of research on real-time, dynamic interactions involving dyads with various BMI levels. To address this gap, our study employed a joint finger-tapping task, where participant dyads engaged in coordinated activity while their brain activity was monitored using functional near-infrared spectroscopy (fNIRS). Our findings showed that both Bidirectional and Unidirectional Interaction conditions exhibited higher levels of behavioral and interbrain synchrony compared to the No Interaction condition. Notably, only in the Bidirectional Interaction condition, higher dyadic BMI was significantly correlated with poorer behavioral coordination and reduced interbrain synchrony. This finding suggests that the ability to maintain social coordination, particularly in scenarios requiring continuous mutual prediction and adjustment, is modulated by dyads' BMI.},
}

@article {pmid40499369,
year = {2025},
author = {Banovoth, RS and K V, K},
title = {Roman domination-based spiking neural network for optimized EEG signal classification of four class motor imagery.},
journal = {Computers in biology and medicine},
volume = {194},
number = {},
pages = {110397},
doi = {10.1016/j.compbiomed.2025.110397},
pmid = {40499369},
issn = {1879-0534},
abstract = {The Spiking Neural Network (SNN) is a third-generation neural network recognized for its energy efficiency and ability to process spatiotemporal information, closely imitating the behavioral mechanisms of biological neurons in the brain. SNN exhibit rich neurodynamic features in the spatiotemporal domain, making them well-suited for processing brain signals, mainly those captured using the widely used non-invasive Electroencephalography (EEG) technique. However, the structural limitations of SNN hinder their feature extraction capabilities for motor imagery signal classification, which leads to under performance of the task. To address the aforementioned challenge, the proposed study introduces a novel model that incorporates Roman Domination within a Spiking Neural Network (RDSNN), where Roman domination identifies the most highly correlated channels or nodes. These channels generate an appropriate threshold for spike generation in the signals, which are then classified using the SNN. The model's performance was evaluated on three typically representative motor imagery datasets: PhysioNet, BCI Competition IV-2a, and BCI Competition IV-2b. RDSNN achieved 73.65% accuracy on PhysioNet, 81.75% on BCI IV-2a, and 84.56% on BCI IV-2b. The results demonstrate not only superior accuracy compared to State-Of-the-Art (SOTA) methods but also a 35% reduction in computation time, attributed to the application of Roman domination.},
}

@article {pmid40499342,
year = {2025},
author = {Silveira, I and Varandas, R and Gamboa, H},
title = {Cognitive Lab: A dataset of biosignals and HCI features for cognitive process investigation.},
journal = {Computer methods and programs in biomedicine},
volume = {269},
number = {},
pages = {108863},
doi = {10.1016/j.cmpb.2025.108863},
pmid = {40499342},
issn = {1872-7565},
abstract = {BACKGROUND AND OBJECTIVE: Attention, cognitive workload/fatigue, and emotional states significantly influence learning outcomes, cognitive performance, and human-machine interactions. However, existing assessment methodologies fail to fully capture the multimodal nature of these cognitive processes, limiting their application in adaptive learning environments. This study presents the Cognitive Lab, a comprehensive multimodal dataset designed to investigate these cognitive processes across real-time learning scenarios. Specifically, it aims to capture and enable the classification of (1) attention and cognitive workload states using standard cognitive tasks, (2) cognitive fatigue arising from prolonged digital activities, and (3) emotional and learning states during interactive lessons.

METHODS: The Cognitive Lab dataset consists of three distinct subsets, each developed through specific experimental scenarios targeting different aspects of learning. Dataset 1 comprises recordings from eight participants performing N-Back and mental subtraction tasks, aimed at assessing attention and cognitive workload. Dataset 2 includes data from 10 participants engaged in a digital lesson, complemented by Corsi block-tapping and concentration tasks, to evaluate cognitive fatigue. Lastly, Dataset 3 captures data from 18 participants during an interactive Jupyter Notebook lesson, focusing on emotional states and learning processes. Each scenario combined biosignals (accelerometry, ECG, EDA, EEG, fNIRS, respiration) with Human-Computer Interaction (HCI) features (mouse-tracking, keyboard activity, screenshots). Machine learning models were applied to classify cognitive states, with cross-validation ensuring robust results.

RESULTS: The dataset enabled accurate classification of learning states, achieving up to 87% accuracy in differentiating learning states using mouse-tracking data. Furthermore, it successfully differentiated attention, cognitive workload, and cognitive fatigue states using biosignal and HCI data, with fNIRS, EEG, and ECG emerging as key contributors to classification performance. Variability across participants highlighted the potential for subject-specific calibration to enhance model accuracy.

CONCLUSIONS: The Cognitive Lab dataset represents a resource for investigating cognitive phenomena in real-world learning scenarios. Its integration of biosignals and HCI features enables the classification of cognitive states and supports advancements in adaptive learning systems, cognitive neuroscience, and brain-computer interface technologies.},
}

@article {pmid40498623,
year = {2025},
author = {Lian, Q and Wang, Y and Qi, Y},
title = {Dynamic Instance-level Graph Learning Network of Intracranial Electroencephalography Signals for Epileptic Seizure Prediction.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3578627},
pmid = {40498623},
issn = {2168-2208},
abstract = {Brain-computer interface (BCI) technology is emerging as a valuable tool for diagnosing and treating epilepsy, with deep learning-based feature extraction methods demonstrating remarkable progress in BCI-aided systems. However, accurately identifying causal relationships in temporal dynamics of epileptic intracranial electroencephalography (iEEG) signals remains a challenge. This paper proposes a Dynamic Instance-level Graph Learning Network (DIGLN) for seizure prediction using iEEG signals. The DIGLN comprises two core components: a grouped temporal neural network that extracts node features and a graph structure learning method to capture the causality from intra-channel to inter-channel. Furthermore, we propose a graphical interactive writeback technique to enable DIGLN to capture the causality from inter-channel to intra-channel. Consequently, our DIGLN enables patient-specific dynamic instance-level graph learning, facilitating the modelling of evolving signals and functional connectivities through end-to-end data-driven learning. Experimental results on the Freiburg iEEG dataset demonstrate the superior performance of DIGLN, surpassing other deep learning-based seizure prediction methods. Visualization results further confirm DIGLN's capability to learn interpretable and diverse connections.},
}

@article {pmid40496741,
year = {2025},
author = {Tiwari, N and Anwar, S and Bhattacharjee, V},
title = {EEG dataset for natural image recognition through visual stimuli.},
journal = {Data in brief},
volume = {60},
number = {},
pages = {111639},
pmid = {40496741},
issn = {2352-3409},
abstract = {Electroencephalography (EEG) is a technique for measuring the brain's electrical activity in the form of action potentials with electrodes placed on the scalp. Because of its non-invasive nature and ease of use, the approach is becoming increasingly popular for investigations. EEG reveals a wide spectrum of human brain potentials, such as event-related, sensory, and visually evoked potentials (VEPs), which aids in the development of intricate applications. Developing Apps or Brain-Computer Interface (BCI) devices demands data on these potentials. The present dataset comprises EEG recordings generated by thirty-two individuals in reaction to visual stimuli (VEPs). The rationale behind gathering this data is its ability to support EEG-based image classification and reconstruction while also advancing visual decoding. The primary purpose is to examine the cognitive processes behind both familiar and unfamiliar observations. A standardized experimental setup comprising many experimental phases was employed to capture the essence of the investigation and gather the dataset.},
}

@article {pmid40496017,
year = {2025},
author = {Reid, LV and Spalluto, CM and Wilkinson, TMA and Staples, KJ},
title = {Influenza-induced microRNA-155 expression is altered in extracellular vesicles derived from the COPD epithelium.},
journal = {Frontiers in cellular and infection microbiology},
volume = {15},
number = {},
pages = {1560700},
pmid = {40496017},
issn = {2235-2988},
mesh = {*MicroRNAs/genetics/metabolism ; Humans ; *Pulmonary Disease, Chronic Obstructive ; *Extracellular Vesicles/metabolism ; *Epithelial Cells/virology/metabolism ; *Influenza, Human/virology ; Cells, Cultured ; Gene Expression Profiling ; Cell Line ; },
abstract = {BACKGROUND: Influenza virus particularly affects those with chronic lung conditions such as Chronic Obstructive Pulmonary Disease (COPD). Airway epithelial cells are the first line of defense and primary target of influenza infection and release extracellular vesicles (EVs). EVs can transfer of biological molecules such as microRNAs (miRNAs) that can modulate the immune response to viruses through control of the innate and adaptive immune systems. The aim of this work was to profile the EV miRNAs released from bronchial epithelial cells in response to influenza infection and discover if EV miRNA expression was altered in COPD.

METHODS: Influenza infection of air-liquid interface (ALI) differentiated BCi-NS1.1 epithelial cells were characterized by analyzing the expression of antiviral genes, cell barrier permeability and cell death. EVs were isolated by filtration and size exclusion chromatography from the apical surface wash of ALI cultured bronchial epithelial cells. The EV miRNA cargo was sequenced and reads mapped to miRBase. The BCi sequencing results were further investigated by RT-qPCR and by using healthy and COPD primary epithelial cells.

RESULTS: Infection of ALI cultured BCi cells with IAV at 3.6 x 10[6] IU/ml for 24 h led to significant upregulation of anti-viral genes without high levels of cell death. EV release from ALI-cultured BCi cells was confirmed using electron microscopy and detection of known tetraspanin EV markers using western blot and the ExoView R100 platform. Differential expression analyses identified 5 miRNA that had a fold change of >0.6: miR-155-5p, miR-122-5p, miR-378a-3p, miR-7-5p and miR-146a-5p (FDR<0.05). Differences between EV, non-EV and cellular levels of these miRNA were detected. Primary epithelial cell release of EV and their miRNA cargo was similar to that observed for BCi. Intriguingly, miR-155 expression was decreased in EVs derived from COPD patients compared to EVs from control samples.

CONCLUSION: Epithelial EV miRNA release may be a key mechanism in modulating the response to IAV in the lungs. Furthermore, changes in EV miRNA expression may play a dysfunctional role in influenza-induced exacerbations of COPD. However, further work to fully characterize the function of EV miRNA in response to IAV in both health and COPD is required.},
}

*MicroRNAs/genetics/metabolism
Humans
*Pulmonary Disease, Chronic Obstructive
*Extracellular Vesicles/metabolism
*Epithelial Cells/virology/metabolism
*Influenza, Human/virology
Cells, Cultured
Gene Expression Profiling
Cell Line

@article {pmid40495523,
year = {2025},
author = {Gou, H and Bu, J and Cheng, Y and Liu, C and Gan, H and Liu, M and Zhao, Q and Chen, X and Ren, J and Hong, W and Wang, R and Cao, Y and Yu, C and Chen, X and Zhang, X},
title = {Improved Response Inhibition Through Cognition-Guided EEG Neurofeedback in Men With Methamphetamine Use Disorder.},
journal = {The American journal of psychiatry},
volume = {},
number = {},
pages = {appiajp20240475},
doi = {10.1176/appi.ajp.20240475},
pmid = {40495523},
issn = {1535-7228},
abstract = {OBJECTIVE: Impaired response inhibition is the core cognitive deficit in methamphetamine use disorder (MUD), and methamphetamine cue reactivity is a major factor that reduces inhibition efficiency. The authors sought to use cognition-guided neurofeedback to deactivate methamphetamine cue-related brain reactivity patterns in men with MUD to improve their response inhibition.

METHODS: A cognition-guided, closed-loop EEG-based neurofeedback protocol was employed. Methamphetamine cue-related brain activity patterns were identified offline using multivariate pattern analysis of EEG data from all channels during a methamphetamine cue reactivity task. In the real-time feedback phase, participants were trained to deactivate their methamphetamine cue-related patterns, which were presented as feedback. The study included two samples, totaling 99 men with MUD. In sample 1, 66 men received 10 neurofeedback sessions based either on their own brain activity patterns (real neurofeedback group 1, N=33) or on randomly matched participants' patterns (yoke neurofeedback group, N=33). Sample 2, which was used to validate findings in sample 1, included a real feedback group (real neurofeedback group 2; N=17) and a standard rehabilitation group (N=16) that received only standard rehabilitation without additional intervention. Response inhibition was assessed using a go/no-go task based on methamphetamine-related cues before and after the intervention.

RESULTS: Compared to the yoke feedback group, real neurofeedback group 1 successfully deactivated methamphetamine cue-related brain reactivity patterns, resulting in significantly enhanced response inhibition (d-prime, Cohen's f=0.31). Neurofeedback performance in real neurofeedback group 1 was significantly correlated with improved response inhibition. Additionally, response inhibition improvements could be predicted by initial neurofeedback performance and baseline characteristics. Sample 2 replicated these findings, showing that response inhibition in real neurofeedback group 2 was improved and predictable. Notably, these intervention effects in real neurofeedback group 2 were better than those in the standard rehabilitation group.

CONCLUSIONS: These findings underscore the efficacy of cognition-guided neurofeedback for treating MUD, thereby suggesting its potential applicability in other addiction interventions.},
}

@article {pmid40495436,
year = {2025},
author = {Rozovsky, R and Wolfe, M and Abdul-Waalee, H and Chobany, M and Malgireddy, G and Hart, JA and Lepore, B and Vahedifard, F and Phillips, ML and Birmaher, B and Skeba, A and Diler, RS and Bertocci, MA},
title = {Gray Matter Differences in Adolescent Psychiatric Inpatients: A Machine Learning Study of Bipolar Disorder and Other Psychopathologies.},
journal = {Brain and behavior},
volume = {15},
number = {6},
pages = {e70589},
doi = {10.1002/brb3.70589},
pmid = {40495436},
issn = {2162-3279},
support = {R01-MH-121451/MH/NIMH NIH HHS/United States ; },
mesh = {Humans ; Adolescent ; *Bipolar Disorder/diagnostic imaging/pathology ; Male ; Female ; *Gray Matter/diagnostic imaging/pathology ; Inpatients ; Magnetic Resonance Imaging/methods ; Machine Learning ; Support Vector Machine ; *Brain/pathology/diagnostic imaging ; *Mental Disorders/diagnostic imaging ; },
abstract = {BACKGROUND: Bipolar disorder (BD) is among the psychiatric disorders most prone to misdiagnosis, with both false positives and false negatives resulting in treatment delay. We employed a whole-brain machine learning approach focusing on gray matter volumes (GMVs) to contribute to defining objective biomarkers of BD and discriminating it from other forms of psychopathology, including subthreshold manic presentations without a BD Type I/II diagnosis.

METHODS: Five support vector machine (SVM) models were used to detect differences in GMVs between inpatient adolescents aged 13-17 with BD-I/II (n = 34), other specified BD (OSB) (n = 106), other non-bipolar psychopathology (OP) (n = 52), and healthy controls (HC) (n = 27). We examined the most discriminative GMVs and tested their associations with clinical symptoms.

RESULTS: Whole-brain classifiers in the model BD-I/II versus OSB achieved total accuracy of 79%, (AUC = 0.70, p = 0.002); BD versus OP 66%, (AUC = 0.61, p = 0.014); BD versus HC 66%, (AUC = 0.67, p = 0.011); OSB versus HC 77%, (AUC = 0.61, p = 0.01); OP versus HC 68%, (AUC = 0.70, p = 0.001). The most discriminative GMVs that contributed to the classification were in areas associated with movement, sensory processing, and cognitive control. Correlations between these GMVs and self-reported mania, negative affect, or anxiety were observed in all inpatient groups.

CONCLUSIONS: These findings indicate that pattern recognition models focusing on GMVs in regions associated with movement, sensory processing, and cognitive control can effectively distinguish well-characterized BD-I/II from other forms of psychopathology, including other specified BD, in a pediatric population. These results may contribute to enhancing diagnostic accuracy and guiding earlier, more targeted interventions.},
}

Humans
Adolescent
*Bipolar Disorder/diagnostic imaging/pathology
Male
Female
*Gray Matter/diagnostic imaging/pathology
Inpatients
Magnetic Resonance Imaging/methods
Machine Learning
Support Vector Machine
*Brain/pathology/diagnostic imaging
*Mental Disorders/diagnostic imaging

@article {pmid40494420,
year = {2025},
author = {Spinelli, R and Sanchís, I and Siano, A},
title = {Fighting Alzheimer's naturally: Peptides as multitarget drug leads.},
journal = {Bioorganic & medicinal chemistry letters},
volume = {},
number = {},
pages = {130305},
doi = {10.1016/j.bmcl.2025.130305},
pmid = {40494420},
issn = {1464-3405},
abstract = {In this review, we provide a comprehensive analysis of the role of natural peptides-particularly those derived from amphibian skin secretions-as multitarget-directed ligands (MTDLs) in the context of Alzheimer's disease (AD). Given the multifactorial nature of AD, where cholinergic dysfunction intersects with amyloid-β aggregation, tau hyperphosphorylation, oxidative stress, metal ion imbalance, and monoamine oxidase dysregulation, therapeutic strategies capable of modulating several pathological pathways simultaneously are urgently needed. We begin by revisiting the cholinergic hypothesis and its molecular and structural underpinnings, emphasizing the relevance of key binding sites such as the catalytic active site (CAS) and the peripheral anionic site (PAS) of cholinesterases. The central axis of this review lies in the exploration of naturally occurring peptides that have demonstrated dual or multiple activities against AD-related targets. We highlight our group's pioneering work on amphibian-derived peptides such as Hp-1971, Hp-1935, and BcI-1003, which exhibit non-competitive inhibition of AChE and BChE, MAO-B modulation, and antioxidant properties. Furthermore, we describe additional peptide-rich extracts and bioactive sequences from various amphibians and other animal or plant sources, expanding the landscape of natural molecules with neuroprotective potential. We also delve into peptide modification strategies-such as amino acid substitution, cyclization, D-amino acid incorporation, and terminal/side-chain functionalization-that have been employed to enhance peptide stability, blood-brain barrier permeability, and target affinity. These strategies not only improve the pharmacokinetic profiles of native peptides but also open the door for the rational design of next-generation peptide therapeutics. Overall, this review underscores the vast potential of natural peptides as scaffolds for the development of multifunctional agents capable of intervening in the complex cascade of Alzheimer's pathology.},
}

@article {pmid40494387,
year = {2025},
author = {Wu, EG and Rudzite, AM and Bohlen, MO and Li, PH and Kling, A and Cooler, S and Rhoades, C and Brackbill, N and Gogliettino, AR and Shah, NP and Madugula, SS and Sher, A and Litke, AM and Field, GD and Chichilnisky, EJ},
title = {Decomposition of retinal ganglion cell electrical images for cell type and functional inference.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ade344},
pmid = {40494387},
issn = {1741-2552},
abstract = {OBJECTIVE: Identifying neuronal cell types and their biophysical properties based on their extracellular electrical features is a major challenge for experimental neuroscience and for the development of high-resolution brain-machine interfaces. One example is identification of retinal ganglion cell (RGC) types and their visual response properties, which is fundamental for developing future electronic implants that can restore vision.

APPROACH: The electrical image (EI) of a RGC, or the mean spatio-temporal voltage footprint of its recorded spikes on a high-density electrode array, contains substantial information about its anatomical, morphological, and functional properties. However, the analysis of these properties is complex because of the high-dimensional nature of the EI. We present a novel optimization-based algorithm to decompose electrical image into a low-dimensional, biophysically-based representation: the temporally-shifted superposition of three learned basis waveforms corresponding to spike waveforms produced in the somatic, dendritic and axonal cellular compartments.

RESULTS: The decomposition was evaluated using large-scale multi-electrode recordings from the macaque retina. The decomposition accurately localized the somatic and dendritic compartments of the cell. The imputed dendritic fields of RGCs correctly predicted the location and shape of their visual receptive fields. The inferred waveform amplitudes and shapes accurately identified the four major primate RGC types (ON and OFF midget and parasol cells) substantially more accurately than previous approaches.

SIGNIFICANCE: These findings contribute to more accurate inference of RGC types and their original light responses based purely on their electrical features, with potential implications for vision restoration technology.},
}

@article {pmid40494367,
year = {2025},
author = {Thielen, J},
title = {Addressing BCI inefficiency in c-VEP-based BCIs: A comprehensive study of neurophysiological predictors, binary stimulus sequences, and user comfort.},
journal = {Biomedical physics & engineering express},
volume = {},
number = {},
pages = {},
doi = {10.1088/2057-1976/ade316},
pmid = {40494367},
issn = {2057-1976},
abstract = {This study investigated the presence of brain-computer interface (BCI) inefficiency in BCIs using the code-modulated visual evoked potential (c-VEP). It further explored neurophysiological predictors of performance variability and evaluated a wide range of binary stimulus sequences in terms of classification accuracy and user comfort, aiming to identify strategies to mitigate c-VEP BCI inefficiency. Approach. In a comprehensive empirical analysis, ten different binary stimulus sequences were offline evaluated. These sequences included five code families (m-sequence, de Bruijn sequence, Golay sequence, Gold code, and a Gold code set), each in original and modulated form. To identify predictors of performance variability, resting-state alpha activity, heart rate and heart rate variability, sustained attention, and flash-VEP characteristics were studied. Main Results. Results confirmed substantial inter-individual variability in c-VEP BCI efficiency. While all participants reached a near-perfect classification accuracy, their obtained speed varied substantially. Four flash-VEP features were found to significantly correlate with the observed performance varibility: the N2 latency, the P2 latency and amplitude, and the N3 amplitude. Among the tested stimulus conditions, the m-sequence emerged as the best-performing universal stimulus. However, tailoring stimulus selection to individuals led to significant improvements in performance. Cross-decoding was successful between modulated stimulus conditions, but showed challenges when generalizing across other stimulus conditions. Lastly, while overall comfort ratings were comparable across conditions, stimulus modulation was associated with a significant decrease in user comfort. Significance. This study challenges the assumption of universal efficiency in c-VEP BCIs. The findings highlight the importance of accounting for individual neurophysiological differences and underscore the need for personalized stimulus protocols and decoding strategies to enhance both performance and user comfort.},
}

@article {pmid40493465,
year = {2025},
author = {Rabiee, A and Ghafoori, S and Cetera, A and Norouzi, M and Besio, W and Abiri, R},
title = {A Comparative Study of Conventional and Tripolar EEG for High-Performance Reach-to-Grasp BCI Systems.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2025.3578235},
pmid = {40493465},
issn = {1558-2531},
abstract = {This study aims to enhance brain-computer interface (BCI) applications for individuals with motor impairments by comparing the effectiveness of noninvasive tripolar concentric ring electrode electroencephalography (tEEG) with conventional electroencephalography (EEG) technology. The goal is to determine which EEG technology is more effective in measuring and decoding different grasp-related neural signals. The approach involves experimenting on ten healthy participants who performed two distinct reach-and-grasp movements: power grasp and precision grasp, with a no-movement condition serving as the baseline. Our research compares EEG and tEEG in decoding grasping movements, focusing on signal-to-noise ratio (SNR), spatial resolution, and wavelet time-frequency analysis. Additionally, our study involved extracting and analyzing statistical features from the wavelet coefficients, and both binary and multiclass classification methods were employed. Four machine learning algorithms-Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Linear Discriminant Analysis (LDA)-were used to evaluate the decoding accuracies. Our results indicated that tEEG demonstrated higher quality performance compared to conventional EEG in various aspects. This included a higher signal-to-noise ratio and improved spatial resolution. Furthermore, wavelet timefrequency analyses validated these findings, with tEEG exhibiting increased power spectra, thus providing a more detailed and informative representation of neural dynamics. The use of tEEG led to significant improvements in decoding accuracy for differentiating grasp movement types. Specifically, tEEG achieved around 90.00% accuracy in binary and 75.97% for multiclass classification. These results exceed those from conventional EEG, which recorded a maximum of 77.85% and 61.27% in similar tasks, respectively.},
}

@article {pmid40493186,
year = {2025},
author = {Alawieh, H and Liu, D and Madera, J and Kumar, S and Racz, FS and Fey, AM and Del R Millán, J},
title = {Electrical spinal cord stimulation promotes focal sensorimotor activation that accelerates brain-computer interface skill learning.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {122},
number = {24},
pages = {e2418920122},
doi = {10.1073/pnas.2418920122},
pmid = {40493186},
issn = {1091-6490},
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; Adult ; *Learning/physiology ; *Spinal Cord Injuries/physiopathology/rehabilitation ; Female ; *Spinal Cord Stimulation/methods ; *Motor Cortex/physiology ; Young Adult ; *Motor Skills/physiology ; },
abstract = {Injuries affecting the central nervous system may disrupt neural pathways to muscles causing motor deficits. Yet the brain exhibits sensorimotor rhythms (SMRs) during movement intents, and brain-computer interfaces (BCIs) can decode SMRs to control assistive devices and promote functional recovery. However, noninvasive BCIs suffer from the instability of SMRs, requiring longitudinal training for users to learn proper SMR modulation. Here, we accelerate this skill learning process by applying cervical transcutaneous electrical spinal stimulation (TESS) to inhibit the motor cortex prior to longitudinal upper-limb BCI training. Results support a mechanistic role for cortical inhibition in significantly increasing focality and strength of SMRs leading to accelerated BCI control in healthy subjects and an individual with spinal cord injury. Improvements were observed following only two TESS sessions and were maintained for at least one week in users who could not otherwise achieve control. Our findings provide promising possibilities for advancing BCI-based motor rehabilitation.},
}

Humans
*Brain-Computer Interfaces
Male
Adult
*Learning/physiology
*Spinal Cord Injuries/physiopathology/rehabilitation
Female
*Spinal Cord Stimulation/methods
*Motor Cortex/physiology
Young Adult
*Motor Skills/physiology

@article {pmid40490658,
year = {2025},
author = {Norizadeh Cherloo, M and Kashefi Amiri, H and Mijani, AM and Zhan, L and Daliri, MR},
title = {A comprehensive study of template-based frequency detection methods in SSVEP-based brain-computer interfaces.},
journal = {Behavior research methods},
volume = {57},
number = {7},
pages = {196},
pmid = {40490658},
issn = {1554-3528},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; Signal-To-Noise Ratio ; *Evoked Potentials, Visual/physiology ; *Signal Processing, Computer-Assisted ; Algorithms ; },
abstract = {Recently, SSVEP-based brain-computer interfaces (BCIs) have received increasing attention from researchers due to their high signal-to-noise ratios (SNR), high information transfer rates (ITR), and low user training. Therefore, various methods have been proposed to recognize the frequency of SSVEPs. This paper reviewed the state-of-the-art frequency detection methods in SSVEP-based BCIs. Nineteen multi-channel SSVEP detection methods, organized into four categories based on different analytical approaches, were studied. All methods are template-based approaches and classified into four groups according to the basic models they employ: canonical correlation analysis (CCA), multivariate synchronization index (MSI), task-related component analysis (TRCA), and correlated component analysis (CORRCA). Each group consists of methods that use one of these basic models as the core model for their approach. This paper provides a description, a clear flowchart, and MATLAB code for each method and helps researchers use or develop the existing SSVEP detection methods. Although all methods were evaluated in separate studies, a comprehensive comparison of methods is still missing. In this study, several experiments were conducted to assess the performance of SSVEP detection methods. The benchmark 40-class SSVEP dataset from 35 subjects was used to evaluate methods. All methods were applied to the dataset and were evaluated in terms of classification accuracy, information transfer rate (ITR), and computational time. The experiment results revealed that four factors efficiently design an accurate, robust SSVEP detection method. (1) employing filter bank analysis to incorporate fundamental and harmonics frequency components, (2) utilizing calibration data to construct optimized reference signals, (3) integrating spatial filters of all stimuli to construct classification features, and (4) calculating spatial filters using training trials. Furthermore, results showed that filter bank ensemble task-related components (FBETRCA) achieved the highest performance.},
}

*Brain-Computer Interfaces
Humans
*Electroencephalography/methods
Signal-To-Noise Ratio
*Evoked Potentials, Visual/physiology
*Signal Processing, Computer-Assisted
Algorithms

@article {pmid40490007,
year = {2025},
author = {Chen, Y and Peng, Y and Tang, J and Camilleri, TA and Camilleri, KP and Kong, W and Cichocki, A},
title = {EEG-based affective brain-computer interfaces: recent advancements and future challenges.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ade290},
pmid = {40490007},
issn = {1741-2552},
abstract = {As one of the most popular brain-computer interface (BCI) paradigms, affective BCI (aBCI) decodes the human emotional states from brain signals and imposes necessary feedback to achieve neural regulation when negative emotional states (i.e., depression, anxiety) are detected, which are considered as the two basic functions of aBCI system. Electroencephalogram (EEG) is the scalp reflection of neural activities and has been regarded as the gold standard of emotional effects. Recently, rapid progresses have been made for emotion recognition and regulation with the purpose of constructing a high-performance closed-loop EEG-based aBCI system. Therefore, it is necessary to make a timely review for aBCI research by summarizing the current progresses as well as challenges and opportunities, to draw the attention from both academia and industry. Toward this goal, a systematic literature review was performed to summarize not only the recent progresses in emotion recognition and regulation from the perspective of closed-loop aBCI, but also the main challenges and future research focuses to narrow the gap between the current research and real applications of aBCI systems. Approach. A systematic literature review on EEG-based emotion recognition and regulation was performed on Web of Science and related databases, resulting in more than 100 identified studies. These studies were analyzed according to the experimental paradigm, emotion recognition methods in terms of different scenarios, and the applications of emotion recognition in diagnosis and regulation of affective disorders. Main results. Based on the literature review, advancements for EEG-based aBCI research were extensively summarized from six aspects including the 'emotion elicitation paradigms and data sets', 'inner exploration of EEG information','outer extension of fusing EEG with other data modalities', 'cross-scene emotion recognition', 'emotion recognition by considering real scenarios', and 'diagnosis and regulation of affective disorders'. In addition, future opportunities were concluded by focusing on the main challenges in hindering the aBCI system to move from laboratory to real applications. Moreover, the neural mechanisms and theoretical basis behind EEG emotion recognition and regulation are also introduced to provide support for the advancements and challenges in aBCI. Significance. This review summarizes the current practices and performance outcomes in emotion recognition and regulation. Future directions in response to the existing challenges are provided with the expectation of guiding the aBCI research to focus on the necessary key technologies of aBCI system in practical deployment.},
}

@article {pmid40490003,
year = {2025},
author = {Tates, A and Matran-Fernandez, A and Halder, S and Daly, I},
title = {Speech imagery brain-computer interfaces: a systematic literature review.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ade28e},
pmid = {40490003},
issn = {1741-2552},
abstract = {Speech Imagery (SI) refers to the mental experience of hearing speech and may be the core of verbal thinking for people who undergo internal monologues. It belongs to the set of possible mental imagery states that produce kinesthetic experiences whose sensations are similar to their non-imagery counterparts. SI underpins language processes and may have similar building blocks to overt speech without the final articulatory outcome. The kinesthetic experience of SI has been proposed to be a projection of the expected articulatory outcome in a top-down processing manner. As SI seems to be a core human cognitive task it has been proposed as a paradigm for Brain Computer Interfaces (BCI). One important aspect of BCI designs is usability, and SI may present an intuitive paradigm, which has brought the attention of researchers to attempt to decode SI from brain signals. In this paper we review the important aspects of SI-BCI decoding pipelines. \textit{Approach}.
We conducted this review according to the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) guidelines. Specifically, we filtered peer-reviewed reports via a search of Google Scholar and PubMed. We selected a total of 104 reports that attempted to decode Speech Imagery from neural activity. \textit{Main results}.
Our review reveals a growing interest in SI decoding in the last 20 years, and shows how different neuroimaging modalities have been employed to record SI in distinct ways to instruct participants to perform this task. We discuss the signal processing methods used along with feature extraction techniques and found a high preference for Deep Learning models. We have summarized and compared the decoding attempts by quantifying the efficacy of decoding by measuring Information Transfer Rates. Notably, fewer than 6\% of studies reported real-time decoding, with the vast majority focused on offline analyses. This suggests existing challenges of this paradigm, as the variety of approaches and outcomes prevents a clear identification of the field's current state-of-the-art. We offer a discussion of future research directions. \textit{Significance}
Speech Imagery is an attractive BCI paradigm. This review outlines the increasing interest in SI, the methodological trends, the efficacy of different approaches, and the current progress toward real-time decoding systems.},
}

@article {pmid40489280,
year = {2025},
author = {Wang, Z and Zhang, Y and Zhang, Z and Xie, SQ and Lanzon, A and Heath, WP and Li, Z},
title = {Instance-Based Transfer Learning with Similarity-Aware Subject Selection for Cross-Subject SSVEP-Based BCIs.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3577813},
pmid = {40489280},
issn = {2168-2208},
abstract = {Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) can achieve high recognition accuracy with sufficient training data. Transfer learning presents a promising solution to alleviate data requirements for the target subject by leveraging data from source subjects; however, effectively addressing individual variability among both target and source subjects remains a challenge. This paper proposes a novel transfer learning framework, termed instance-based task-related component analysis (iTRCA), which leverages knowledge from source subjects while considering their individual contributions. iTRCA extracts two types of features: (1) the subject-general feature, capturing shared information between source and target subjects in a common latent space, and (2) the subject-specific feature, preserving the unique characteristics of the target subject. To mitigate negative transfer, we further design an enhanced framework, subject selection-based iTRCA (SS-iTRCA), which integrates a similarity-based subject selection strategy to identify appropriate source subjects for transfer based on their task-related components (TRCs). Comparative evaluations on the Benchmark, BETA, and a self-collected dataset demonstrate the effectiveness of the proposed iTRCA and SS-iTRCA frameworks. This study provides a potential solution for developing high-performance SSVEP-based BCIs with reduced target subject data.},
}

@article {pmid40485770,
year = {2025},
author = {Tyler, WJ and Adavikottu, A and Blanco, CL and Mysore, A and Blais, C and Santello, M and Unnikrishnan, A},
title = {Neurotechnology for enhancing human operation of robotic and semi-autonomous systems.},
journal = {Frontiers in robotics and AI},
volume = {12},
number = {},
pages = {1491494},
pmid = {40485770},
issn = {2296-9144},
abstract = {Human operators of remote and semi-autonomous systems must have a high level of executive function to safely and efficiently conduct operations. These operators face unique cognitive challenges when monitoring and controlling robotic machines, such as vehicles, drones, and construction equipment. The development of safe and experienced human operators of remote machines requires structured training and credentialing programs. This review critically evaluates the potential for incorporating neurotechnology into remote systems operator training and work to enhance human-machine interactions, performance, and safety. Recent evidence demonstrating that different noninvasive neuromodulation and neurofeedback methods can improve critical executive functions such as attention, learning, memory, and cognitive control is reviewed. We further describe how these approaches can be used to improve training outcomes, as well as teleoperator vigilance and decision-making. We also describe how neuromodulation can help remote operators during complex or high-risk tasks by mitigating impulsive decision-making and cognitive errors. While our review advocates for incorporating neurotechnology into remote operator training programs, continued research is required to evaluate the how these approaches will impact industrial safety and workforce readiness.},
}

@article {pmid40398443,
year = {2025},
author = {Jehn, C and Kossmann, A and Katerina Vavatzanidis, N and Hahne, A and Reichenbach, T},
title = {CNNs improve decoding of selective attention to speech in cochlear implant users.},
journal = {Journal of neural engineering},
volume = {22},
number = {3},
pages = {},
doi = {10.1088/1741-2552/addb7b},
pmid = {40398443},
issn = {1741-2552},
mesh = {Humans ; *Cochlear Implants ; *Attention/physiology ; *Speech Perception/physiology ; Female ; Male ; Electroencephalography/methods ; Middle Aged ; Adult ; *Neural Networks, Computer ; Aged ; Acoustic Stimulation/methods ; Support Vector Machine ; },
abstract = {Objective. Understanding speech in the presence of background noise such as other speech streams is a difficult problem for people with hearing impairment, and in particular for users of cochlear implants (CIs). To improve their listening experience, auditory attention decoding (AAD) aims to decode the target speaker of a listener from electroencephalography (EEG), and then use this information to steer an auditory prosthesis towards this speech signal. In normal-hearing individuals, deep neural networks (DNNs) have been shown to improve AAD compared to simpler linear models. We aim to demonstrate that DNNs can improve attention decoding in CI users too, which would make them the state-of-the-art candidate for a neuro-steered CI.Approach. To this end, we first collected an EEG dataset on selective auditory attention from 25 bilateral CI users, and then implemented both a linear model as well as a convolutional neural network (CNN) for attention decoding. Moreover, we introduced a novel, objective CI-artifact removal strategy and evaluated its impact on decoding accuracy, alongside learnable speaker classification using a support vector machine (SVM).Main results. The CNN outperformed the linear model across all decision window sizes from 1 to 60 s. Removing CI artifacts modestly improved the CNN's decoding accuracy. With SVM classification, the CNN decoder reached a peak mean decoding accuracy of 74% at the population level for a 60 s decision window.Significance. These results demonstrate the superior potential of CNN-based decoding for neuro-steered CIs, which could improve speech perception of its users in cocktail party situations significantly.},
}

Humans
*Cochlear Implants
*Attention/physiology
*Speech Perception/physiology
Female
Male
Electroencephalography/methods
Middle Aged
Adult
*Neural Networks, Computer
Aged
Acoustic Stimulation/methods
Support Vector Machine

@article {pmid40484831,
year = {2025},
author = {Zhao, JZ},
title = {[A historical review and future outlook of neurosurgery in China].},
journal = {Zhonghua yi xue za zhi},
volume = {105},
number = {21},
pages = {1679-1685},
doi = {10.3760/cma.j.cn112137-20250325-00727},
pmid = {40484831},
issn = {0376-2491},
mesh = {*Neurosurgery/trends/history ; China ; Humans ; History, 20th Century ; History, 21st Century ; Societies, Medical ; Artificial Intelligence ; },
abstract = {Since its inception in the early 20th century at Peking Union Medical College Hospital, neurosurgery in China has gone through a century-long process from its initial establishment, development to modernization, forming a complete system, covering vascular diseases, tumors, epilepsy, and other diseases. This article reviews the key pioneers and historical milestones in Chinese neurosurgery, highlights the founding and advancement of the Society of Neurosurgery of Chinese Medical Association, and shows major achievements in standardization, training, and international cooperation, etc. At present, with the application of technologies such as artificial intelligence and brain-computer interfaces, network-based neurosurgery has emerged and developed rapidly, marking the transition to Neurosurgery 4.0. In the future, Chinese neurosurgery is poised to further promote interdisciplinary integration and clinical translation in support of the high-quality development of brain science.},
}

*Neurosurgery/trends/history
China
Humans
History, 20th Century
History, 21st Century
Societies, Medical
Artificial Intelligence

@article {pmid40483841,
year = {2025},
author = {Zakrzewski, S and Stasiak, B and Wojciechowski, A},
title = {Supervised factor selection in tensor decomposition of EEG signal.},
journal = {Computer methods and programs in biomedicine},
volume = {269},
number = {},
pages = {108866},
doi = {10.1016/j.cmpb.2025.108866},
pmid = {40483841},
issn = {1872-7565},
abstract = {BACKGROUND AND OBJECTIVE: Tensor decomposition methods are important tools for multidimensional data analysis, which have also proved useful for EEG signal processing. However, their direct application for tasks involving supervised training, such as EEG data classification in systems based on brain-computer interfaces, is limited by the inherently unsupervised nature of the optimization algorithms used for tensor factorization.

METHODS: In this work, we propose a solution for a motor imagery classification task based on parallel factor analysis (PARAFAC) of EEG data. The individual factors obtained through PARAFAC decomposition are subjected to statistical analysis, enabling us to select signal components relevant to the classification problem. To choose the rank of the decomposition, we propose a special score function based on cosine similarity of all possible pairs of decompositions.

RESULTS: The proposed method was shown to significantly increase the classification accuracy in the case of the best-performing subjects, when provided with an EEG signal satisfying certain conditions, such as sufficient trial length. Besides, representation of the statistically significant components in the form of a heatmap, defined over the space-frequency plane, proved suitable for direct interpretation in the context of event-related synchronization/desynchronization of cortical activity.

CONCLUSION: The proposed approach, joining universal tensor decomposition methods with statistical evaluation of the obtained components, has the potential to yield high accuracy and explainability of the results while significantly reducing the input space dimensionality.},
}

@article {pmid40483616,
year = {2025},
author = {Han, J and Zhan, G and Wang, L and Liang, D and Zhang, H and Zhang, L and Kang, X},
title = {Decoding EEG-based cognitive load using fusion of temporal and functional connectivity features.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-16},
doi = {10.1080/10255842.2025.2514132},
pmid = {40483616},
issn = {1476-8259},
abstract = {Evaluating cognitive load using electroencephalogram (EEG) signals is a crucial research area in the field of Brain-Computer Interfaces (BCI). However, achieving high accuracy and generalization in feature extraction and classification for cognitive load assessment remains a challenge, primarily due to the low signal-to-noise ratio of EEG signals and the inter-individual variability in EEG data. In this study, we propose a novel deep learning architecture that integrates temporal information features and functional connectivity features to enhance EEG signal analysis. Functional connectivity features capture inter-channel information, while temporal features are extracted from continuous signal segments using a Long Short-Term Memory (LSTM) network enhanced with an attention mechanism. The fusion strategy combines these two information streams to leverage their complementary strengths, resulting in improved classification performance. We evaluated our architecture on two publicly available datasets, and the results demonstrate its robustness in cognitive load recognition. Achieving performance comparable to the best existing methods on two public datasets. Ablation studies further substantiate the contributions of each module, demonstrating the importance of combining temporal and functional connectivity features for optimal results. These findings underscore the robustness and versatility of the proposed approach, paving the way for more effective EEG-based BCI applications.},
}

@article {pmid40482972,
year = {2025},
author = {M V, H and K, K and B, SB},
title = {An EEG-based imagined speech recognition using CSP-TP feature fusion for enhanced BCI communication.},
journal = {Behavioural brain research},
volume = {},
number = {},
pages = {115652},
doi = {10.1016/j.bbr.2025.115652},
pmid = {40482972},
issn = {1872-7549},
abstract = {BACKGROUND: Imagined speech has emerged as a promising paradigm for intuitive control of brain-computer interface (BCI)-based communication systems, providing a means of communication for individuals with severe brain disabilities. In this work, a non-invasive electroencephalogram (EEG)-based automated imagined speech recognition model was proposed to assist communication to convey the individual's intentions or commands. The proposed approach uses Common Spatial Patterns (CSP) and Temporal Patterns (TP) for feature extraction, followed by feature fusion to capture both spatial and temporal dynamics in EEG signals. This fusion of the CSP and TP domains enhances the discriminative power of the extracted features, leading to improved classification accuracy.

NEW METHOD: An EEG data set was collected from 15 subjects while performing an imagined speech task with a set of ten words that are more suitable for paralyzed patients. The EEG signals were preprocessed and a set of statistical characteristics was extracted from the fused CSP and TP domains. Spectral analysis of the signals was performed with respect to ten imagined words to identify the underlying patterns in EEG. Machine learning models, including Linear Discriminant Analysis (LDA), Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR), were employed for pairwise and multiclass classification.

RESULTS: The proposed model achieved average classification accuracies of 83.83% ± 5.94 and 64.58% ± 10.43 and maximum accuracies of 97.78% and 79.22% for pairwise and multiclass classification, respectively. These results demonstrate the effectiveness of the CSP-TP feature fusion approach, outperforming existing state-of-the-art methods in imagined speech recognition.

CONCLUSION: The findings suggest that EEG-based automatic imagined speech recognition (AISR) using CSP and TP techniques has significant potential for use in BCI-based assistive technologies, offering a more natural and intuitive means of communication for individuals with severe communication limitations.},
}

@article {pmid40481499,
year = {2025},
author = {Zhang, N and Huang, Z and Xia, Y and Tao, S and Wu, T and Sun, S and Zhu, Y and Jiang, G and Lu, X and Gao, Y and Guo, F and Cao, R and Chen, S and Zhang, L and Zou, X and Chen, M and Zhang, G},
title = {Remote ischemia precondition protects against renal IRI through apoptosis associated vesicles carrying MIF protein via modulating DUSP6/JNK signaling axis.},
journal = {Journal of nanobiotechnology},
volume = {23},
number = {1},
pages = {422},
pmid = {40481499},
issn = {1477-3155},
support = {tsgn202103116//Tai-Shan Scholar Program from Shandong Province/ ; 81900618//the National Natural Science Foundation of China/ ; 2023GX026//the Program of Scientific and Technological Development of Weifang/ ; GSP-LCYJFH11//Zhongda Hospital Affiliated to Southeast University, Jiangsu Province High-Level Hospital Construction Funds/ ; 2023YXZDXK02//Jiangsu Provincial Key Discipline and Laboratory Construction Funds of Urology/ ; CZXM-ZK-47//National clinical key discipline construction funds/ ; 202305033//Nanjing Key Science and Technology Special Project (Life and Health) - Medical-Engineering Collaborative Project/ ; 82100732//Natural Science Foundation of China/ ; },
abstract = {BACKGROUND: Remote ischemic preconditioning (rIPC) has been reported to protect against kidney ischemia-reperfusion injury (IRI) through the delivery of extracellular vesicles (EVs). Among these, apoptosis-induced compensatory proliferation signaling-related vesicles (ACPSVs) can transmit proliferation signals to surrounding cells. However, the underlying mechanisms remain unclear. This study aimed to investigate the role of ACPSVs in renal IRI following rIPC and to elucidate the associated mechanisms.

RESULTS: We demonstrated that rIPC plasma or ACPSVs alleviated renal damage and inflammation, with the protective effects abolished upon the removal of ACPSVs from the plasma. EVs isolated via differential centrifugation exhibited defining characteristics of ACPSVs. Co-culture experiments revealed that ACPSVs reduced apoptosis and enhanced the viability of HK-2 cells under hypoxia/reoxygenation (H/R) conditions. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses highlighted the critical role of macrophage migration inhibitory factor (MIF) protein in ACPSVs. Using CRISPR/Cas9 technology, we generated MIF-knockout HeLa cells to induce the production of MIF-deficient ACPSVs. The protective effects of ACPSVs were significantly attenuated when MIF was knocked out. Transcriptome sequencing and chromatin immunoprecipitation (ChIP) assays revealed that MIF suppresses dual-specificity phosphatase 6 (DUSP6) expression by promoting H3K9 trimethylation (H3K9me3) in the DUSP6 promoter region, thereby activating the JNK signaling pathway. In rescue experiments, treatment with the DUSP6 inhibitor BCI effectively restored the protective function of MIF-deficient ACPSVs.

CONCLUSION: This study underscores the protective role of ACPSVs derived from rIPC-treated rats and serum-starved cells against renal IRI through the MIF/DUSP6/JNK signaling axis, offering a potential clinical therapeutic strategy for acute kidney injury induced by IRI.

GRAPHICAL ABSTRACT: [Image: see text]

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12951-025-03505-9.},
}

@article {pmid40481295,
year = {2025},
author = {Zheng, J and Yu, J and Xu, M and Guan, C and Fu, Y and Shen, M and Chen, H},
title = {Expectation violation enhances short-term source memory.},
journal = {Psychonomic bulletin & review},
volume = {},
number = {},
pages = {},
pmid = {40481295},
issn = {1531-5320},
abstract = {Recent studies of short-term source amnesia demonstrated that source information is rapidly forgotten in memory, reflecting a highly selective mode of memory encoding. In this study, we explored the flexibility of memory selection by investigating whether short-term source amnesia is affected by expectation violations. In seven experiments, we first replicated the short-term source amnesia phenomenon and then induced various forms of expectation violations. The results consistently showed that the short-term source amnesia was significantly reduced or attenuated when expectation violation occurred, indicating a strengthening effect of expectation violation on short-term source memory. This effect occurred quite quickly, nearly at the same time as the occurrence of unexpected events. Moreover, the source memory was improved even when the unexpected events were completely irrelevant to the task set or target stimuli. These findings suggest that short-term memory tends to encode and maintain more detailed source information when encountering expectation violations, which might be an adaptive mechanism for handling unexpected environmental changes.},
}

@article {pmid40481078,
year = {2025},
author = {Peng, L and Wang, L and Wu, S and Gu, M and Deng, S and Liu, J and Cheng, CK and Sui, X},
title = {Biomechanics characterization of an implantable ultrathin intracortical electrode through finite element method.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {19938},
pmid = {40481078},
issn = {2045-2322},
support = {No. 2022ZD0208601//the STI 2030-Major Projects/ ; No. 2022ZD0208601//the STI 2030-Major Projects/ ; No. 2022ZD0208601//the STI 2030-Major Projects/ ; No. 2022ZD0208601//the STI 2030-Major Projects/ ; No. 2022ZD0208601//the STI 2030-Major Projects/ ; No. 2022ZD0208601//the STI 2030-Major Projects/ ; No. 2022ZD0208601//the STI 2030-Major Projects/ ; No. 62176158//the National Natural Science Foundation of China/ ; No. 62176158//the National Natural Science Foundation of China/ ; No. 62176158//the National Natural Science Foundation of China/ ; No. 62176158//the National Natural Science Foundation of China/ ; No. 62176158//the National Natural Science Foundation of China/ ; WH410362603/001//the STAR Project of Shanghai Jiao Tong University/ ; WH410362603/001//the STAR Project of Shanghai Jiao Tong University/ ; WH410362603/001//the STAR Project of Shanghai Jiao Tong University/ ; WH410362603/001//the STAR Project of Shanghai Jiao Tong University/ ; WH410362603/001//the STAR Project of Shanghai Jiao Tong University/ ; WH410362603/001//the STAR Project of Shanghai Jiao Tong University/ ; },
mesh = {Finite Element Analysis ; *Electrodes, Implanted ; Biomechanical Phenomena ; Microelectrodes ; Brain/physiology ; Humans ; Stress, Mechanical ; Brain-Computer Interfaces ; },
abstract = {Neural electrodes are widely used in brain-computer interfaces and neuroprosthesis for the treatment of various neurological disorders. However, as components that come into direct contact with neural tissue, implanted neural electrodes could cause mechanical damage during surgical insertions or while inside the brain. Thus, accurately and timely assessing this damage was vital for chronic implantation, which posed a significant challenge. This study aimed to evaluate the biomechanical effects and clinical application risks of a polyimide-based ultrathin flexible intracortical microelectrode through the finite element method (FEM). It analyzed the electrode-brain biomechanical effects during the electrode's insertion process and under steady-state acceleration with the electrode inside the brain. Furthermore, the study examined the impact of factors including implantation depth (ranging from 5 to 5000 μm), cortical thickness (0.5 mm, 2.5 mm, and 4.5 mm), step displacement (from 1 to 5 μm) during insertion, and acceleration direction (vertical and horizontal) on the electrode's biomechanical effects. The primary findings showed that the 98th percentile Von Mises Strain (ε98) and Von Mises Stress (σ98) in the region of interest (ROI) decreased dual-exponentially with increasing implantation depth and increased linearly with larger step displacements. Compared to the Von Mises strain threshold of 14.65%, as proposed by Sahoo et al., indicating a 50% risk of diffuse axonal injury (DAI), it was recommended to limit the initial step displacement during insertion to 1 μm, increasing to 5 μm at deeper locations (over 500 μm) to balance safety and efficiency. Additionally, it was found that cortical thickness had a negligible impact during insertion and while experiencing steady-state acceleration in vivo, with the three fitted curves almost coinciding when cortical thicknesses were 0.5 mm, 2.5 mm, and 4.5 mm. The flexible electrode exhibited excellent mechanical performance under steady-state acceleration in vivo, with ε98 being less than 0.3% and σ98 being less than 50 Pa, although it was more sensitive to horizontal acceleration. Thus, it could be concluded that during long-duration accelerations from transportation modes such as elevators and high-speed trains, the electrode's mechanical effects on brain tissue could be neglected, demonstrating long-term mechanical stability. This research was significant for guiding surgical insertion and clinical applications of flexible electrodes.},
}

Finite Element Analysis
*Electrodes, Implanted
Biomechanical Phenomena
Microelectrodes
Brain/physiology
Humans
Stress, Mechanical
Brain-Computer Interfaces

@article {pmid40481044,
year = {2025},
author = {Yi, W and Chen, J and Wang, D and Hu, X and Xu, M and Li, F and Wu, S and Qian, J},
title = {A multi-modal dataset of electroencephalography and functional near-infrared spectroscopy recordings for motor imagery of multi-types of joints from unilateral upper limb.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {953},
pmid = {40481044},
issn = {2052-4463},
support = {12275295//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62006014//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62006014//National Natural Science Foundation of China (National Science Foundation of China)/ ; 12275295//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Humans ; *Electroencephalography ; Spectroscopy, Near-Infrared ; Brain-Computer Interfaces ; *Upper Extremity/physiology ; *Imagination ; *Joints/physiology ; Movement ; },
abstract = {As one of the important brain-computer interface (BCI) paradigms, motor imagery (MI) enables the control of external devices via identification of motor intention by decoding the features of Electroencephalography (EEG). Movement imagination of multi-types of joints from the same limb allows the development of more accurate and intuitive BCI systems. In this work, we reported an open dataset including EEG and functional near-infrared spectroscopy (fNIRS) recordings from 18 subjects performing eight MI tasks from four types of joints including hand open/close, wrist flexion/extension, wrist abduction/adduction, elbow pronation/supination, elbow flexion/extension, shoulder pronation/supination, shoulder abduction/adduction, and shoulder flexion/extension, resulting in a total of 5760 trials. The validity of multi-modal data was verified both from the EEG/fNIRS activation patterns and the classification performance. It is expected that this dataset will facilitate the development and innovation of decoding algorithms for MI of multi-types of joints based on multi-modal EEG-fNIRS data.},
}

Humans
*Electroencephalography
Spectroscopy, Near-Infrared
Brain-Computer Interfaces
*Upper Extremity/physiology
*Imagination
*Joints/physiology
Movement

@article {pmid40480870,
year = {2025},
author = {Gunda, NK and Khalaf, MI and Bhatnagar, S and Quraishi, A and Gudala, L and Venkata, AKP and Alghayadh, FY and Alsubai, S and Bhatnagar, V},
title = {Retraction notice to "Lightweight attention mechanisms for EEG emotion recognition for brain computer interface".},
journal = {Journal of neuroscience methods},
volume = {},
number = {},
pages = {110502},
doi = {10.1016/j.jneumeth.2025.110502},
pmid = {40480870},
issn = {1872-678X},
}

@article {pmid40457516,
year = {2025},
author = {Zhang, Y and Deng, X and Wang, S and Zhou, W and Wu, Z and Tang, X and Lee, HJ and Zhang, D},
title = {High-Specificity Spatiotemporal Cholesterol Detection by Quadrature Phase-Shifted Polarization Stimulated Raman Imaging.},
journal = {Angewandte Chemie (International ed. in English)},
volume = {},
number = {},
pages = {e202505038},
doi = {10.1002/anie.202505038},
pmid = {40457516},
issn = {1521-3773},
support = {2024YFA1408900//National Key Research and Development Program of China/ ; 82372011//National Natural Science Foundation of China/ ; 12074339//National Natural Science Foundation of China/ ; 2025ZFJH01-01//Fundamental Research Funds for the Central Universities of China/ ; },
abstract = {Visualizing cholesterol dynamics in living systems in situ remains a fundamental challenge in biomedical imaging. Although fluorescence microscopy requires bulky tags that perturb small molecule behavior, stimulated Raman scattering (SRS) microscopy enables label-free detection of CH-rich molecules. However, conventional SRS probes only polarized Raman components, limiting molecular specificity by seemingly overlapped peaks. Here, we extend SRS microscopy to achieve rapid, comprehensive detection of Raman tensor through quadrature phase-shifted polarization SRS (QP[2]-SRS) microscopy. This technique exploits the underlying molecular signatures by detecting both polarized and depolarized components of third-order nonlinear susceptibility χ[(3)] that originates from molecular structural features. We adopt a specialized optical delay line that rapidly alternates between parallel- and perpendicular-polarization states. QP[2]-SRS enables unprecedented distinction of similar molecular species in complex mixtures, demonstrating approximately 10× enhancement in chemical specificity and 5× improvement in analytical accuracy. This enhanced sensitivity enables real-time monitoring of lipid dynamics in living C. elegans and reveals component heterogeneity and morphological changes of LD in NAFLD livers. QP[2]-SRS creates new opportunities for investigating cholesterol-dependent biological processes in their native environment, with broad potential for chemical imaging with enhanced molecular specificity.},
}

@article {pmid40480308,
year = {2025},
author = {Zhang, T and Jia, Y and Wang, N and Chai, X and He, Q and Cao, T and Mu, Q and Lan, Q and Zhao, J and Yang, Y},
title = {Recent advances in potential mechanisms of epidural spinal cord stimulation for movement disorders.},
journal = {Experimental neurology},
volume = {},
number = {},
pages = {115330},
doi = {10.1016/j.expneurol.2025.115330},
pmid = {40480308},
issn = {1090-2430},
abstract = {BACKGROUND: Epidural spinal cord stimulation (eSCS) has emerged as a promising neuromodulation technique for treating movement disorders. The underlying mechanisms of eSCS are still being explored, making it a compelling area for further research.

OBJECTIVE: This review aims to provide a comprehensive analysis of the mechanisms of eSCS, its stimulation parameters, and its clinical applications in movement disorders. It seeks to synthesize the current understanding of how eSCS interacts with the central nervous system to enhance motor function and promotes neural plasticity for sustained recovery.

METHODS: A literature search was performed in databases such as Web of Science, Scopus, and PubMed to identify studies on eSCS for movement disorders.

RESULTS: The therapeutic effects of eSCS are achieved through both immediate facilitative actions and long-term neural reorganization. By activating sensory neurons in the dorsal root, facilitating proprioceptive input and modulating spinal interneurons, eSCS enhances motor neuron excitability. Additionally, eSCS influences corticospinal interactions, increasing cortical excitability and promoting corticospinal circuit remodeling. Neuroplasticity plays a critical role in the long-term efficacy of eSCS, with evidence suggesting that stimulation can enhance axonal sprouting, synaptic formation, and neurotrophic factor expression while reducing neuroinflammation. Its regulation of the sympathetic nervous system further enhances recovery by improving blood flow, muscle tone, and other physiological parameters.

CONCLUSIONS: Epidural spinal cord stimulation shows promise in enhancing motor function and promoting neuroplasticity, but further research is needed to optimize treatment protocols and establish long-term efficacy.},
}

@article {pmid40480249,
year = {2025},
author = {Pritchard, M and Campelo, F and Goldingay, H},
title = {An investigation of multimodal EMG-EEG fusion strategies for upper-limb gesture classification.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ade1f9},
pmid = {40480249},
issn = {1741-2552},
abstract = {Objective: Upper-limb gesture identification is an important problem in the advancement of robotic prostheses. Prevailing research into classifying electromyographic (EMG) muscular data or electroencephalographic (EEG) brain data for this purpose is often limited in methodological rigour, the extent to which generalisation is demonstrated, and the granularity of gestures classified. This work evaluates three architectures for multimodal fusion of EMG & EEG data in gesture classification, including a novel Hierarchical strategy, in both subject-specific and subject-independent settings. Approach: We propose an unbiased methodology for designing classifiers centred on Automated Machine Learning through Combined Algorithm Selection & Hyperparameter Optimisation (CASH); the first application of this technique to the biosignal domain. Using CASH, we introduce an end-to-end pipeline for data handling, algorithm development, modelling, and fair comparison, addressing established weaknesses among biosignal literature. Main results: EMG-EEG fusion is shown to provide significantly higher subject-independent accuracy in same-hand multi-gesture classification than an equivalent EMG classifier. Our CASH-based design methodology produces a more accurate subject-specific classifier design than recommended by literature. Our novel Hierarchical ensemble of classical models outperforms a domain-standard CNN architecture. We achieve a subject-independent EEG multiclass accuracy competitive with many subject-specific approaches used for similar, or more easily separable, problems. Significance: To our knowledge, this is the first work to establish a systematic framework for automatic, unbiased designing and testing of fusion architectures in the context of multimodal biosignal classification. We demonstrate a robust end-to-end modelling pipeline for biosignal classification problems which if adopted in future research can help address the risk of bias common in multimodal BCI studies, enabling more reliable and rigorous comparison of proposed classifiers than is usual in the domain. We apply the approach to a more complex task than typical of EMG-EEG fusion research, surpassing literature-recommended designs and verifying the efficacy of a novel Hierarchical fusion architecture.},
}

@article {pmid40479831,
year = {2025},
author = {Li, C and Di, G and Li, Q and Sun, L and Wang, W and Wang, Y and Jiang, X and Wu, J},
title = {Microsurgical anatomy of the fiber tracts and vascular structures lateral to the internal capsule.},
journal = {Journal of neurosurgery},
volume = {},
number = {},
pages = {1-9},
doi = {10.3171/2025.2.JNS243025},
pmid = {40479831},
issn = {1933-0693},
abstract = {OBJECTIVE: The cerebral structures lateral to the internal capsule are frequently involved in studies of nervous system functions and diseases. This study aimed to investigate the fiber tracts and vascular structures of the brain lateral to the internal capsule using cranial specimens and specimen perfusion techniques.

METHODS: Ten cranial specimens were perfused via arteries and veins using specimen perfusion techniques and then processed using the fiber dissection method. The authors studied the fiber tracts and vascular structures from the cerebral cortex to the internal capsule, moving from lateral to medial.

RESULTS: The topographical relationships between the fiber tracts, nuclei, and vascular structures were identified. This was achieved by examining structures from the gray matter cortex of the brain's lateral surface, including U fibers, long association fiber tracts, and the insular lobe, extending to the level of the internal capsule.

CONCLUSIONS: Understanding the anatomical structures of white matter fiber tracts and vascular structures from the brain's lateral surface to the level of the internal capsule aids in planning safe, effective, and minimally invasive surgical procedures. It also contributes to advancements in neuroscience research.},
}

@article {pmid40478867,
year = {2025},
author = {Jiang, M and Luo, Q and Wang, X and Tan, Y},
title = {The "Dogs' Catching Mice" conjecture in Chinese phonogram processing.},
journal = {PloS one},
volume = {20},
number = {6},
pages = {e0324848},
pmid = {40478867},
issn = {1932-6203},
mesh = {*Phonetics ; Humans ; Male ; *Language ; Semantics ; Female ; China ; Adult ; Animals ; Young Adult ; East Asian People ; },
abstract = {In Chinese phonogram processing studies, it is not strange that phonetic radicals contribute phonologically to phonograms' phonological recognition. The present study, however, based on previous findings of phonetic radicals' proneness to semantic activation, as well as free-standing phonetic radicals' possession of triadic interconnections of orthography, phonology, and semantics at the lexical level, proposed that phonetic radicals may contribute semantically to the host phonograms' phonological recognition. We label this speculation as the "Dogs' Catching Mice" Conjecture. To examine this conjecture, three experiments were conducted. Experiment 1 was designed to confirm whether phonetic radicals, when embedded in phonograms, can contribute semantically to their host phonograms' phonological recognition. Experiment 2 was intended to show that the embedded phonetic radicals employed in Experiment 1 were truly semantically activated. Experiment 3, on top of the first two experiments, was devoted to demonstrating that the semantically activated phonetic radicals, when used as independent characters, can truly contribute semantically to their phonological recognition. Results from the three experiments combine to confirm the conjecture. The implication drawn is that phonetic radicals may have forged two paths in contributing to the host phonograms' phonological recognition: one is the regular "Cats' Catching Mice" Path, the other is the novel "Dogs' Catching Mice" Path.},
}

*Phonetics
Humans
Male
*Language
Semantics
Female
China
Adult
Animals
Young Adult
East Asian People

@article {pmid40478707,
year = {2025},
author = {Li, H and Zhang, H and Chen, Y},
title = {Dual-TSST: A Dual-Branch Temporal-Spectral-Spatial Transformer Model for EEG Decoding.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3577611},
pmid = {40478707},
issn = {2168-2208},
abstract = {The decoding of electroencephalography (EEG) signals allows access to user intentions conveniently, which plays an important role in the fields of human-machine interaction. To effectively extract sufficient characteristics of the multichannel EEG, a novel decoding architecture network with a dual-branch temporal-spectral-spatial transformer (Dual-TSST) is proposed in this study. Specifically, by utilizing convolutional neural networks (CNNs) on different branches, the proposed processing network first extracts the temporal-spatial features of the original EEG and the temporal-spectral-spatial features of time-frequency domain data converted by wavelet transformation, respectively. These perceived features are then integrated by a feature fusion block, serving as the input of the transformer to capture the global long-range dependencies entailed in the non-stationary EEG, and being classified via the global average pooling and multi-layer perceptron blocks. To evaluate the efficacy of the proposed approach, the competitive experiments are conducted on three publicly available datasets of BCI IV 2a, BCI IV 2b, and SEED, with the head-to-head comparison of more than ten other state-of-the-art methods. As a result, our proposed Dual-TSST performs superiorly in various tasks, which achieves the promising EEG classification performance of average accuracy of 82.79% in BCI IV 2a, 89.38% in BCI IV 2b, and 96.65% in SEED, respectively. Extensive ablation experiments conducted between the Dual-TSST and comparative baseline model also reveal the enhanced decoding performance with each module of our proposed method. This study provides a new approach to high-performance EEG decoding and has great potential for future CNN-Transformer based applications.},
}

@article {pmid40476694,
year = {2025},
author = {Nazareth, G},
title = {Speaking from the heart: a story about innovation, resilience, and infinite possibilities with AAC.},
journal = {Augmentative and alternative communication (Baltimore, Md. : 1985)},
volume = {},
number = {},
pages = {1-2},
doi = {10.1080/07434618.2025.2508491},
pmid = {40476694},
issn = {1477-3848},
abstract = {Communication is the cornerstone of human connection, impacting everything from our personal relationships to our professional success. This concept became heartbreakingly real for me when I was diagnosed with motor neuron disease at the age of 24. The rapid decline of my speech left me feeling all alone and isolated. After experimenting with AAC options, I yearned for a system that was lightweight, portable and stylish. This sparked my entrepreneurial spirit, leading me to assemble components catering to my diverse interests and professional pursuits. Over the years, I have built multiple AAC systems using different hardware platforms. Currently, I am focused on integrating emotional expression and faster communication speeds into AAC technology. Artificial intelligence, multi-modal inputs and non-invasive brain-computer interfaces hold immense potential for people who use AAC. Building my communication tools has revealed profound truths about living life to the fullest, accepting complete responsibility for our lives and embracing the good, the bad and the ugly. Through innovation and resilience, I have discovered infinite possibilities and I continue to use AAC to work miracles in my own life.},
}

@article {pmid40475558,
year = {2025},
author = {Sicorello, M and Gianaros, PJ and Wright, AGC and Bogdan, P and Kraynak, TE and Manuck, SB and Schmahl, C and Wager, TD},
title = {The functional neurobiology of negative affective traits across regions, networks, signatures, and a machine learning multiverse.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.05.15.653674},
pmid = {40475558},
issn = {2692-8205},
abstract = {Understanding the neural basis of negative affective traits like neuroticism remains a critical challenge across psychology, neuroscience, and psychiatry. Here, we investigate which level of brain organization-regions, networks, or validated whole-brain machine-learning signatures-best explains negative affective traits in a community sample of 458 adults performing the two most widely used affective fMRI tasks, viewing emotional faces and scenes. Neuroticism could not be predicted from brain activity, with Bayesian evidence against all theory-guided neural measures. However, preregistered whole-brain models successfully decoded vulnerability to stress, a lower-level facet of neuroticism, with results replicating in a hold-out sample. The neural stress vulnerability pattern demonstrated good psychometric properties and indicated that negative affective traits are best represented by distributed whole-brain patterns related to domain-general stimulation rather than localized activity. Together with results from a comprehensive multiverse analysis across 14 traits and 1,176 models- available for exploration in an online app-the findings speak against simplistic neurobiological theories of negative affective traits, highlight a striking gap between predicting individual differences (r <.35) and within-person emotional states (r =.88), and underscore the importance of aligning psychological constructs with neural measures at the appropriate level of granularity.},
}

@article {pmid40477046,
year = {2020},
author = {Amoo-Adare, EA},
title = {The Art of (Un)Thinking: When Hyper Productivity Says 'Enough!', Is a Feast.},
journal = {Postdigital science and education},
volume = {2},
number = {3},
pages = {606-613},
pmid = {40477046},
issn = {2524-4868},
}

@article {pmid40472937,
year = {2025},
author = {Qian, MB and Huang, JL and Wang, L and Zhou, CH and Zhu, TJ and Zhu, HH and He, YT and Zhou, XN and Lai, YS and Li, SZ},
title = {Clonorchiasis in China: geospatial modelling of the population infected or at risk, based on national surveillance.},
journal = {The Journal of infection},
volume = {},
number = {},
pages = {106528},
doi = {10.1016/j.jinf.2025.106528},
pmid = {40472937},
issn = {1532-2742},
abstract = {OBJECTIVES: Clonorchiasis is highly endemic in China. The unavailability of fine-scale distribution of population with infection and chemotherapy need hinders the control.

METHODS: This study established Bayesian geostatistical models to estimate age- and gender-specific prevalence of Clonorchis sinensis infection at high spatial resolution (5 × 5km[2]), based on the surveillance data in China between 2016 and 2021, together with socioeconomic, environmental and behavioral determinants. The population at risk and under infection, as well as chemotherapy need were then estimated.

RESULTS: In 2020, population-weighted prevalence of 0.67% (95% Bayesian credible interval (BCI): 0.58%-0.77%) was estimated for C. sinensis infection in China, corresponding to 9.46 million (95% BCI: 8.22 million-10.88 million) persons under infection. High prevalence was demonstrated in southern areas including Guangxi (8.92%, 95% BCI: 7.10%-10.81%) and Guangdong (2.99%, 95% BCI: 2.43%-3.74%). A conservative estimation of 99.13 million (95% BCI: 88.61 million-114.40 million) people were at risk of infection, of which 51.69 million (95% BCI: 45.48 million-57.84 million) need chemotherapy.

CONCLUSIONS: Clonorchiasis is an important public health problem in China, especially in southern areas due to the huge population at risk and large number of people under infection. Implementation of chemotherapy is urged to control the morbidity.

Environmental and socioeconomic data are open access (Table S1 in Supplementary Information). Epidemiological and behavioral data are not publicly available but are available on reasonable request after reviewed by the National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research).},
}

@article {pmid40472336,
year = {2025},
author = {Ranieri, A and Pichiorri, F and Colamarino, E and Cincotti, F and Mattia, D and Toppi, J},
title = {SPectral graph theory And Random walK (SPARK) toolbox for static and dynamic characterization of (di)graphs: A tutorial.},
journal = {PloS one},
volume = {20},
number = {6},
pages = {e0319031},
pmid = {40472336},
issn = {1932-6203},
mesh = {Humans ; Algorithms ; Electroencephalography ; Stroke/physiopathology ; *Software ; Brain/physiopathology/physiology ; },
abstract = {Spectral graph theory and its applications constitute an important forward step in modern network theory. Its increasing consensus over the last decades fostered the development of innovative tools, allowing network theory to model a variety of different scenarios while answering questions of increasing complexity. Nevertheless, a comprehensive understanding of spectral graph theory's principles requires a solid technical background which, in many cases, prevents its diffusion through the scientific community. To overcome such an issue, we developed and released an open-source MATLAB toolbox - SPectral graph theory And Random walK (SPARK) toolbox - that combines spectral graph theory and random walk concepts to provide a both static and dynamic characterization of digraphs. Once described the theoretical principles grounding the toolbox, we presented SPARK structure and the list of available indices and measures. SPARK was then tested in a variety of scenarios including: two-toy examples on synthetic networks, an example using public datasets in which SPARK was used as an unsupervised binary classifier and a real data scenario relying on functional brain networks extracted from the EEG data recorded from two stroke patients in resting state condition. Results from both synthetic and real data showed that indices extracted using SPARK toolbox allow to correctly characterize the topology of a bi-compartmental network. Furthermore, they could also be used to find the "optimal" vertex set partition (i.e., the one that minimizes the number of between-cluster links) for the underlying network and compare it to a given a priori partition. Finally, the application to real EEG-based networks provides a practical case study where the SPARK toolbox was used to describe networks' alterations in stroke patients and put them in relation to their motor impairment.},
}

Humans
Algorithms
Electroencephalography
Stroke/physiopathology
*Software
Brain/physiopathology/physiology

@article {pmid40471721,
year = {2025},
author = {Li, J and Fu, B and Li, F and Gu, W and Ji, Y and Li, Y and Liu, T and Shi, G},
title = {Applying SSVEP BCI on Dynamic Background.},
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.2025.3576984},
pmid = {40471721},
issn = {1558-0210},
abstract = {Brain-computer interfaces (BCIs) based on steady-state visual evoked potential (SSVEP) have attracted much attention due to their high efficiency and accuracy. The SSVEP paradigm and decoding methods have been extensively studied and achieved remarkable results. This study proposed two modulation methods for the SSVEP paradigm, namely color inversion modulation and brightness compression modulation. Color inversion modulation adjusts the stimulus to adapt to the changing background, while brightness compression modulation ensures high contrast by reducing the background brightness. Furthermore, we proposed Multi-scale Temporal-Spatial Global average pooling Neural Network (MTSGNN), an end-to-end network for decoding SSVEP signals evoked by the post-modulation paradigm. MTSGNN is built with efficient convolutional structures and uses global average pooling to achieve classification, which effectively reduces the risk of model overfitting on small EEG datasets and improves classification performance. We conduct experiments to evaluate the performance of the proposed modulation and decoding methods. Compared with color inversion modulation and no modulation, the brightness compression modulation method achieved the best performance. In addition, MTSGNN outperforms the best competitive decoding method by 11.98%, 3.9% and 5.15% under color inversion modulation, brightness compression modulation and no modulation, respectively. The experimental results demonstrate the effectiveness of the proposed modulation methods and the robustness of the proposed decoding method. This study significantly improves the performance of SSVEP in dynamic backgrounds and effectively expands the practical application scenarios of BCI.},
}

@article {pmid40471491,
year = {2025},
author = {Liu, X and Jia, Z and Xun, M and Wan, X and Lu, H and Zhou, Y},
title = {MSFHNet: a hybrid deep learning network for multi-scale spatiotemporal feature extraction of spatial cognitive EEG signals in BCI-VR systems.},
journal = {Medical & biological engineering & computing},
volume = {},
number = {},
pages = {},
pmid = {40471491},
issn = {1741-0444},
support = {62276022//National Natural Science Foundation of China/ ; 62206014//National Natural Science Foundation of China/ ; },
abstract = {The integration of brain-computer interface (BCI) and virtual reality (VR) systems offers transformative potential for spatial cognition training and assessment. By leveraging artificial intelligence (AI) to analyze electroencephalogram (EEG) data, brain activity patterns during spatial tasks can be decoded with high precision. In this context, a hybrid neural network named MSFHNet is proposed, optimized for extracting spatiotemporal features from spatial cognitive EEG signals. The model employs a hierarchical architecture where its temporal module uses multi-scale dilated convolutions to capture dynamic EEG variations, while its spatial module integrates channel-spatial attention mechanisms to model inter-channel dependencies and spatial distributions. Cross-stacked modules further refine discriminative features through deep-level fusion. Evaluations demonstrate the superiority of MSFHNet in the beta2 frequency band, achieving 98.58% classification accuracy and outperforming existing models. This innovation enhances EEG signal representation, advancing AI-powered BCI-VR systems for robust spatial cognitive training.},
}

@article {pmid40470749,
year = {2025},
author = {Li, W and Gao, C and Li, Z and Diao, Y and Li, J and Zhou, J and Zhou, J and Peng, Y and Chen, G and Wu, X and Wu, K},
title = {BrainFusion: a Low-Code, Reproducible, and Deployable Software Framework for Multimodal Brain‒Computer Interface and Brain‒Body Interaction Research.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {},
number = {},
pages = {e17408},
doi = {10.1002/advs.202417408},
pmid = {40470749},
issn = {2198-3844},
support = {2023YFC2414500//National Key Research and Development Program of China/ ; 2023YFC2414504//National Key Research and Development Program of China/ ; 81971585//Natural Science Foundation of China/ ; 72174082//Natural Science Foundation of China/ ; 82271953//Natural Science Foundation of China/ ; 82301688//Natural Science Foundation of China/ ; 2021B1515020064//Guangdong Basic and Applied Basic Research Foundation Outstanding Youth Project/ ; 2023B0303020001//Key Research and Development Program of Guangdong/ ; 2023B0303010003//Key Research and Development Program of Guangdong/ ; 2022A1515140142//Basic and Applied Basic Research Foundation of Guangdong Province/ ; 2024A1515013058//Natural Science Foundation of Guangdong Province/ ; 202206060005//Science and Technology Program of Guangzhou/ ; 202206080005//Science and Technology Program of Guangzhou/ ; 202206010077//Science and Technology Program of Guangzhou/ ; 202206010034//Science and Technology Program of Guangzhou/ ; 202201010093//Science and Technology Program of Guangzhou/ ; 2023A03J0856//Science and Technology Program of Guangzhou/ ; 2023A03J0839//Science and Technology Program of Guangzhou/ ; },
abstract = {This study presents BrainFusion, a unified software framework designed to improve reproducibility and support translational applications in multimodal brain-computer interface (BCI) and brain-body interaction research. While electroencephalography (EEG) -based BCIs have advanced considerably, integrating multimodal physiological signals remains hindered by analytical complexity, limited standardization, and challenges in real-world deployment. BrainFusion addresses these gaps through standardized data structures, automated preprocessing pipelines, cross-modal feature engineering, and integrated machine learning modules. Its application generator further enables streamlined deployment of workflows as standalone executables. Demonstrated in two case studies, BrainFusion achieves 95.5% accuracy in within-subject EEG-functional near-infrared spectroscopy (fNIRS) motor imagery classification using ensemble modeling and 80.2% accuracy in EEG-electrocardiography (ECG) sleep staging using deep learning, with the latter successfully deployed as an executable tool. Supporting EEG, fNIRS, electromyography (EMG) , and ECG, BrainFusion provides a low-code, visually guided environment, facilitating accessibility and bridging the gap between multimodal research and application in real world.},
}

@article {pmid40469097,
year = {2025},
author = {Wang, Z and Wang, Y},
title = {Multi-branch GAT-GRU-transformer for explainable EEG-based finger motor imagery classification.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1599960},
pmid = {40469097},
issn = {1662-5161},
abstract = {Electroencephalography (EEG) provides a non-invasive and real-time approach to decoding motor imagery (MI) tasks, such as finger movements, offering significant potential for brain-computer interface (BCI) applications. However, due to the complex, noisy, and non-stationary nature of EEG signals, traditional classification methods-such as Common Spatial Pattern (CSP) and Power Spectral Density (PSD)-struggle to extract meaningful, multidimensional features. While deep learning models like CNNs and RNNs have shown promise, they often focus on single-dimensional aspects and lack interpretability, limiting their neuroscientific relevance. This study proposes a novel multi-branch deep learning framework, termed Multi-Branch GAT-GRU-Transformer, to enhance EEG-based MI classification. The model consists of parallel branches to extract spatial, temporal, and frequency features: a Graph Attention Network (GAT) models spatial relationships among EEG channels, a hybrid Gated Recurrent Unit (GRU) and Transformer module captures temporal dependencies, and one-dimensional CNNs extract frequency-specific information. Feature fusion is employed to integrate these heterogeneous representations. To improve interpretability, the model incorporates SHAP (SHapley Additive exPlanations) and Phase Locking Value (PLV) analyses. Notably, PLV is also used to construct the GAT adjacency matrix, embedding biologically-informed spatial priors into the learning process. The proposed model was evaluated on the Kaya dataset, achieving a five-class MI classification accuracy of 55.76%. Ablation studies confirmed the effectiveness of each architectural component. Furthermore, SHAP and PLV analyses identified C3 and C4 as critical EEG channels and highlighted the Beta frequency band as highly relevant, aligning with known motor-related neural activity. The Multi-Branch GAT-GRU-Transformer effectively addresses key challenges in EEG-based MI classification by integrating domain-relevant spatial, temporal, and frequency features, while enhancing model interpretability through biologically grounded mechanisms. This work not only improves classification performance but also provides a transparent framework for neuroscientific investigation, with broad implications for BCI development and cognitive neuroscience research.},
}

@article {pmid40469096,
year = {2025},
author = {Li, P and Yu, D and Cheng, L and Wang, K},
title = {Influence of attentional state on EEG-based motor imagery of lower limb.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1545492},
pmid = {40469096},
issn = {1662-5161},
abstract = {INTRODUCTION: Motor imagery (MI) has emerged as a promising technique for enhancing motor skill acquisition and facilitating neural adaptation training. Attention plays a key role in regulating the neural mechanisms underlying MI. This study aims to investigate how attentional states modulate EEG-based lower-limb motor imagery performance by influencing event-related desynchronization (ERD) and the alpha modulation index (AMI) and to develop a real-time attention monitoring method based on the theta/beta ratio (TBR).

METHODS: Fourteen healthy right-handed subjects (aged 21-23) performed right-leg MI tasks, while their attentional states were modulated via a key-press paradigm. EEG signals were recorded using a 32-channel system and preprocessed with independent component analysis (ICA) to remove artifacts. Attentional states were quantified using both the traditional offline AMI and the real-time TBR index, with time-frequency analysis applied to assess ERD and its relationship with attention.

RESULTS: The results indicated a significant increase in ERD during high attentional states compared to low attentional states, with AMI values showing a strong positive correlation with ERD (r = 0.9641, p < 0.01). Cluster-based permutation testing confirmed that this α-band ERD difference was significant (corrected p < 0.05). Moreover, the TBR index proved to be an effective real-time metric, decreasing significantly under focused attention. Offline paired t-tests showed a significant TBR reduction [t (13) = 5.12, p = 2.4 × 10[-5]], and online analyses further validated second-by-second discrimination (Bonferroni-corrected p < 0.01). These findings confirm the feasibility and efficacy of TBR for real-time attention monitoring and suggest that enhanced attentional focus during lower-limb MI can improve signal quality and overall performance.

CONCLUSION: This study reveals that attentional states significantly influence the neural efficiency of lower-limb motor imagery by modulating ERD/AMI and demonstrates that the TBR can serve as a real-time indicator of attention, providing a novel tool for optimizing attentional processes in motor skill training.},
}

@article {pmid40468342,
year = {2025},
author = {Wang, M and Zhou, H and Zhang, X and Chen, Q and Tong, Q and Han, Q and Zhao, X and Wang, D and Lai, J and He, H and Zhang, S and Hu, S},
title = {Alleviating cognitive impairments in bipolar disorder with a novel DTI-guided multimodal neurostimulation protocol: a double-blind randomized controlled trial.},
journal = {BMC medicine},
volume = {23},
number = {1},
pages = {334},
pmid = {40468342},
issn = {1741-7015},
support = {52407261, 82201675//National Natural Science Foundation of China/ ; 52407261, 82201675//National Natural Science Foundation of China/ ; 2023YFC2506200//National Key Research and Development Program of China/ ; No. JNL-2023001B//Research Project of Jinan Microecological Biomedicine Shandong Laboratory/ ; 2021R52016//Leading Talent of Scientific and Technological Innovation-"Ten Thousand Talents Program" of Zhejiang Province/ ; 2020R01001//Innovation team for precision diagnosis and treatment of major brain diseases/ ; 2022KTZ004//Chinese Medical Education Association/ ; 226-2022-00193, 226-2022-00002, 2023ZFJH01-01, 2024ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; },
mesh = {Humans ; Double-Blind Method ; Female ; Male ; *Bipolar Disorder/therapy/complications/psychology ; *Diffusion Tensor Imaging/methods ; Adult ; *Transcranial Magnetic Stimulation/methods ; *Cognitive Dysfunction/therapy/etiology/diagnostic imaging ; Middle Aged ; *Transcranial Direct Current Stimulation/methods ; Treatment Outcome ; },
abstract = {BACKGROUND: Traditional neuromodulation strategies show promise in enhancing cognitive abilities in bipolar disorder (BD) but remain suboptimal. This study introduces a novel multimodal neurostimulation (MNS) protocol to improve therapeutic outcomes.

METHODS: The novel MNS protocol used individualized diffusion tensor imaging (DTI) data to identify fiber tracts between the dorsolateral prefrontal cortex and dorsal anterior cingulate cortex. The highest structural connectivity point is selected as the individualized stimulation site, which is then targeted using a combination of optimized transcranial alternating current stimulation (tACS) and robot-assisted navigated repetitive transcranial magnetic stimulation (rTMS). A double-blind randomized controlled trial was conducted to investigate the clinical efficacy of this innovative neuromodulation approach on cognitive abilities in stable-phase BD patients. One hundred BD patients were randomly assigned to four groups: group A (active tACS-active rTMS (MNS protocol)), group B (sham tACS-active rTMS), group C (active tACS-sham rTMS), and group D (sham tACS-sham rTMS). Participants underwent 15 sessions over 3 weeks. Cognitive assessments (THINC integrated tool) were conducted at baseline (week 0) and post-treatment (week 3).

RESULTS: Sixty-six participants completed all 15 sessions. Group A (MNS protocol) showed superior improvements in Spotter CRT, TMT, and DSST scores compared to other groups at week 3. Only group A exhibited significant activation in the left frontal region post-MNS intervention. The novel MNS protocol was well tolerated, with no significant side effects observed.

CONCLUSIONS: The study indicates that DTI-guided multimodal neurostimulation mode significantly improves cognitive impairments and is safe for stable-phase BD patients.

TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT05964777.},
}

Humans
Double-Blind Method
Female
Male
*Bipolar Disorder/therapy/complications/psychology
*Diffusion Tensor Imaging/methods
Adult
*Transcranial Magnetic Stimulation/methods
*Cognitive Dysfunction/therapy/etiology/diagnostic imaging
Middle Aged
*Transcranial Direct Current Stimulation/methods
Treatment Outcome

@article {pmid40467567,
year = {2025},
author = {Pang, J and Xu, J and Chen, L and Teng, H and Su, C and Zhang, Z and Gao, L and Zhang, R and Liu, G and Chen, Y and He, J and Pang, Y and Li, H},
title = {Family history, inflammation, and cerebellum in major depression: a combined VBM and dynamic functional connectivity study.},
journal = {Translational psychiatry},
volume = {15},
number = {1},
pages = {188},
pmid = {40467567},
issn = {2158-3188},
support = {222102310205//Science and Technology Department of Henan Province (Henan Provincial Department of Science and Technology)/ ; 62103377//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Humans ; *Depressive Disorder, Major/physiopathology/diagnostic imaging/pathology/genetics/blood ; Female ; Male ; Adult ; *Cerebellum/diagnostic imaging/physiopathology/pathology ; Magnetic Resonance Imaging ; *Inflammation/blood ; Interleukin-6/blood ; Gray Matter/diagnostic imaging/pathology ; C-Reactive Protein/metabolism/analysis ; Middle Aged ; Prefrontal Cortex/diagnostic imaging/physiopathology ; Case-Control Studies ; Young Adult ; },
abstract = {A family history (FH) of depression significantly influences the progress of major depressive disorder (MDD). However, the underlying neural mechanism of FH remains unclear. This study examined the association between brain structural and connectivity alterations, inflammation, and FH in MDD. A total of 134 MDD patients with (FH group, n = 43) and without (NFH group, n = 91) first-degree FH and 96 demographic-matched healthy controls (HCs) were recruited. Voxel-based morphometry (VBM) and sliding-window dynamic functional connectivity (dFC) analyses were performed, and inflammatory biomarkers (C-reactive protein (CRP) and interleukin-6 (IL-6)) were detected. Compared with HCs, FH and NFH groups showed decreased gray matter volume (GMV) in the left cerebellum posterior lobe and increased dFC between this region and the left inferior parietal lobule. The FH group showed increased dFC between the cerebellum region and medial prefrontal cortex (mPFC) compared to NFH and HCs. The combination of these brain measurements further differentiated between FH and NFH. Moreover, the GMV of the cerebellum was positively correlated with CRP in the NFH group, while the dFC between the cerebellum and mPFC was positively correlated with IL-6 in the FH group. The present findings indicate that cerebellar structure and dynamic function vary according to FH of MDD and are related to inflammatory factors, potentially offering novel insights into the underlying pathogenic mechanisms of MDD.},
}

Humans
*Depressive Disorder, Major/physiopathology/diagnostic imaging/pathology/genetics/blood
Female
Male
Adult
*Cerebellum/diagnostic imaging/physiopathology/pathology
Magnetic Resonance Imaging
*Inflammation/blood
Interleukin-6/blood
Gray Matter/diagnostic imaging/pathology
C-Reactive Protein/metabolism/analysis
Middle Aged
Prefrontal Cortex/diagnostic imaging/physiopathology
Case-Control Studies
Young Adult

@article {pmid40465456,
year = {2025},
author = {Li, C and Hasegawa, I and Tanigawa, H},
title = {Protocol for assisting frequency band definition and decoding neural dynamics using hierarchical clustering and multivariate pattern analysis.},
journal = {STAR protocols},
volume = {6},
number = {2},
pages = {103870},
doi = {10.1016/j.xpro.2025.103870},
pmid = {40465456},
issn = {2666-1667},
abstract = {Traditional fixed frequency band divisions often limit neural data analysis accuracy. Here, we present a protocol for assisting frequency band definition for multichannel neural data using macaque electrocorticography (ECoG) data. We describe steps for performing time-frequency analysis on preprocessed signals and applying hierarchical clustering to frequency power profiles to identify data-informed groupings. We then detail procedures for defining frequency bands guided by these clusters and using multivariate pattern analysis (MVPA) on the derived bands for functional validation via time-series decoding. For complete details on the use and execution of this protocol, please refer to Tanigawa et al.[1].},
}

@article {pmid40463690,
year = {2025},
author = {Rabiee, A and Ghafoori, S and Cetera, A and Abiri, R},
title = {Wavelet Analysis of Noninvasive EEG Signals Discriminates Complex and Natural Grasp Types.},
journal = {ArXiv},
volume = {},
number = {},
pages = {},
pmid = {40463690},
issn = {2331-8422},
abstract = {This research aims to decode hand grasps from Electroencephalograms (EEGs) for dexterous neuroprosthetic development and Brain-Computer Interface (BCI) applications, especially for patients with motor disorders. Particularly, it focuses on distinguishing two complex natural power and precision grasps in addition to a neutral condition as a no-movement condition using a new EEG-based BCI platform and wavelet signal processing. Wavelet analysis involved generating time-frequency and topographic maps from wavelet power coefficients. Then, by using machine learning techniques with novel wavelet features, we achieved high average accuracies: 85.16% for multiclass, 95.37% for No-Movement vs Power, 95.40% for No-Movement vs Precision, and 88.07% for Power vs Precision, demonstrating the effectiveness of these features in EEG-based grasp differentiation. In contrast to previous studies, a critical part of our study was permutation feature importance analysis, which highlighted key features for grasp classification. It revealed that the most crucial brain activities during grasping occur in the motor cortex, within the alpha and beta frequency bands. These insights demonstrate the potential of wavelet features in real-time neuroprosthetic technology and BCI applications.},
}

@article {pmid40462746,
year = {2025},
author = {Song, J and Chai, X and Zhang, X and Lv, Z and Wan, F and Yang, Y and Shan, X and Liu, J},
title = {HEGNet: EEG and EMG fusion decoding method in motor imagery and actual movement.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-14},
doi = {10.1080/10255842.2025.2512877},
pmid = {40462746},
issn = {1476-8259},
abstract = {The widespread adoption od brain-computer interface (BCI) has been hindered by the limited classification accuracy of electroencephalography (EEG) signals alone. This study proposes a novel BCI model, HEGNet, that addresses this challenge by fusing EEG and electromyography (EMG) signals. HEGNet incorporates an EMG feature extraction component to mitigate the inherent instability and low signal-to-noise ratio limitations of relying solely on EEG data. Additionally, HEGNet employs a feature fusion module to dynamically adjust the focus on EEG and EMG features, thereby enhancing its overall robustness. These findings suggest that EMG information can serve as a valuable supplement to EEG data.},
}

@article {pmid40031188,
year = {2025},
author = {Yu, H and Zeng, F and Liu, D and Wang, J and Liu, J},
title = {Neural Manifold Decoder for Acupuncture Stimulations With Representation Learning: An Acupuncture-Brain Interface.},
journal = {IEEE journal of biomedical and health informatics},
volume = {29},
number = {6},
pages = {4147-4160},
doi = {10.1109/JBHI.2025.3530922},
pmid = {40031188},
issn = {2168-2208},
mesh = {Humans ; *Electroencephalography/methods ; *Acupuncture Therapy/methods ; Male ; Adult ; *Brain-Computer Interfaces ; Female ; *Signal Processing, Computer-Assisted ; *Deep Learning ; Young Adult ; *Brain/physiology ; Neural Networks, Computer ; },
abstract = {Acupuncture stimulations in somatosensory system can modulate spatiotemporal brain activity and improve cognitive functions of patients with neurological disorders. The correlation between these somatosensory stimulations and dynamical brain responses is still unclear. We proposed a deep learning framework using electroencephalographic activity of stimulated subjects to decode the needling processes of various acupuncture manipulations performed on Zusanli acupoint. Contrastive representation learning integrated with domain adaptation strategy was applied to estimate 3D hand postures and hand joint motion trajectories of acupuncturist with video recordings, by which finite dimensional representations of behavior manifolds for needling operations were inferred. Distinct transition dynamics of behavior manifold were observed for acupuncture with lifting-thrusting and twisting-rotating manipulations. Moreover, latent neural manifolds of acupuncture evoked EEG signals were estimated in low dimensional state space of brain activities with unsupervised manifold learning, which can reliably represent acupuncture stimulations. Furthermore, a nonlinear decoder based on neural networks was designed to transform neural manifolds to behavior manifolds and further predict acupuncture manipulation as well as needling process. Experimental results demonstrated a high performance of the proposed decoding framework for four types of acupuncture manipulations with a precision of 92.42%. The EEG decoder provides an acupuncture-brain interface linking somatosensory stimulations with neural representations, an effective scheme for revealing clinical efficacy of acupuncture treatment.},
}

Humans
*Electroencephalography/methods
*Acupuncture Therapy/methods
Male
Adult
*Brain-Computer Interfaces
Female
*Signal Processing, Computer-Assisted
*Deep Learning
Young Adult
*Brain/physiology
Neural Networks, Computer

@article {pmid40461535,
year = {2025},
author = {He, X and Chen, J and Zhong, Y and Cen, P and Shen, L and Huang, F and Wang, J and Jin, C and Zhou, R and Zhang, X and Wang, A and Fan, J and Wu, S and Tu, M and Qin, X and Luo, X and Zhou, Y and Peng, J and Zhou, Y and Civelek, AC and Tian, M and Zhang, H},
title = {Forebrain neural progenitors effectively integrate into host brain circuits and improve neural function after ischemic stroke.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {5132},
pmid = {40461535},
issn = {2041-1723},
support = {82030049, 32027802//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82102095//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82302262//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82302267//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82394433//National Natural Science Foundation of China (National Science Foundation of China)/ ; LY23H180005//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; },
mesh = {Animals ; *Neural Stem Cells/transplantation/metabolism/cytology ; Rats ; Humans ; Forkhead Transcription Factors/metabolism/genetics ; *Prosencephalon/cytology ; Nerve Tissue Proteins/metabolism/genetics ; Neurons/metabolism/cytology ; *Ischemic Stroke/therapy/physiopathology/diagnostic imaging ; Induced Pluripotent Stem Cells/cytology/transplantation/metabolism ; Cell Differentiation ; Male ; Stem Cell Transplantation/methods ; Recovery of Function ; Rats, Sprague-Dawley ; Neurogenesis ; Disease Models, Animal ; *Stroke/therapy ; Positron-Emission Tomography ; Synapses ; },
abstract = {Human cortical neural progenitor cell transplantation holds significant potential in cortical stroke treatment by replacing lost cortical neurons and repairing damaged brain circuits. However, commonly utilized human cortical neural progenitors are limited in yield a substantial proportion of diverse cortical neurons and require an extended period to achieve functional maturation and synaptic integration, thereby potentially diminishing the optimal therapeutic benefits of cell transplantation for cortical stroke. Here, we generated forkhead box G1 (FOXG1)-positive forebrain progenitors from human inducible pluripotent stem cells, which can differentiate into diverse and balanced cortical neurons including upper- and deep-layer excitatory and inhibitory neurons, achieving early functional maturation simultaneously in vitro. Furthermore, these FOXG1 forebrain progenitor cells demonstrate robust cortical neuronal differentiation, rapid functional maturation and efficient synaptic integration after transplantation into the sensory cortex of stroke-injured adult rats. Notably, we have successfully utilized the non-invasive [18]F-SynVesT-1 PET imaging technique to assess alterations in synapse count before and after transplantation therapy of FOXG1 progenitors in vivo. Moreover, the transplanted FOXG1 progenitors improve sensory and motor function recovery following stroke. These findings provide systematic and compelling evidence for the suitability of these FOXG1 progenitors for neuronal replacement in ischemic cortical stroke.},
}

Animals
*Neural Stem Cells/transplantation/metabolism/cytology
Rats
Humans
Forkhead Transcription Factors/metabolism/genetics
*Prosencephalon/cytology
Nerve Tissue Proteins/metabolism/genetics
Neurons/metabolism/cytology
*Ischemic Stroke/therapy/physiopathology/diagnostic imaging
Induced Pluripotent Stem Cells/cytology/transplantation/metabolism
Cell Differentiation
Male
Stem Cell Transplantation/methods
Recovery of Function
Rats, Sprague-Dawley
Neurogenesis
Disease Models, Animal
*Stroke/therapy
Positron-Emission Tomography
Synapses

@article {pmid40460359,
year = {2025},
author = {Chen, Z and Zhang, Y and Ding, J and Li, Z and Tian, Y and Zeng, M and Wu, X and Su, B and Jiang, J and Wu, C and Wei, D and Sun, J and Lim, CT and Fan, H},
title = {Hydrogel-Based Multifunctional Deep Brain Probe for Neural Sensing, Manipulation, and Therapy.},
journal = {ACS nano},
volume = {},
number = {},
pages = {},
doi = {10.1021/acsnano.5c03865},
pmid = {40460359},
issn = {1936-086X},
abstract = {Implantable deep brain probes (DBPs) constitute a vital component of brain-machine interfaces, facilitating direct interaction between neural tissues and the external environment. Most multifunctional DBPs used for neural system sensing and modulation are currently fabricated through thermal tapering of polymeric materials. However, this approach faces a fundamental challenge in selecting materials that simultaneously accommodate the thermal stretching process and yet match the modulus of brain tissues. Here, we introduce a kind of multifunctional hydrogel-based fiber (HybF) designed for neural sensing, on-demand deep brain manipulation, and photodynamic therapy, and was achieved by integrating ion chelation/dechelation effects with templating methods throughout the entire wet-spinning process. With a low bending stiffness of approximately 0.3 N/m and a high conductivity of about 97 S/m at 1 kHz, HybF facilitates a high-quality signal recording (SNR ∼10) while minimizing immune rejection. It also effectively mediates deep brain optogenetic stimulation, successfully manipulating the behavior of hippocampal neurons in hSyn-ChrimsonR-tdTomato SD rats. Importantly, by leveraging HybF, this study explores the use of a spatiotemporally controllable photodynamic strategy in antiepilepsy, in which the high-amplitude abnormal electrical discharges were instantaneously eliminated without affecting normal cognitive/memory abilities. The above innovative approach established a distinct paradigm for deep brain manipulation and degenerative disease treatment, providing interesting insights into brain circuits and bioelectronic devices.},
}

@article {pmid40459463,
year = {2025},
author = {Savitz, BL and Dean, YE and Popa, NK and Cornely, RM and Byers, V and Gutama, BW and Abbott, EN and Torres-Guzman, R and Alter, N and Stehr, JD and Hill, JB and Elmaraghi, S},
title = {Targeted Muscle Reinnervation and Regenerative Peripheral Nerve Interface for Myoelectric Prosthesis Control: The State of Evidence.},
journal = {Annals of plastic surgery},
volume = {94},
number = {6S Suppl 4},
pages = {S572-S576},
doi = {10.1097/SAP.0000000000004273},
pmid = {40459463},
issn = {1536-3708},
mesh = {Humans ; *Artificial Limbs ; Electromyography ; *Nerve Regeneration/physiology ; *Muscle, Skeletal/innervation ; *Peripheral Nerves/physiology/surgery ; *Amputation Stumps/innervation ; Phantom Limb/prevention & control ; *Amputation, Surgical/rehabilitation ; },
abstract = {Prosthetic rehabilitation after amputation poses significant challenges, often due to functional limitations, residual limb pain (RLP), and phantom limb pain (PLP). These issues not only affect physical health but also mental well-being and quality of life. In this review, we describe targeted muscle reinnervation (TMR) and regenerative peripheral nerve interface (RPNI) and explore their clinical role in the evolution of myoelectric prosthetic control as well as postamputation pain and neuroma management. Early myoelectric prostheses, which detected electrical potentials from muscles to control prosthetic limbs, faced limitations such as inconsistent signal acquisition and complex control modes. Novel microsurgical techniques at the turn of the century such as TMR and RPNI significantly advanced myoelectric prosthetic control. TMR involves reinnervating denervated muscles with residual nerves to create electromyography (EMG) potentials and prevent painful neuromas. Similarly, RPNI relies on small muscle grafts to amplify EMG signals and distinguish from stochastic noise for refined prosthetic control. Techniques like TMR and RPNI not only improved prosthetic function, but also significantly reduced postamputation pain, making them critical in improving amputees' quality of life. Modern myoelectric prostheses evolved with advancements in microprocessor and sensor technologies, enhancing their functionality and user experience. Today, researchers have developed more intuitive and reliable prosthetic control by utilizing pattern recognition software and machine learning algorithms that may supersede reliance on surgically amplifying EMG signals. Future developments in brain-computer interfaces and machine learning hold promise for even greater advancements in prosthetic technology, emphasizing the importance of continued innovation in this field.},
}

Humans
*Artificial Limbs
Electromyography
*Nerve Regeneration/physiology
*Muscle, Skeletal/innervation
*Peripheral Nerves/physiology/surgery
*Amputation Stumps/innervation
Phantom Limb/prevention & control
*Amputation, Surgical/rehabilitation

@article {pmid40459258,
year = {2025},
author = {Seibert, B and Caceres, CJ and Gay, LC and Shetty, N and Faccin, FC and Carnaccini, S and Walters, MS and Marr, LC and Lowen, AC and Rajao, DS and Perez, DR},
title = {Air-liquid interface model for influenza aerosol exposure in vitro.},
journal = {Journal of virology},
volume = {},
number = {},
pages = {e0061925},
doi = {10.1128/jvi.00619-25},
pmid = {40459258},
issn = {1098-5514},
abstract = {UNLABELLED: Airborne transmission is an essential mode of infection and spread of influenza viruses among humans. However, most studies use liquid inoculum for virus infection. To better replicate natural airborne infections in vitro, we generated a calm-aerosol settling chamber system designed to examine the aerosol infectivity of influenza viruses in different cell types. Aerosol inoculation was characterized for multiple influenza A virus (FLUAV) subtypes, including pandemic 2009 H1N1, seasonal swine H3N2, and avian H9N2, using this exposure system. While each FLUAV strain displayed high infectivity within MDCK cells via liquid inoculation, differences in infectivity were observed during airborne inoculation. This was further observed in recently developed immortalized differentiated human airway epithelial cells (BCi-NS1.1) cultured in an air-liquid interface. The airborne infectious dose 50 for each virus was based on the exposure dose per well. Our findings indicate that this system has the potential to enhance our understanding of the factors influencing influenza transmission via the airborne route. This could be invaluable for conducting risk assessments, potentially reducing the reliance on extensive and costly in vivo animal studies.

IMPORTANCE: This study presents a significant advancement in influenza research by developing a novel in vitro system to assess aerosol infectivity, a crucial aspect of influenza transmission. The system's ability to differentiate between mammalian-adapted and avian-adapted influenza viruses based on their aerosol infectivity offers a valuable tool for pre-screening the pandemic potential of different strains. This could potentially streamline the risk assessment process and inform public health preparedness strategies. Moreover, the system's capacity to examine aerosol infectivity in human airway epithelial cells provides a more relevant model for studying virus-host interactions in natural airborne infections. Overall, this study provides an accessible platform for investigating aerosol infectivity, which could significantly contribute to our understanding of influenza transmission and pandemic preparedness.},
}

@article {pmid40459142,
year = {2025},
author = {Gao, J and Jiang, D and Wang, H and Wang, X},
title = {Opioid Enantiomers: Exploring the Complex Interplay of Stereochemistry, Pharmacodynamics, and Therapeutic Potential.},
journal = {Journal of medicinal chemistry},
volume = {},
number = {},
pages = {},
doi = {10.1021/acs.jmedchem.5c00136},
pmid = {40459142},
issn = {1520-4804},
abstract = {Opioids have been essential in pain management, particularly when other analgesics prove insufficient, but their use is complicated by risks of addiction, tolerance, and a range of adverse effects. These challenges are further exacerbated by the presence of opioid enantiomers that interact in a variety of ways with biological systems. This Perspective provides a comprehensive exploration of opioid enantiomers, focusing on their synthesis, pharmacodynamics, and potential therapeutic applications beyond traditional pain management. It highlights the complexity of synthesizing morphine enantiomers and additional challenges in producing the less-studied (+)-morphine. The Perspective also examines structure-activity relationship studies on (+)-opioid compounds, revealing their potential in modulating neuroinflammatory responses through non-opioid pathways and suggesting new therapeutic avenues for conditions like neuropathic pain and drug addiction. Furthermore, it discusses the differential safety profiles of opioid enantiomers, emphasizing the need for future research to advance precision medicine in opioid therapy, ultimately aiming to develop safer and more effective pain management strategies.},
}

@article {pmid40458259,
year = {2025},
author = {Zhang, W and Wang, T and Qin, C and Xu, B and Hu, H and Wang, T and Shen, Y},
title = {Vibration stimulation enhances robustness in teleoperation robot system with EEG and eye-tracking hybrid control.},
journal = {Frontiers in bioengineering and biotechnology},
volume = {13},
number = {},
pages = {1591316},
pmid = {40458259},
issn = {2296-4185},
abstract = {INTRODUCTION: The application of non-invasive brain-computer interfaces (BCIs) in robotic control is limited by insufficient signal quality and decoding capabilities. Enhancing the robustness of BCIs without increasing the cognitive load remains a major challenge in brain-control technology.

METHODS: This study presents a teleoperation robotic system based on hybrid control of electroencephalography (EEG) and eye movement signals, and utilizes vibration stimulation to assist motor imagery (MI) training and enhance control signals. A control experiment involving eight subjects was conducted to validate the enhancement effect of this tactile stimulation technique.

RESULTS: Experimental results showed that during the MI training phase, the addition of vibration stimulation improved the brain region activation response speed in the tactile group, enhanced the activation of the contralateral motor areas during imagery of non-dominant hand movements, and demonstrated better separability (p = 0.017). In the robotic motion control phase, eye movement-guided vibration stimulation effectively improved the accuracy of online decoding of MI and enhanced the robustness of the control system and success rate of the grasping task.

DISCUSSION: The vibration stimulation technique proposed in this study can effectively improve the training efficiency and online decoding rate of MI, helping users enhance their control efficiency while focusing on control tasks. This tactile enhancement technology has potential applications in robot-assisted elderly care, rehabilitation training, and other robotic control scenarios.},
}

@article {pmid40457127,
year = {2025},
author = {Xiao, Z and She, Q and Fang, F and Meng, M and Zhang, Y},
title = {Auxiliary classifier adversarial networks with maximum subdomain discrepancy for EEG-based emotion recognition.},
journal = {Medical & biological engineering & computing},
volume = {},
number = {},
pages = {},
pmid = {40457127},
issn = {1741-0444},
support = {62371172//National Natural Science Foundation of China/ ; 62271181//National Natural Science Foundation of China/ ; ZY2024025//Wenzhou Institute of Biomaterials and Engineering/ ; },
abstract = {Domain adaptation (DA) is considered to be effective solutions for unsupervised emotion recognition cross-session and cross-subject tasks based on electroencephalogram (EEG). However, the cross-domain shifts caused by individual differences and sessions differences seriously limit the generalization ability of existing models. Moreover, existing models often overlook the discrepancies among task-specific subdomains. In this study, we propose the auxiliary classifier adversarial networks (ACAN) to tackle these two key issues by aligning global domains and subdomains and maximizing subdomain discrepancies to enhance model effectiveness. Specifically, to address cross-domain discrepancies, we deploy a domain alignment module in the feature space to reduce inter-domain and inter-subdomain discrepancies. Meanwhile, to maximum subdomain discrepancies, the auxiliary adversarial classifier is introduced to generate distinguishable subdomain features by promoting adversarial learning between feature extractor and auxiliary classifier. System experiment results on three benchmark databases (SEED, SEED-IV, and DEAP) validate the model's effectiveness and superiority in cross-session and cross-subject experiments. The method proposed in this study outperforms other state-of-the-art DA, that effectively address domain shifts in multiple emotion recognition tasks, and promote the development of brain-computer interfaces.},
}

@article {pmid40456926,
year = {2025},
author = {Slutzky, MW and Vansteensel, MJ and Herff, C and Gaunt, RA},
title = {A brain-computer interface working definition.},
journal = {Nature biomedical engineering},
volume = {},
number = {},
pages = {},
pmid = {40456926},
issn = {2157-846X},
}

@article {pmid40456256,
year = {2025},
author = {Tangermann, M and Chevallier, S and Dold, M and Guetschel, P and Kobler, R and Papadopoulo, T and Thielen, J},
title = {Learning from Small Datasets - Review of Workshop 6 of the 10th International BCI Meeting 2023.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/addf80},
pmid = {40456256},
issn = {1741-2552},
abstract = {In brain-computer interfacing (BCI), a primary objective is to reduce calibration time by recording as few as possible novel data points to (re-)train decoder models. Minimizing the calibration can be crucial for enhancing the usability of a BCI application with patients, increasing the acceptance by healthy users, facilitating a fast adaptation during non-stationary recordings, or transferring between sessions. At the 10th International BCI Meeting in 2023, our workshop addressed the latest proposed techniques to train classification or regression machine learning models with small datasets. We explored methodologies from both traditional machine learning as well as deep learning. In addition to talks and discussions, we introduced Python toolboxes for all presented methods and for the benchmarking of classification models. This review provides a comprehensive overview of the workshop's content and discusses the insights that were obtained.},
}

@article {pmid40456243,
year = {2025},
author = {Galiotta, V and Caracci, V and Toppi, J and Pichiorri, F and Colamarino, E and Cincotti, F and Mattia, D and Riccio, A},
title = {P300-based Brain-Computer Interface for communication in Assistive Technology centres: influence of users' profile on BCI access.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/addf7f},
pmid = {40456243},
issn = {1741-2552},
abstract = {Assistive technology (AT) refers to any product that enables people to live independently and with dignity and to participate in activities of daily life. A Brain-Computer Interface (BCI) is an AT that provides an alternative output, based on neurophysiological signals, to control an external device. The aim of the study is to screen patients accessing an AT-center to investigate their eligibility for BCI access and the factors influencing the BCI control. Approach. Thirty-five users and 11 healthy subjects were included in the study. Participants were required to operate a P300-speller BCI. We evaluated the influence of clinical diagnosis, socio-demographic factors, level of dependence and disability of users, neuropsychological profile on BCI performance. Main results. The 7.1% of the users controlled the system with a mean accuracy of 93.6±8.0%, while 8 users had an online accuracy below 70%. We found that the neuropsychological profile significantly affected online accuracy and ITR. Significance. The percentage of users who had an accuracy considered functional for communication is an encouraging data in terms of BCI effectiveness. The results regarding accuracy and factors influencing (and not influencing) it, are a contribution to the introduction of BCIs in the AT-centers, considering the BCI for communication both as an AT and as an additional input to provide multimodal access to AT.},
}

@article {pmid40456242,
year = {2025},
author = {Schmid, PR and Sweeney-Reed, CM and Dürschmid, S and Reichert, C},
title = {Stimulus predictability has little impact on decoding of covert visual spatial attention.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/addf81},
pmid = {40456242},
issn = {1741-2552},
abstract = {Brain-computer interfaces (BCI) that are aimed at supporting completely locked-in patients require independence from eye movements. Since visual spatial attention (VSA) shifts precede eye movements, they can be used for non-invasive, gaze-independent BCI control. In VSA tasks, stimuli locations and presentation onsets are commonly unpredictable. In this study we investigated the impact of predictability of potential target stimuli on the decoding accuracy of a BCI. Approach. We presented visual stimuli simultaneously to the left and right visual fields while participants shifted attention to a target stimulus. Using canonical correlation analysis, we decoded the direction of attention under different combinations of temporal and spatial predictability and compared the performance. Main results. We found no variation in decoding accuracies with spatial predictability. In addition, jittered timing did not alter the decoding accuracy compared to a constant stimulus onset asynchrony (SOA). Finally, reducing the SOA enabled faster BCI communication without accuracy loss. Using time-resolved decoding and interpretable models, we show that a later positive difference wave (between 300 ms and 350 ms post-stimulus onset) at occipital sites, rather than the N2pc, primarily contributes to decoding the target receiving attention. Significance. Our results demonstrate that stimulus predictability has no beneficial impact on decoding accuracy, but the paradigm proved robust to alterations in various stimulus parameters, making VSA a promising cognitive process for use in non-invasive, gaze-independent BCI-based communication. .},
}

@article {pmid40456241,
year = {2025},
author = {Pang, Z and Zhang, R and Li, M and Li, Z and Cui, H and Chen, X},
title = {SSVEP-based BCI using ultra-low-frequency and high-frequency peripheral flickers.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/addf82},
pmid = {40456241},
issn = {1741-2552},
abstract = {OBJECTIVE: Existing steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems predominantly employ a flicker frequency range of 8-20 Hz, which often induces visual fatigue in users, thereby compromising system performance. Considering that, this study introduces an innovative paradigm to enhance the user experience of SSVEP-based BCIs while maintaining the performance.

APPROACH: The system encodes 12 targets by integrating ultra-low-frequency (2.00-3.32 Hz) and high-frequency (34.00-35.32 Hz) flickers with peripheral stimulation, and task-related component analysis (TRCA) is employed for SSVEP signal identification.

MAIN RESULTS: The feasibility of the ultra-low-frequency peripheral stimulation paradigm was validated through online experiments, achieving an average accuracy of 89.03 ± 9.95% and an information transfer rate (ITR) of 66.74 ± 15.44 bits/min. For the high-frequency peripheral stimulation paradigm, only the stimulation frequency changed, the paradigm, the signal processing algorithm and the step of frequency and phase were unchanged. The online experiments demonstrated an average accuracy of 93.55 ± 3.02% and an ITR of 51.88 ± 3.74 bits/min.

SIGNIFICANCE: The performance of the proposed system has reached a relatively high level among the current user-friendly SSVEP-based BCI systems. This study successfully innovates the paradigm for SSVEP-based BCIs, offering new insights into the development of user-friendly systems that balance high performance and user comfort.},
}

@article {pmid40456131,
year = {2025},
author = {Jing, S and Dai, Z and Liu, X and Yang, X and Cheng, J and Chen, T and Feng, Z and Liu, X and Dong, F and Xin, Y and Han, Z and Hu, H and Su, X and Wang, C},
title = {Correction: Effectiveness of Neurofeedback-Assisted and Conventional 6-Week Web-Based Mindfulness Interventions on Mental Health of Chinese Nursing Students: Randomized Controlled Trial.},
journal = {Journal of medical Internet research},
volume = {27},
number = {},
pages = {e78147},
doi = {10.2196/78147},
pmid = {40456131},
issn = {1438-8871},
abstract = {[This corrects the article DOI: 10.2196/71741.].},
}

@article {pmid40456094,
year = {2025},
author = {Arpaia, P and Esposito, A and Galdieri, F and Natalizio, A},
title = {Acquisition delay of wireless EEG instruments in time-sensitive applications.},
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.2025.3575695},
pmid = {40456094},
issn = {1558-0210},
abstract = {The aim of this study is to characterize the acquisition delay in wireless EEG instruments and evaluate its impact on the detection of time-locked neural phenomena, such as P300 and movement-related cortical potentials (MRCP). Accurate timing is critical for both research and clinical applications, especially for real-time brain-computer interfaces (BCI). A measurement setup was thus developed to assess acquisition delays and their uncertainty. Delays were measured at both the start and stop of a reference signal generation to investigate the consistency and reliability of the devices. BCI experiments were also performed to evaluate the impact of the measured delay on the detection of the time-locked phenomena. Statistical tests confirmed significant differences in delays across devices and configurations (e.g., from few tens to a hundred ms). These delays directly impacted P300 and MRCP detection, raising concerns about potential misclassification. Nonetheless, the correction of the measured acquisition delay proved beneficial, especially with regard to the P300 latency measured through low-cost instrumentation.},
}

@article {pmid40456080,
year = {2025},
author = {Jin, L and Song, Y and Zhao, H and Cao, J and Cheung, VCK and Liao, WH},
title = {Frequency-Aware Spatial-Temporal Attention Explainable Network for EEG Decoding.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3576088},
pmid = {40456080},
issn = {2168-2208},
abstract = {Representation learning in spatial and temporal domains has shown significant potential in EEG decoding, advancing the field of brain-computer interfaces (BCIs). However, the critical role of frequency information, closely tied to the brain's neurological mechanism, has been largely neglected. In this paper, we propose FSTNet, which integrates frequency-spatial-temporal domains synergistically. The network allows broadband EEG signals as input and adaptively learns informative frequency signatures. A frequency-aware module emphasizes the importance of frequency information by selectively assigning weights to latent representations in the frequency space. Subsequently, self-attention captures spatial and temporal dependencies, extracting discriminative neural signatures for EEG decoding. We conducted extensive experiments on EEG datasets for motor imagery and emotion recognition, achieving superior results of 90.17% on SEED, 88.38% on PhysioNet, and 77.02% on the OpenBMI dataset in both individual and cross-subject scenarios. Additionally, visualization reveals that the network captures informative frequency ranges and spatial patterns associated with specific tasks, aligning with known physiological mechanisms. This enhances the transparency of the network's learning process. In conclusion, our method exhibits the potential for decoding EEG and advancing the understanding of neurological processes in the brain.},
}

@article {pmid40455568,
year = {2025},
author = {Chen, Y and Fan, Z and Shi, N and Cheng, B and Huang, C and Liu, X and Gao, X and Liu, R},
title = {MXene-Based Microneedle Electrode for Brain-Computer Interface in Diverse Scenarios.},
journal = {ACS applied materials & interfaces},
volume = {},
number = {},
pages = {},
doi = {10.1021/acsami.5c03798},
pmid = {40455568},
issn = {1944-8252},
abstract = {In this study, we introduce a brain-computer interface (BCI) framework incorporating MXene microneedle EEG electrodes, tailored for versatile deployment. The dry electrodes, configured as 1 mm[2] microneedles, underwent meticulous processing to establish a cohesive integration with the MXene conductive material. The microneedle architecture facilitates epidermal penetration, yielding low contact impedance, enabling the recording of spontaneous EEG and induced brain activity, and ensuring high precision in steady-state visual evoked potential (SSVEP) speller. Simultaneously, the microneedle electrode demonstrates commendable biological compatibility and superior nuclear magnetic resonance compatibility. It exhibits minimal artifact generation and manifests no heating-related adaptations in nuclear magnetic environments. The inherent microneedle electrode structure endows it with robust anti-interference capabilities. In vibrational environments, the SSVEP text input accuracy of the microneedle electrode remains comparable to that of gel electrodes, maintaining consistent impedance and delivering high-fidelity EEG acquisition during real-motion scenarios. The microneedle electrode devised in this study serves as a reliable signal acquisition tool, thereby advancing the development of BCI systems tailored for practical usage scenarios.},
}

@article {pmid40454682,
year = {2025},
author = {Pitt, KM and Mikuls, A and Ousley, CL and Boster, JB and Mahmoudi, M and McCarthy, J and Burnison, J},
title = {Considering whether brain-computer interfaces have prospective potential to support children who have the physical abilities for touch-based AAC access: a forum manuscript.},
journal = {Augmentative and alternative communication (Baltimore, Md. : 1985)},
volume = {},
number = {},
pages = {1-9},
doi = {10.1080/07434618.2025.2495897},
pmid = {40454682},
issn = {1477-3848},
abstract = {Augmentative and alternative communication (AAC) may help address communication challenges for both those with developmental disabilities (DD) and intellectual and developmental disabilities (IDD). This forum manuscript explores the possibility of various future applications of brain-computer interface technology for AAC control (BCI-AAC) by children who have the physical abilities to utilize touch-based AAC access. Due to the early status of BCI-AAC research, the forum focuses on those with DD, though considerations for those with IDD are also discussed. Departing from the prevalent focus on severe speech and physical impairments (SSPI), this work shifts the spotlight toward children who may employ touch selection for AAC access, exploring the challenges and prospective possibilities within this population. Applying the International Classification of Functioning, Disability, and Health (ICF) framework, we explore potential BCI-AAC considerations across Activities and Participation, Functions and Structures, Environmental Factors, and Personal Factors. Proposing prospective BCI-AAC strategies, such as leveraging brain activity for functional intent recognition and emotion detection, this paper is designed to fuel discussion on tailoring AAC interventions to the diverse profiles of children with DD and IDD. Acknowledging the significant hurdles faced by BCI-AAC technology, we support the inclusive consideration of individuals in BCI-AAC development. While not seeking to lay a definitive roadmap, this forum aims to serve as a catalyst for future interdisciplinary dialogues, including those who use AAC and their support network, laying the groundwork for considering diverse BCI-AAC applications in children.},
}

@article {pmid40450930,
year = {2025},
author = {Fan, C and Song, Y and Mao, X},
title = {A classification method of motor imagery based on brain functional networks by fusing PLV and ECSP.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {190},
number = {},
pages = {107684},
doi = {10.1016/j.neunet.2025.107684},
pmid = {40450930},
issn = {1879-2782},
abstract = {In order to enhance the decoding ability of brain states and evaluate the functional connection changes of relevant nodes in brain regions during motor imagery (MI), this paper proposes a brain functional network construction method which fuses edge features and node features. And we use deep learning methods to realize MI classification of left and right hand grasping tasks. Firstly, we use phase locking value (PLV) to extract edge features and input a weighted PLV to enhanced common space pattern (ECSP) to extract node features. Then, we fuse edge features and node features to construct a novel brain functional network. Finally, we construct an attention and multi-scale feature convolutional neural network (AMSF-CNN) to validate our method. The performance indicators of the brain functional network on the SHU_Dataset in the corresponding brain region will increase and be higher than those in the contralateral brain region when imagining one hand grasping. The average accuracy of our method reaches 79.65 %, which has a 25.85 % improvement compared to the accuracy provided by SHU_Dataset. By comparing with other methods on SHU_Dataset and BCI IV 2a Dataset, the average accuracies achieved by our method outperform other references. Therefore, our method provides theoretical support for exploring the working mechanism of the human brain during MI.},
}

@article {pmid40450863,
year = {2025},
author = {Niu, X and Zhang, J and Peng, Y and Kong, Y and Li, Y and Han, Y and Shi, L and Zheng, G},
title = {Extraction and analysis of abnormal EEG features in children with amblyopia.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {175},
number = {},
pages = {2110765},
doi = {10.1016/j.clinph.2025.2110765},
pmid = {40450863},
issn = {1872-8952},
abstract = {OBJECTIVE: Early and accurate diagnosis of amblyopia is crucial for the healthy development of children. Existing clinical diagnostic methods rely on patient cooperation, which can easily lead to misdiagnosis. The commonly used features derived from visual evoked potentials (VEP) only provided limited information for characterizing the whole brain, highlighting the need for integrating additional data sources, such as brain network metrics, to achieve a more comprehensive understanding.

METHODS: We extracted 488 features from 64-channel EEG data recorded from thirty amblyopic children. The features mainly derived from a weighted functional brain network based on coherence across different frequency bands. Feature selection and linear classification techniques were employed to assess their effectiveness in distinguishing amblyopia from normal children.

RESULTS: Abnormal EEG features were distributed not only in the occipital lobe but also in non-visual regions, with a higher prevalence in the alpha and beta bands. Their decoding performance surpassed traditional VEP features, and their combination achieved the highest accuracy (89.00%). Moreover, features beyond the occipital lobe exhibited limited decoding performance when considered individually, yet they still have an obvious contribution.

CONCLUSIONS: The study identified novel abnormal EEG features associated with amblyopia and demonstrated the potential of multi-channel EEG recordings to assist in the diagnosis of amblyopia.

SIGNIFICANCE: The study suggests amblyopia may impair more abilities beyond visual cognition and further provides objective biomarkers for diagnosing amblyopia, which is essential for effective treatment.},
}

@article {pmid40450806,
year = {2025},
author = {Wosnick, N and Dörfer, T and Turner, M and Nicholls, C and Richardson, M and Génier, I and Hauser-Davis, RA},
title = {Assessing the potential physiological impacts of urban development around lemon shark (Negaprion brevirostris) nurseries: effects on neonate and juvenile health.},
journal = {Marine pollution bulletin},
volume = {218},
number = {},
pages = {118233},
doi = {10.1016/j.marpolbul.2025.118233},
pmid = {40450806},
issn = {1879-3363},
abstract = {Urbanization driven by population growth, development and tourism increasingly threatens even remote areas, potentially impacting biodiversity. This is particularly concerning given the ecological and economic importance of biodiversity, especially for island nations, where ecotourism plays a crucial role in the economy. This study examines urban-driven degradation effects on the nurseries of lemon sharks, a predator with strong site fidelity to its birthing and nursery areas. Six sites in South Eleuthera, The Bahamas, were assessed, analyzing proxies indicative of body condition (triglycerides/cholesterol ratio, body condition index) and energetic stress markers (glucose, β-hydroxybutyrate, triglycerides, total cholesterol) in neonates and juveniles compared across nurseries relative to degradation scores. While TAG/CHOL and BCI were not significantly different between nurseries, energetic markers were overall higher in more degraded nurseries. Moreover, total urban score was a significant predictor for glucose, β-hydroxybutyrate, and triglyceride ciruclating concentrations. These findings, coupled with prior studies carried out in Bimini, suggest that urban development around lemon shark nurseries in The Bahamas may negatively impact shark health. Cooperative monitoring, community initiatives for mangrove preservation, and stronger urbanization laws are required to mitigate these impacts. As urbanization and environmental degradation are universal threats to mangroves worldwide, this approach can be adapted to study urbanization impacts on other species in regions such as Southeast Asia, the Caribbean, the Pacific Islands, and the coasts of Africa and South America, which face similar urban encroachment, habitat degradation, and biodiversity loss challenges.},
}

@article {pmid40450046,
year = {2025},
author = {Maltezou-Papastylianou, C and Scherer, R and Paulmann, S},
title = {Human voices communicating trustworthy intent: A demographically diverse speech audio dataset.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {921},
pmid = {40450046},
issn = {2052-4463},
mesh = {Humans ; Adult ; Middle Aged ; Female ; Male ; *Voice ; Young Adult ; Adolescent ; *Speech ; *Trust ; },
abstract = {The multi-disciplinary field of voice perception and trustworthiness lacks accessible and diverse speech audio datasets representing diverse speaker demographics, including age, ethnicity, and sex. Existing datasets primarily feature white, younger adult speakers, limiting generalisability. This paper introduces a novel open-access speech audio dataset with 1,152 utterances from 96 untrained speakers, across white, black and south Asian backgrounds, divided into younger (N = 60, ages 18-45) and older (N = 36, ages 60+) adults. Each speaker recorded both, their natural speech patterns (i.e. "neutral" or no intent), and their attempt to convey their trustworthy intent as they perceive it during speech production. Our dataset is described and evaluated through classification methods between neutral and trustworthy speech. Specifically, extracted acoustic and voice quality features were analysed using linear and non-linear classification models, achieving accuracies of around 70%. This dataset aims to close a crucial gap in the existing literature and provide additional research opportunities that can contribute to the generalisability and applicability of future research results in this field.},
}

Humans
Adult
Middle Aged
Female
Male
*Voice
Young Adult
Adolescent
*Speech
*Trust

@article {pmid40448829,
year = {2025},
author = {Marques, LM and Strauss, A and Castellani, A and Barbosa, S and Simis, M and Fregni, F and Battistella, L},
title = {Dynamics of sensorimotor-related brain oscillations: EEG insights from healthy individuals in varied upper limb movement conditions.},
journal = {Experimental brain research},
volume = {243},
number = {7},
pages = {160},
pmid = {40448829},
issn = {1432-1106},
support = {#21/05897-5//Fundação de Amparo à Pesquisa do Estado de São Paulo/ ; #21/12790-2//Fundação de Amparo à Pesquisa do Estado de São Paulo/ ; #20/08512-4//Fundação de Amparo à Pesquisa do Estado de São Paulo/ ; #17/12943-8//Fundação de Amparo à Pesquisa do Estado de São Paulo/ ; #17/12943-8//Fundação de Amparo à Pesquisa do Estado de São Paulo/ ; },
mesh = {Humans ; Male ; Female ; Adult ; Young Adult ; Cross-Sectional Studies ; Electroencephalography ; *Sensorimotor Cortex/physiology ; *Upper Extremity/physiology ; *Brain Waves/physiology ; Movement/physiology ; *Motor Activity/physiology ; Brain-Computer Interfaces ; *Psychomotor Performance/physiology ; *Cortical Synchronization/physiology ; Imagination/physiology ; Middle Aged ; },
abstract = {Event-related desynchronization (ERD) and event-related synchronization (ERS) are critical neurophysiological phenomena associated with motor execution and inhibitory processes. Their utility spans neurophysiological biomarker research and Brain-Computer Interface (BCI) development. However, standardized frameworks for analyzing ERD and ERS oscillations across motor tasks and frequency ranges remain scarce. This study conducted a cross-sectional analysis of 76 healthy participants from the DEFINE cohort to explore ERD and ERS variations across four motor-related tasks (Motor Execution, Motor Imagery, Active Observation, and Passive Observation) and six frequency bands (Delta, Theta, Low Alpha, High Alpha, Low Beta, and High Beta) using C3 electrode activity. Repeated measures ANOVA revealed task-sensitive ERD and ERS power modulations, with oscillatory responses spanning the 1-30 Hz spectrum. Beta activity exhibited pronounced differences between tasks, highlighting its relevance in motor control, while other bands showed distinct task-dependent variations. These findings underscore the variability in ERD/ERS patterns across different tasks and frequency bands, reinforcing the importance of further research into standardized analytical frameworks. By refining ERD/ERS analyses, our study contributes to developing reference frameworks that can enhance clinical and Brain-Computer Interface (BCI) applications.},
}

Humans
Male
Female
Adult
Young Adult
Cross-Sectional Studies
Electroencephalography
*Sensorimotor Cortex/physiology
*Upper Extremity/physiology
*Brain Waves/physiology
Movement/physiology
*Motor Activity/physiology
Brain-Computer Interfaces
*Psychomotor Performance/physiology
*Cortical Synchronization/physiology
Imagination/physiology
Middle Aged

@article {pmid40448287,
year = {2025},
author = {Cao, L and Zheng, Q and Wu, Y and Liu, H and Guo, M and Bai, Y and Ni, G},
title = {A dual-modality study on the neural features of cochlear implant simulated tone and consonant perception.},
journal = {Annals of the New York Academy of Sciences},
volume = {},
number = {},
pages = {},
doi = {10.1111/nyas.15380},
pmid = {40448287},
issn = {1749-6632},
support = {2023YFF1203500//National Key Research and Development Program of China/ ; 824B2056//National Natural Science Foundation of China/ ; },
abstract = {Accurately perceiving lexical tones and consonants is critical for understanding speech in tonal languages. Cochlear implant (CI) users exhibit reduced phonetic perception due to spectral loss in CI encoding, yet the underlying neural mechanisms remain unclear. This study combined electroencephalography and functional near-infrared spectroscopy (fNIRS) to investigate the neural processing mechanisms of CI-simulated channelized speech in 26 normal-hearing adults during the processing of tones (T1-T4) and consonants ("ba," "da," "ga," "za"). Results showed that the N1 amplitude in auditory evoked potentials was significantly lower for channelized speech than a natural human voice (NH), particularly for T2 and T4 tones, indicating a weaker perception of channelized speech. Functional connectivity analysis revealed that an NH exhibited significantly higher synchrony in the δ and θ frequency bands than channelized speech, which was more pronounced in the right temporal lobe. This finding was also observed with "za" consonants. fNIRS results showed stronger right temporal lobe activation for channelized speech, suggesting that the brain requires greater auditory effort to process channelized speech. Combining both modalities revealed neural compensatory mechanisms underlying channelized speech-manifesting as "low-efficiency perception with high cognitive load." This study provides potential biomarkers for CI rehabilitation assessment and a foundation for future research.},
}

@article {pmid40446349,
year = {2025},
author = {Wang, S and Chen, G and Xie, J and Yang, R and Wang, X and Shan, Q and Liu, W and Zhao, D and Wang, F and Li, K and Zhang, Q and Guo, Y},
title = {Development and validation of a predictive model for poor initial outcomes after Gamma Knife radiosurgery for trigeminal neuralgia: a prognostic correlative analysis.},
journal = {Journal of neurosurgery},
volume = {},
number = {},
pages = {1-12},
doi = {10.3171/2025.2.JNS242655},
pmid = {40446349},
issn = {1933-0693},
abstract = {OBJECTIVE: The present study aimed to develop a reliable predictive model for identifying preoperative predictors of poor initial outcomes in patients with primary trigeminal neuralgia (PTN) treated with Gamma Knife radiosurgery (GKRS) and further elucidate the clinical significance of these predictors in initial outcomes and long-term pain recurrence.

METHODS: A total of 217 PTN patients were divided into a training set (n = 167) and a validation set (n = 50). The initial outcomes of GKRS treatment were assessed based on the Barrow Neurological Institute pain intensity scale. A predictive model was developed through multivariate regression and validated with repeated sampling. The differences in predictors of long-term pain recurrence were assessed using Kaplan-Meier analysis. The association between predictors was tested using chi-square tests, and subgroup analyses were performed to compare initial outcomes and long-term pain recurrence between two clinically significant correlates.

RESULTS: The training and validation sets showed areas under the curve of 0.85 and 0.88, respectively. Calibration curves and decision curve analysis indicated significant clinical benefits in both sets. Independent risk factors for poor initial outcomes included hyperglycemia, absence of neurovascular contact, carbamazepine insensitivity, and atypical pain (trigeminal neuralgia type 2 [TN2]). Carbamazepine insensitivity was moderately associated with TN2 and predicted long-term pain recurrence. Patients with both phenotypes had significantly worse initial outcomes compared with other subgroups (adjusted p = 0.0125).

CONCLUSIONS: Patients with both TN2 and carbamazepine insensitivity have the poorest initial treatment outcomes and face an increased risk of recurrence. Furthermore, this predictive model is highly accurate and useful, offering a comprehensive method of identifying PTN patients likely to experience poor initial outcomes based on clinical characteristics and imaging perspectives. The authors believe that the nomogram presented in this model enables clinicians to calculate multiple variables and predict the probability of adverse events.},
}

@article {pmid40446280,
year = {2025},
author = {Liang, X and Ding, Y and Yuan, Z and Han, Y and Zhou, Y and Jiang, J and Xie, Z and Fei, P and Sun, Y and Jia, P and Gu, G and Zhong, Z and Chen, F and Si, G and Gong, Z},
title = {Mechanics of Soft-Body Rolling Motion without External Torque.},
journal = {Physical review letters},
volume = {134},
number = {19},
pages = {198401},
doi = {10.1103/PhysRevLett.134.198401},
pmid = {40446280},
issn = {1079-7114},
mesh = {Animals ; Robotics ; Larva/physiology ; *Models, Biological ; Biomechanical Phenomena ; *Drosophila/physiology ; Muscle Contraction/physiology ; Torque ; },
abstract = {The Drosophila larva, a soft-body animal, can bend its body and roll efficiently to escape danger. However, contrary to common belief, this rolling motion is not driven by the imbalance of gravity and ground reaction forces. Through functional imaging and ablation experiments, we demonstrate that the sequential actuation of axial muscles within an appropriate range of angles is critical for generating rolling. We model the interplay between muscle contraction, hydrostatic skeleton deformation, and body-environment interactions, and systematically explain how sequential muscle actuation generates the rolling motion. Additionally, we construct a pneumatic soft robot to mimic the larval rolling strategy, successfully validating our model. This mechanics model of soft-body rolling motion not only advances the study of related neural circuits, but also holds potential for applications in soft robotics.},
}

Animals
Robotics
Larva/physiology
*Models, Biological
Biomechanical Phenomena
*Drosophila/physiology
Muscle Contraction/physiology
Torque

@article {pmid40443843,
year = {2025},
author = {Yang, H and Li, T and Zhao, L and Wei, Y and Chen, X and Pan, J and Fu, Y},
title = {Guiding principles and considerations for designing a well-structured curriculum for the brain-computer interface major based on the multidisciplinary nature of brain-computer interface.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1554266},
pmid = {40443843},
issn = {1662-5161},
abstract = {Brain-computer interface (BCI) is a novel human-computer interaction technology, and its rapid development has led to a growing demand for skilled BCI professionals, culminating in the emergence of the BCI major. Despite its significance, there is limited literature addressing the curriculum design for this emerging major. This paper seeks to bridge this gap by proposing and discussing a curricular framework for the BCI major, based on the inherently multidisciplinary nature of BCI research and development. The paper begins by elucidating the primary factors behind the emergence of the BCI major, the increasing demand for both medical and non-medical applications of BCI, and the corresponding need for specialized talent. It then delves into the multidisciplinary nature of BCI research and offers principles for curriculum design to address this nature. Based on these principles, the paper provides detailed suggestions for structuring a BCI curriculum. Finally, it discusses the challenges confronting the development of the BCI major, including the lack of consensus and international collaboration in the construction of the BCI major, as well as the inadequacy or lack of teaching materials. Future work needs to improve the curriculum design of the BCI major from a competency-oriented perspective. It is expected that this paper will provide a reference for the curriculum design and construction of the BCI major.},
}

@article {pmid40442937,
year = {2025},
author = {Wang, F and Wang, L and Zhu, X and Lu, Y and Du, X},
title = {Neuron-Inspired Ferroelectric Bioelectronics for Adaptive Biointerfacing.},
journal = {Advanced materials (Deerfield Beach, Fla.)},
volume = {},
number = {},
pages = {e2416698},
doi = {10.1002/adma.202416698},
pmid = {40442937},
issn = {1521-4095},
support = {B2302045//Shenzhen Medical Research Fund/ ; 52022102//National Natural Science Foundation of China/ ; 52261160380//National Natural Science Foundation of China/ ; 32471042//National Natural Science Foundation of China/ ; 32300845//National Natural Science Foundation of China/ ; 2017YFA0701303//National Key R&D Program of China/ ; Y2023100//Youth Innovation Promotion Association of CAS/ ; RCJC20221008092729033//Fundamental Research Program of Shenzhen/ ; JCYJ20220818101800001//Fundamental Research Program of Shenzhen/ ; 2024A1515010645//Basic and Applied Basic Research Foundation of Guangdong Province/ ; },
abstract = {Implantable bioelectronics, which are essential to neuroscience studies, neurological disorder treatment, and brain-machine interfaces, have become indispensable communication bridges between biological systems and the external world through sensing, monitoring, or manipulating bioelectrical signals. However, conventional implantable bioelectronic devices face key challenges in adaptive interfacing with neural tissues due to their lack of neuron-preferred properties and neuron-similar behaviors. Here, innovative neuron-inspired ferroelectric bioelectronics (FerroE) are reported that consists of biocompatible polydopamine-modified barium titanate nanoparticles, ferroelectric poly(vinylidene fluoride-co-trifluoroethylene) copolymer, and cellular-scale micropyramid array structures, imparting adaptive interfacing with neural systems. These FerroE not only achieve neuron-preferred flexible and topographical properties, but also offer neuron-similar behaviors including highly efficient and stable light-induced polarization change, superior capability of producing electric signals, and seamless integration and adaptive communication with neurons. Moreover, the FerroE allows for adaptive interfacing with both peripheral and central neural networks of mice, enabling regulation of their heart rate and motion behavior in a wireless, non-genetic, and non-contact manner. Notably, the FerroE demonstrates unprecedented structural and functional stability and negligible immune response even after 3 months of implantation in vivo. Such bioinspired FerroE are opening new opportunities for next-generation brain-machine interfaces, tissue engineering materials, and biomedical devices.},
}

@article {pmid40442546,
year = {2025},
author = {Wang, L and Li, T and Li, X and Liu, F and Feng, C},
title = {Mapping trait justice sensitivity in the Brain: Whole-brain resting-state functional connectivity as a predictor of other-oriented not self-oriented justice sensitivity.},
journal = {Cognitive, affective & behavioral neuroscience},
volume = {},
number = {},
pages = {},
pmid = {40442546},
issn = {1531-135X},
support = {2024B0303390003//Research Center for Brain Cognition and Human Development, Guangdong, China/ ; 32020103008//National Natural Science Foundation of China/ ; 32271126//National Natural Science Foundation of China/ ; 81922036//National Natural Science Foundation of China/ ; },
abstract = {Justice sensitivity (JS) reflects personal concern and commitment to the principle of justice, showing considerable heterogeneity among the general population. Despite a growing interest in the behavioral characteristics of JS over the past decades, the neurobiological substrates underlying trait JS are not well comprehended. We addressed this issue by employing a machine learning approach to decode the trait JS, encompassing its various orientations, from whole-brain resting-state functional connectivity. We demonstrated that the machine-learning model could decode the individual trait of other-oriented JS but not self-oriented JS from resting-state functional connectivity across multiple neural systems, including functional connectivity between and within parietal lobe and motor cortex as well as their connectivity with other brain systems. Key nodes that contributed to the prediction model included the parietal, motor, temporal, and subcortical regions that have been linked to other-oriented JS. Additionally, the machine learning model can distinctly distinguish between the distinct roles associated with other-oriented JS, including observer, perpetrator, and beneficiary, with key brain regions in the predictive networks exhibiting both similarities and disparities. These findings remained robust using different validation procedures. Collectively, these results support the separation between other-oriented JS and self-oriented JS, while also highlighting the distinct intrinsic neural correlates among the three roles of other-oriented JS: observer, perpetrator, and beneficiary.},
}

@article {pmid40442206,
year = {2025},
author = {Akama, T and Zhang, Z and Li, P and Hongo, K and Minamikawa, S and Polouliakh, N},
title = {Predicting artificial neural network representations to learn recognition model for music identification from brain recordings.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {18869},
pmid = {40442206},
issn = {2045-2322},
mesh = {*Music ; Humans ; *Neural Networks, Computer ; Electroencephalography ; *Brain/physiology ; Male ; *Auditory Perception/physiology ; Female ; Adult ; Acoustic Stimulation ; Young Adult ; Brain-Computer Interfaces ; },
abstract = {Recent studies have demonstrated that the representations of artificial neural networks (ANNs) can exhibit notable similarities to cortical representations when subjected to identical auditory sensory inputs. In these studies, the ability to predict cortical representations is probed by regressing from ANN representations to cortical representations. Building upon this concept, our approach reverses the direction of prediction: we utilize ANN representations as a supervisory signal to train recognition models using noisy brain recordings obtained through non-invasive measurements. Specifically, we focus on constructing a recognition model for music identification, where electroencephalography (EEG) brain recordings collected during music listening serve as input. By training an EEG recognition model to predict ANN representations-representations associated with music identification-we observed a significant improvement in classification accuracy. This study introduces a novel approach to developing recognition models for brain recordings in response to external auditory stimuli. It holds promise for advancing brain-computer interfaces (BCI), neural decoding techniques, and our understanding of music cognition. Furthermore, it provides new insights into the relationship between auditory brain activity and ANN representations.},
}

*Music
Humans
*Neural Networks, Computer
Electroencephalography
*Brain/physiology
Male
*Auditory Perception/physiology
Female
Adult
Acoustic Stimulation
Young Adult
Brain-Computer Interfaces

@article {pmid40442062,
year = {2025},
author = {Shah, NP and Avansino, D and Kamdar, F and Nicolas, C and Kapitonava, A and Vargas-Irwin, C and Hochberg, LR and Pandarinath, C and Shenoy, KV and Willett, FR and Henderson, JM},
title = {Pseudo-linear summation explains neural geometry of multi-finger movements in human premotor cortex.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {5008},
pmid = {40442062},
issn = {2041-1723},
support = {Milton Safenowtiz Postdoctoral Scholarship//Amyotrophic Lateral Sclerosis Association (ALS Association)/ ; },
mesh = {Humans ; *Fingers/physiology ; *Motor Cortex/physiology/physiopathology ; Male ; Movement/physiology ; Adult ; Quadriplegia/physiopathology ; },
abstract = {How does the motor cortex combine simple movements (such as single finger flexion/extension) into complex movements (such as hand gestures, or playing the piano)? To address this question, motor cortical activity was recorded using intracortical multi-electrode arrays in two male people with tetraplegia as they attempted single, pairwise and higher-order finger movements. Neural activity for simultaneous movements was largely aligned with linear summation of corresponding single finger movement activities, with two violations. First, the neural activity exhibited normalization, preventing a large magnitude with an increasing number of moving fingers. Second, the neural tuning direction of weakly represented fingers changed significantly as a result of the movement of more strongly represented fingers. These deviations from linearity resulted in non-linear methods outperforming linear methods for neural decoding. Simultaneous finger movements are thus represented by the combination of individual finger movements by pseudo-linear summation.},
}

Humans
*Fingers/physiology
*Motor Cortex/physiology/physiopathology
Male
Movement/physiology
Adult
Quadriplegia/physiopathology

@article {pmid40441574,
year = {2025},
author = {Rahman, MH and Mondal, MIH},
title = {Investigation of neem-oil-loaded PVA/chitosan biocomposite film for hydrophobic dressing, rapid hemostasis and wound healing applications.},
journal = {International journal of biological macromolecules},
volume = {},
number = {},
pages = {144712},
doi = {10.1016/j.ijbiomac.2025.144712},
pmid = {40441574},
issn = {1879-0003},
abstract = {The present work aims to develop a hydrophobic dressing with a blood-repellent surface that achieves fast clotting without blood loss, having antibacterial properties, clot self-detachment, and superior wound healing activity. For these reasons, a novel approach was applied by producing a hydrophobic film made of PVA, chitosan, and neem seed oil (NSO). The film had the necessary hydrophobicity, mechanical strength, stability and was able to transmit water vapor to be suitable for the wound skin surface and demonstrated faster blood clotting (BCI = 91.44 % in 5 min and 85.22 % in 10 min). The proportion of red blood cells (2.78 %) and platelets (17.33 %) attached to the film proved its excellent hemostatic activity. It was anti-adhesive, created spontaneous clot detachment, and exhibited antibacterial properties at the wound site, as evidenced by in vivo testing. Moreover, in vivo testing and histopathological findings showed enhanced wound healing activity, greater re-epithelialization, and decreased granulation tissue. Additionally, the film's eco-friendliness was evaluated using a soil burial degradation test, and the results show that it deteriorated into the soil but did so slowly because of its hydrophobic property. Thus, PVA/CS/NSO composite film may be a green biomedical material for hemostasis and wound healing.},
}

@article {pmid40440260,
year = {2025},
author = {Tong, B and Li, G and Bu, X and Wang, Y and Yu, X},
title = {A deep learning-based algorithm for the detection of personal protective equipment.},
journal = {PloS one},
volume = {20},
number = {5},
pages = {e0322115},
pmid = {40440260},
issn = {1932-6203},
mesh = {*Personal Protective Equipment ; *Deep Learning ; Humans ; *Algorithms ; Neural Networks, Computer ; Construction Industry ; },
abstract = {Personal protective equipment (PPE) is critical for ensuring the safety of construction workers. However, site surveillance images from construction sites often feature multi-size and multi-scale targets, leading to low detection accuracy for PPE in existing models. To address this issue, this paper proposes an improved model based on YOLOv8n.By enriching feature diversity and enhancing the model's adaptability to geometric transformations, the detection accuracy is improved.A Multi-Scale Group Convolution Module (MSGP) was designed to extract multi-level features using different convolution kernels. A Multi-Scale Feature Diffusion Pyramid Network (MFDPN) was developed, which aggregates multi-scale features through the Multiscale Feature Focus (MFF) module and diffuses them across scales, providing each scale with detailed contextual information. A customized Task Alignment Module was introduced to integrate interactive features, optimizing both classification and localization tasks. The DCNV2(Deformable Convolutional Networks v2) module was incorporated to handle geometric scale transformations by generating spatial offsets and feature masks from interactive features, thereby improving localization accuracy and dynamically selecting weights to enhance classification precision.The improved model incorporates rich multi-level and multi-scale features, allowing it to better adapt to tasks involving geometric transformations and aligning with the image data distribution in construction scenarios. Additionally, structured pruning techniques were applied to the model at varying levels, further reducing computational and parameter loads. Experimental results indicate that at a pruning level of 1.5, mAP@0.5 and mAP@0.5:0.95 improved by 3.9% and 4.6%, respectively, while computational load decreased by 21% and parameter count dropped by 53%. The proposed MFD-YOLO(1.5) model achieves significant progress in detecting personal protective equipment on construction sites, with a substantial reduction in parameter count, making it suitable for deployment on resource-constrained edge devices.},
}

*Personal Protective Equipment
*Deep Learning
Humans
*Algorithms
Neural Networks, Computer
Construction Industry

@article {pmid40438784,
year = {2025},
author = {Du, Y and Yang, X and Wang, M and Lv, Q and Zhou, H and Sang, G},
title = {Longitudinal changes in children with autism spectrum disorder receiving applied behavior analysis or early start denver model interventions over six months.},
journal = {Frontiers in pediatrics},
volume = {13},
number = {},
pages = {1546001},
pmid = {40438784},
issn = {2296-2360},
abstract = {BACKGROUND: Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder characterized by social communication difficulties, restricted interests, repetitive behaviors, and sensory abnormalities. The rising prevalence of ASD presents a significant public health concern, with no pharmacological treatments available for its core symptoms. Therefore, early and effective behavioral interventions are crucial to improving developmental outcomes for children with ASD. Current interventions primarily focus on educational rehabilitation methods, including Applied behavior Analysis (ABA) and the Early Start Denver Model (ESDM).

OBJECTIVE: This study aims to examine the developmental changes in children with ASD following six months of ABA therapy or ESDM intervention.

METHODS: From December 2021 to December 2023, 30 children receiving ABA therapy at the Zhejiang Rehabilitation Medical Center (40 min/session, 4 sessions/day, 5 days/week), while another 30 children undergoing ESDM training at Hangzhou Children's Hospital (2 h of one-on-one sessions and 0.5 h of group sessions/day, 5 days/week). Both groups participated in their respective interventions for six months. Pre- and post-treatment assessments were conducted using the Psycho-educational Profile-Third Edition (PEP-3).

RESULTS: Both groups showed significant improvements in PEP-3 scores post-treatment, including cognitive verbal/pre-verbal, expressive language, receptive language, social reciprocity, small muscles, imitation, emotional expression, and verbal and nonverbal behavioral characteristics.

CONCLUSION: Both ABA and ESDM interventions were associated with comprehensive improvements in children with ASD over a six-month period.},
}

@article {pmid40438090,
year = {2025},
author = {Ding, W and Liu, A and Chen, X and Xie, C and Wang, K and Chen, X},
title = {Reducing calibration efforts of SSVEP-BCIs by shallow fine-tuning-based transfer learning.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {81},
pmid = {40438090},
issn = {1871-4080},
abstract = {The utilization of transfer learning (TL), particularly through pre-training and fine-tuning, in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) has substantially reduced the calibration efforts. However, commonly employed fine-tuning approaches, including end-to-end fine-tuning and last-layer fine-tuning, require data from target subjects that encompass all categories (stimuli), resulting in a time-consuming data collection process, especially in systems with numerous categories. To address this challenge, this study introduces a straightforward yet effective ShallOw Fine-Tuning (SOFT) method to substantially reduce the number of calibration categories needed for model fine-tuning, thereby further mitigating the calibration efforts for target subjects. Specifically, SOFT involves freezing the parameters of the deeper layers while updating those of the shallow layers during fine-tuning. Freezing the parameters of deeper layers preserves the model's ability to recognize semantic and high-level features across all categories, as established during pre-training. Moreover, data from different categories exhibit similar individual-specific low-level features in SSVEP-BCIs. Consequently, updating the parameters of shallow layers-responsible for processing low-level features-with data solely from partial categories enables the fine-tuned model to efficiently capture the individual-related features shared by all categories. The effectiveness of SOFT is validated using two public datasets. Comparative analysis with commonly used end-to-end and last-layer fine-tuning methods reveals that SOFT achieves higher classification accuracy while requiring fewer calibration categories. The proposed SOFT method further decreases the calibration efforts for target subjects by reducing the calibration category requirements, thereby improving the feasibility of SSVEP-BCIs for real-world applications.},
}

@article {pmid40437332,
year = {2025},
author = {Cherukuri, SB and Ramakrishnan, S},
title = {Eye-blink artifact removal in single-channel electroencephalogram using K-means and Savitzky Golay-singular Spectrum Analysis hybrid technique.},
journal = {Physical and engineering sciences in medicine},
volume = {},
number = {},
pages = {},
pmid = {40437332},
issn = {2662-4737},
abstract = {Electroencephalogram (EEG) acquisition systems are used to record the neural condition of humans for diagnosing various neural problems. The eye-blink or Electrooculogram (EOG) artifact caused by eye-lid movements, influences the EEG signal measurements and interferes with the diagnosis. The complete removal of eye-blink artifact while preserving the EEG content is a challenging task that needs highly efficient denoising methods, particularly from Single-Channel EEG which is widely used for Out-Of-Hospital (OOH) neurological patients and for Brain-Computer-Interface (BCI) applications. When compared to multi-channel EEG systems, Single-channel EEG system suffers certain difficulties such as lack of spatial information, redundancy, etc. This paper proposes an innovative hybrid method combining K-Means clustering and Savitzky Golay-Singular Spectrum Analysis (SG-SSA) methods for effective eye-blink artifact removal from single channel EEG. The eye-blink artifact is extracted and then subtracted from the noisy EEG signal, so that the EEG content available in the eye-blink periods are preserved. Through extensive experiments with synthetic as well as real time EEG, we show that our proposed method outperforms the other contemporary methods from literature. Our proposed hybrid approach achieves a significant reduction in Mean Absolute Error (MAE) and Relative Root Mean Square Error (RRMSE) than the Fourier-Bessel Series Expansion based Empirical Wavelet Transform (FBSE-EWT), SSA combined with independent component analysis (SSA-ICA) and Ensemble Empirical Mode Decomposition combined with ICA (EEMD-ICA), proposed in recent literature.},
}

@article {pmid40436265,
year = {2025},
author = {Chopra, M and Kumar, H},
title = {Navigating the complexities of spinal cord injury: an overview of pathology, treatment strategies and clinical trials.},
journal = {Drug discovery today},
volume = {},
number = {},
pages = {104387},
doi = {10.1016/j.drudis.2025.104387},
pmid = {40436265},
issn = {1878-5832},
abstract = {Spinal cord injury (SCI) is a debilitating neurological condition characterized by sensory and motor deficits. It significantly affects patient quality of life and poses a substantial socioeconomic burden. The complex and multifaceted pathophysiology of SCI complicates its effective treatment. Following the primary mechanical insult, a secondary injury cascade disrupts the microenvironment at the injury site, exacerbating the tissue damage. Despite extensive research, no fully effective treatment is currently available. This review explores current pharmacological and non-pharmacological treatment strategies at the preclinical and clinical stages, providing insights into promising interventions. The findings highlight potential avenues for future research aimed at improving SCI management.},
}

@article {pmid40434889,
year = {2025},
author = {Ruszala, BM and Schieber, MH},
title = {Injecting information in the cortical reach-to-grasp network is effective in ventral but not dorsal nodes.},
journal = {Cell reports},
volume = {44},
number = {5},
pages = {115664},
doi = {10.1016/j.celrep.2025.115664},
pmid = {40434889},
issn = {2211-1247},
abstract = {Although control of movement involves many cortical association areas, bidirectional brain-machine interfaces (BMIs) typically decode movement intent from the motor cortex and deliver feedback information to the primary somatosensory cortex (S1). Compared to the S1, the parietal and premotor areas encode more complex information about object properties, hand pre-shaping, and reach trajectories. BMIs therefore might deliver richer information to those cortical association areas than to primary areas. Here, we investigated whether instructions for a center-out task could be delivered via intracortical microstimulation (ICMS) in the anterior intraparietal area (AIP), dorsal posterior parietal cortex (dPPC), or dorsal premotor cortex (PMd) as well as the ventral premotor cortex (PMv) and S1. Two monkeys successfully learned to use AIP, PMv, or S1-ICMS instructions, but neither learned to use dPPC- or PMd-ICMS instructions. The AIP, PMv, and S1 may thus be effective cortical territory for delivering information to the brain, whereas the dPPC or PMd may be comparatively ineffective.},
}

@article {pmid40434816,
year = {2025},
author = {Liu, L},
title = {Did you see it?.},
journal = {eLife},
volume = {14},
number = {},
pages = {},
pmid = {40434816},
issn = {2050-084X},
mesh = {Humans ; *Brain/physiology ; *Consciousness/physiology ; },
abstract = {Cautious reporting choices can artificially enhance how well analyses of brain activity reflect conscious and unconscious experiences, making distinguishing between the two more challenging.},
}

Humans
*Brain/physiology
*Consciousness/physiology

@article {pmid40434551,
year = {2025},
author = {Sokhadze, E},
title = {Neurofeedback and Brain-Computer Interface-Based Methods for Post-stroke Rehabilitation.},
journal = {Applied psychophysiology and biofeedback},
volume = {},
number = {},
pages = {},
pmid = {40434551},
issn = {1573-3270},
abstract = {Stroke has been identified as a major public health concern and one of the leading causes contributing to long-term neurological disability. People suffering from stroke often present with upper limb paralysis impacting their quality of life and ability to work. Motor impairments in the upper limb represent the most prevalent symptoms in stroke sufferers. There is a need to develop novel intervention strategies that can be used as stand-alone techniques or combined with current gold standard post-stroke rehabilitation procedures. There was reported evidence about the utility of rehabilitation protocols with motor imagery (MI) used either alone or in combination with physical therapy resulting in enhancement of post-stroke functional recovery of paralyzed limbs. Brain-Computer Interface (BCI) and EEG neurofeedback (NFB) training can be considered as novel technologies to be used in conjunction with MI and motor attempt (MA) to enable direct translation of EEG induced by imagery or attempted movement to arrange training that has potential to enhance functional motor recovery of upper limbs after stroke. There are reported several controlled trials and multiple cases series that have shown that stroke patients are able to learn modulation of their EEG sensorimotor rhythm in BCI mode to control external devices, including exoskeletons, prosthetics, and such interventions were shown promise in facilitation of recovery in stroke sufferers. A review of the literature suggests there has been significant progress in the development of new methods for post-stroke rehabilitation procedures. There are reviewed findings supportive of NFB and BCI methods as evidence-based treatment for post-stroke motor function recovery.},
}

@article {pmid40433677,
year = {2025},
author = {He, J and Zhou, G and Sun, B and Yan, L and Lang, X and Yang, Y and Hao, H},
title = {Graphene quantum dots induced performance enhancement in memristors.},
journal = {Nanoscale},
volume = {},
number = {},
pages = {},
doi = {10.1039/d5nr00597c},
pmid = {40433677},
issn = {2040-3372},
abstract = {With the rapid development of information technology, the demand for miniaturization, integration, and intelligence of electronic devices is growing rapidly. As a key device in the non-von Neumann architecture, memristors can perform computations while storing data, enhancing computational efficiency and reducing power consumption. Memristors have become pivotal in driving the advancement of artificial intelligence (AI) and Internet of Things technologies. Combining the electronic properties of graphene with the size effects of quantum dots, graphene quantum dot (GQD)-based memristors exhibit potential applications in constructing brain-inspired neuromorphic computing systems and achieving AI hardware acceleration, making them a focal point of research interest. This review provides an overview of the preparation, mechanism, and application of GQD-based memristors. Initially, the structure, properties, and synthesis methods of GQDs are introduced in detail. Subsequently, the memristive mechanisms of GQD-based memristors are presented from three perspectives: the metal conductive filament mechanism, the electron trapping and detrapping mechanism, and the oxygen vacancy conductive filament mechanism. Furthermore, the different application scenarios of GQD-based memristors in both digital and analog types are summarized, encompassing information storage, brain-like artificial synapses, visual perception systems, and brain-machine interfaces. Finally, the challenges and future development prospects of GQD-based memristors are discussed.},
}

@article {pmid40431969,
year = {2025},
author = {You, Z and Guo, Y and Zhang, X and Zhao, Y},
title = {Virtual Electroencephalogram Acquisition: A Review on Electroencephalogram Generative Methods.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {10},
pages = {},
doi = {10.3390/s25103178},
pmid = {40431969},
issn = {1424-8220},
mesh = {*Electroencephalography/methods ; Humans ; *Brain-Computer Interfaces ; Algorithms ; Machine Learning ; Signal Processing, Computer-Assisted ; Neural Networks, Computer ; Brain/physiology ; },
abstract = {Driven by the remarkable capabilities of machine learning, brain-computer interfaces (BCIs) are carving out an ever-expanding range of applications across a multitude of diverse fields. Notably, electroencephalogram (EEG) signals have risen to prominence as the most prevalently utilized signals within BCIs, owing to their non-invasive essence, exceptional portability, cost-effectiveness, and high temporal resolution. However, despite the significant strides made, the paucity of EEG data has emerged as the main bottleneck, preventing generalization of decoding algorithms. Taking inspiration from the resounding success of generative models in computer vision and natural language processing arenas, the generation of synthetic EEG data from limited recorded samples has recently garnered burgeoning attention. This paper undertakes a comprehensive and thorough review of the techniques and methodologies underpinning the generative models of the general EEG, namely the variational autoencoder (VAE), the generative adversarial network (GAN), and the diffusion model. Special emphasis is placed on their practical utility in augmenting EEG data. The structural designs and performance metrics of the different generative approaches in various application domains have been meticulously dissected and discussed. A comparative analysis of the strengths and weaknesses of each existing model has been carried out, and prospective avenues for future enhancement and refinement have been put forward.},
}

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

@article {pmid40431893,
year = {2025},
author = {Sasatake, Y and Matsushita, K},
title = {EEG Baseline Analysis for Effective Extraction of P300 Event-Related Potentials for Welfare Interfaces.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {10},
pages = {},
doi = {10.3390/s25103102},
pmid = {40431893},
issn = {1424-8220},
support = {JPMJSP2125//JST SPRING/ ; Not Applicable//THERS Make New Standards Program for the Next Generation Researchers/ ; },
mesh = {Humans ; *Event-Related Potentials, P300/physiology ; *Electroencephalography/methods ; Male ; Adult ; Female ; *Brain-Computer Interfaces ; Young Adult ; Photic Stimulation ; Signal Processing, Computer-Assisted ; },
abstract = {Enabling individuals with complete paralysis to operate devices voluntarily requires an effective interface; EEG-based P300 event-related potential (ERP) interfaces are considered a promising approach. P300 is an EEG peak generated in response to specific sensory stimuli recognized by an individual. Accurate detection of this peak necessitates a stable pre-stimulus baseline EEG signal, which serves as the reference for baseline correction. Previous studies have commonly employed either a single-time-point amplitude (e.g., at 100 ms before stimulus onset) or a time-range-averaged amplitude over a specified pre-stimulus period (e.g., 0-200 ms) as a baseline correction method, assuming these provide the most stable EEG reference. However, in assistive P300 interfaces, continuous visual stimuli at 400 ms intervals are typically used to efficiently evoke P300 peaks. Since stimuli are presented before the EEG stabilizes, it remains unclear whether conventional neuroscience baseline correction methods are suitable for such applications. To address this, the present study conducted a P300 induction experiment based on continuous 400 ms interval visual stimuli. Using EEG data recorded from 0 to 1000 ms before each visual stimulus (sampled at 1 ms intervals), we applied three baseline correction methods-single-time-point amplitude, time-range-averaged amplitude, and multi-time-point amplitude-to determine the most effective EEG reference and evaluate the impact on P300 detection performance. The results showed that baseline correction using an amplitude at a single point in time is unstable when the basic EEG rhythm and low-frequency noise remain, while time-range-averaged baseline correction using the 0-200 ms pre-stimulus period led to relatively effective P300 detection. However, it was also found that using only one value averaged over the amplitude from 0 to 200 ms did not result in an accurate EEG reference potential, resulting in an error. Finally, this study confirmed that the multi-time-point baseline correction method, through which the amplitude state from 0 to 200 ms before the visual stimulus is comprehensively evaluated, may be the most effective method for P300 determination.},
}

Humans
*Event-Related Potentials, P300/physiology
*Electroencephalography/methods
Male
Adult
Female
*Brain-Computer Interfaces
Young Adult
Photic Stimulation
Signal Processing, Computer-Assisted

@article {pmid40431780,
year = {2025},
author = {Polo-Hortigüela, C and Ortiz, M and Soriano-Segura, P and Iáñez, E and Azorín, JM},
title = {Time-Frequency Analysis of Motor Imagery During Plantar and Dorsal Flexion Movements Using a Low-Cost Ankle Exoskeleton.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {10},
pages = {},
doi = {10.3390/s25102987},
pmid = {40431780},
issn = {1424-8220},
support = {PID2021-124111OB-C31//MICIU /AEI/10.13039/501100011033 and by ERDF, EU/ ; PRE2022-103336//MICIU/AEI/10.13039 501100011033/ ; //Valencian Graduate School and Research Network of Artificial Intelligence (ValgrAI), Generalitat Valenciana and European Union/ ; //Project "Neurokit" funded by Centro Internacional para la Investigación del Envejecimiento de la Fundación de la Comunitat Valenciana (ICAR)/ ; 101118964//European Union's research and innovation programme under the Marie Skłodowska-Curie/ ; },
mesh = {Humans ; Electroencephalography/methods ; Brain-Computer Interfaces ; Movement/physiology ; Male ; *Ankle/physiology ; *Exoskeleton Device ; Adult ; Biomechanical Phenomena ; Foot/physiology ; Female ; Wearable Electronic Devices ; Fourier Analysis ; },
abstract = {Sensor technology plays a fundamental role in neuro-motor rehabilitation by enabling precise movement analysis and control. This study explores the integration of brain-machine interfaces (BMIs) and wearable sensors to enhance motor recovery in individuals with neuro-motor impairments. Specifically, different time-frequency transforms are evaluated to analyze the correlation between electroencephalographic (EEG) activity and ankle position, measured by using inertial measurement units (IMUs). A low-cost ankle exoskeleton was designed to conduct the experimental trials. Six subjects performed plantar and dorsal flexion movements while the EEG and IMU signals were recorded. The correlation between brain activity and foot kinematics was analyzed using the Short-Time Fourier Transform (STFT), Stockwell (ST), Hilbert-Huang (HHT), and Chirplet (CT) methods. The 8-20 Hz frequency band exhibited the highest correlation values. For motor imagery classification, the STFT achieved the highest accuracy (92.9%) using an EEGNet-based classifier and a state-machine approach. This study presents a dual approach: the analysis of EEG-movement correlation in different cognitive states, and the systematic comparison of four time-frequency transforms for both correlation and classification performance. The results support the potential of combining EEG and IMU data for BMI applications and highlight the importance of cognitive state in motion analysis for accessible neurorehabilitation technologies.},
}

Humans
Electroencephalography/methods
Brain-Computer Interfaces
Movement/physiology
Male
*Ankle/physiology
*Exoskeleton Device
Adult
Biomechanical Phenomena
Foot/physiology
Female
Wearable Electronic Devices
Fourier Analysis

@article {pmid40428890,
year = {2025},
author = {Endzelytė, E and Petruševičienė, D and Kubilius, R and Mingaila, S and Rapolienė, J and Rimdeikienė, I},
title = {Integrating Brain-Computer Interface Systems into Occupational Therapy for Enhanced Independence of Stroke Patients: An Observational Study.},
journal = {Medicina (Kaunas, Lithuania)},
volume = {61},
number = {5},
pages = {},
doi = {10.3390/medicina61050932},
pmid = {40428890},
issn = {1648-9144},
mesh = {Humans ; Male ; Female ; *Brain-Computer Interfaces/standards/trends ; *Occupational Therapy/methods/standards ; *Stroke Rehabilitation/methods/standards ; Middle Aged ; Aged ; Activities of Daily Living/psychology ; Upper Extremity/physiopathology ; Adult ; Stroke/complications ; },
abstract = {Background and Objectives: Brain-computer interface (BCI) technology is revolutionizing stroke rehabilitation by offering innovative neuroengineering solutions to address neurological deficits. By bypassing peripheral nerves and muscles, BCIs enable individuals with severe motor impairments to communicate their intentions directly through control signals derived from brain activity, opening new pathways for recovery and improving the quality of life. The aim of this study was to explore the beneficial effects of BCI system-based interventions on upper limb motor function and performance of activities of daily living (ADL) in stroke patients. We hypothesized that integrating BCI into occupational therapy would result in measurable improvements in hand strength, dexterity, independence in daily activities, and cognitive function compared to baseline. Materials and Methods: An observational study was conducted on 56 patients with subacute stroke. All patients received standard medical care and rehabilitation for 54 days, as part of the comprehensive treatment protocol. Patients underwent BCI training 2-3 times a week instead of some occupational therapy sessions, with each patient completing 15 sessions of BCI-based recoveriX treatment during rehabilitation. The occupational therapy program included bilateral exercises, grip-strengthening activities, fine motor/coordination tasks, tactile discrimination exercises, proprioceptive training, and mirror therapy to enhance motor recovery through visual feedback. Participants received ADL-related training aimed at improving their functional independence in everyday activities. Routine occupational therapy was provided five times a week for 50 min per session. Upper extremity function was evaluated using the Box and Block Test (BBT), Nine-Hole Peg Test (9HPT), and dynamometry to assess gross manual dexterity, fine motor skills, and grip strength. Independence in daily living was assessed using the Functional Independence Measure (FIM). Results: Statistically significant improvements were observed across all the outcome measures (p < 0.001). The strength of the stroke-affected hand improved from 5.0 kg to 6.7 kg, and that of the unaffected hand improved from 29.7 kg to 40.0 kg. Functional independence increased notably, with the FIM scores rising from 43.0 to 83.5. Cognitive function also improved, with MMSE scores increasing from 22.0 to 26.0. The effect sizes ranged from moderate to large, indicating clinically meaningful benefits. Conclusions: This study suggests that BCI-based occupational therapy interventions effectively improve upper extremity motor function and daily functions and have a positive impact on the cognition of patients with subacute stroke.},
}

Humans
Male
Female
*Brain-Computer Interfaces/standards/trends
*Occupational Therapy/methods/standards
*Stroke Rehabilitation/methods/standards
Middle Aged
Aged
Activities of Daily Living/psychology
Upper Extremity/physiopathology
Adult
Stroke/complications

@article {pmid40428702,
year = {2025},
author = {Ma, X and Miao, T and Xie, F and Zhang, J and Zheng, L and Liu, X and Hai, H},
title = {Development of Wearable Wireless Multichannel f-NIRS System to Evaluate Activities.},
journal = {Micromachines},
volume = {16},
number = {5},
pages = {},
doi = {10.3390/mi16050576},
pmid = {40428702},
issn = {2072-666X},
abstract = {Functional near-infrared spectroscopy is a noninvasive neuroimaging technique that uses optical signals to monitor subtle changes in hemoglobin concentrations within the superficial tissue of the human body. This technology has widespread applications in long-term brain-computer interface monitoring within both traditional medical domains and, increasingly, domestic settings. The popularity of this approach lies in the fact that new single-channel brain oxygen sensors can be used in a variety of scenarios. Given the diverse sensor structure requirements across applications and numerous approaches to data acquisition, the accurate extraction of comprehensive brain activity information requires a multichannel near-infrared system. This study proposes a novel distributed multichannel near-infrared system that integrates two near-infrared light emissions at differing wavelengths (660 nm, 850 nm) with a photoelectric receiver. This substantially improves the accuracy of regional signal sampling. Through a basic long-time mental arithmetic paradigm, we demonstrate that the accompanying algorithm supports offline analysis and is sufficiently versatile for diverse scenarios relevant to the system's functionality.},
}

@article {pmid40428683,
year = {2025},
author = {Hong, S},
title = {Wireless Optogenetic Microsystems Accelerate Artificial Intelligence-Neuroscience Coevolution Through Embedded Closed-Loop System.},
journal = {Micromachines},
volume = {16},
number = {5},
pages = {},
doi = {10.3390/mi16050557},
pmid = {40428683},
issn = {2072-666X},
support = {N/A//Hongik University/ ; },
abstract = {Brain-inspired models in artificial intelligence (AI) originated from foundational insights in neuroscience. In recent years, this relationship has been moving toward a mutually reinforcing feedback loop. Currently, AI is significantly contributing to advancing our understanding of neuroscience. In particular, when combined with wireless optogenetics, AI enables experiments without physical constraints. Furthermore, AI-driven real-time analysis facilitates closed-loop control, allowing experimental setups across a diverse range of scenarios. And a deeper understanding of these neural networks may, in turn, contribute to future advances in AI. This work demonstrates the synergy between AI and miniaturized neural technology, particularly through wireless optogenetic systems designed for closed-loop neural control. We highlight how AI is now revolutionizing neuroscience experiments from decoding complex neural signals and quantifying behavior, to enabling closed-loop interventions and high-throughput phenotyping in freely moving subjects. Notably, AI-integrated wireless implants can monitor and modulate biological processes with unprecedented precision. We then recount how neuroscience insights derived from AI-integrated neuroscience experiments can potentially inspire the next generation of machine intelligence. Insights gained from these technologies loop back to inspire more efficient and robust AI systems. We discuss future directions in this positive feedback loop between AI and neuroscience, arguing that the coevolution of the two fields, grounded in technologies like wireless optogenetics and guided by reciprocal insight, will accelerate progress in both, while raising new challenges and opportunities for interdisciplinary collaboration.},
}

@article {pmid40428114,
year = {2025},
author = {Zheng, Y and Wu, S and Chen, J and Yao, Q and Zheng, S},
title = {Cross-Subject Motor Imagery Electroencephalogram Decoding with Domain Generalization.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {12},
number = {5},
pages = {},
doi = {10.3390/bioengineering12050495},
pmid = {40428114},
issn = {2306-5354},
abstract = {Decoding motor imagery (MI) electroencephalogram (EEG) signals in the brain-computer interface (BCI) can assist patients in accelerating motor function recovery. To realize the implementation of plug-and-play functionality for MI-BCI applications, cross-subject models are employed to alleviate time-consuming calibration and avoid additional model training for target subjects by utilizing EEG data from source subjects. However, the diversity in data distribution among subjects limits the model's robustness. In this study, we investigate a cross-subject MI-EEG decoding model with domain generalization based on a deep learning neural network that extracts domain-invariant features from source subjects. Firstly, a knowledge distillation framework is adopted to obtain the internally invariant representations based on spectral features fusion. Then, the correlation alignment approach aligns mutually invariant representations between each pair of sub-source domains. In addition, we use distance regularization on two kinds of invariant features to enhance generalizable information. To assess the effectiveness of our approach, experiments are conducted on the BCI Competition IV 2a and the Korean University dataset. The results demonstrate that the proposed model achieves 8.93% and 4.4% accuracy improvements on two datasets, respectively, compared with current state-of-the-art models, confirming that the proposed approach can effectively extract invariant features from source subjects and generalize to the unseen target distribution, hence paving the way for effective implementation of the plug-and-play functionality in MI-BCI applications.},
}

@article {pmid40426690,
year = {2025},
author = {Taha, BN and Baykara, M and Alakuş, TB},
title = {Neurophysiological Approaches to Lie Detection: A Systematic Review.},
journal = {Brain sciences},
volume = {15},
number = {5},
pages = {},
doi = {10.3390/brainsci15050519},
pmid = {40426690},
issn = {2076-3425},
abstract = {Background and Objectives: Lie detection is crucial in domains such as security, law enforcement, and clinical assessments. Traditional methods suffer from reliability issues and susceptibility to countermeasures. In recent years, electroencephalography (EEG) and particularly the Event-Related Potential (ERP) P300 component have gained prominence for identifying concealed information. This systematic review aims to evaluate recent studies (2017-2024) on EEG-based lie detection using ERP P300 responses, especially in relation to recognized and unrecognized face stimuli. The goal is to summarize commonly used EEG signal processing techniques, feature extraction methods, and classification algorithms, identifying those that yield the highest accuracy in lie detection tasks. Methods: This review followed PRISMA guidelines for systematic reviews. A comprehensive literature search was conducted using IEEE Xplore, Web of Science, Scopus, and Google Scholar, restricted to English-language articles from 2017 to 2024. Studies were included if they focused on EEG-based lie detection, utilized experimental protocols like Concealed Information Test (CIT), Guilty Knowledge Test (GKT), or Deceit Identification Test (DIT), and evaluated classification accuracy using ERP P300 components. Results: CIT with ERP P300 was the most frequently employed protocol. The most used preprocessing method was Bandpass Filtering (BPF), and the Discrete Wavelet Transform (DWT) emerged as the preferred feature extraction technique due to its suitability for non-stationary EEG signals. Among classification algorithms, Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and Convolutional Neural Networks (CNN) were frequently utilized. These findings demonstrate the effectiveness of hybrid and deep learning-based models in enhancing classification performance. Conclusions: EEG-based lie detection, particularly using the ERP P300 response to face recognition tasks, shows promising accuracy and robustness compared to traditional polygraph methods. Combining advanced signal processing methods with machine learning and deep learning classifiers significantly improves performance. This review identifies the most effective methodologies and suggests that future research should focus on real-time applications, cross-individual generalization, and reducing system complexity to facilitate broader adoption.},
}

@article {pmid40426631,
year = {2025},
author = {Mao, Q and Zhu, H and Yan, W and Zhao, Y and Hei, X and Luo, J},
title = {MCL-SWT: Mirror Contrastive Learning with Sliding Window Transformer for Subject-Independent EEG Recognition.},
journal = {Brain sciences},
volume = {15},
number = {5},
pages = {},
doi = {10.3390/brainsci15050460},
pmid = {40426631},
issn = {2076-3425},
support = {23JK0556//the Scientific Research Program Founded by Shaanxi Provincial Education Department of China/ ; 61906152, 62376213 and U21A20524//the National Natural Science Foundation of China/ ; },
abstract = {Background: In brain-computer interfaces (BCIs), transformer-based models have found extensive application in motor imagery (MI)-based EEG signal recognition. However, for subject-independent EEG recognition, these models face challenges: low sensitivity to spatial dynamics of neural activity and difficulty balancing high temporal resolution features with manageable computational complexity. The overarching objective is to address these critical issues. Methods: We introduce Mirror Contrastive Learning with Sliding Window Transformer (MCL-SWT). Inspired by left/right hand motor imagery inducing event-related desynchronization (ERD) in the contralateral sensorimotor cortex, we develop a mirror contrastive loss function. It segregates feature spaces of EEG signals from contralateral ERD locations while curtailing variability in signals sharing similar ERD locations. The Sliding Window Transformer computes self-attention scores over high temporal resolution features, enabling efficient capture of global temporal dependencies. Results: Evaluated on benchmark datasets for subject-independent MI EEG recognition, MCL-SWT achieves classification accuracies of 66.48% and 75.62%, outperforming State-of-the-Art models by 2.82% and 2.17%, respectively. Ablation studies validate the efficacy of both the mirror contrastive loss and sliding window mechanism. Conclusions: These findings underscore MCL-SWT's potential as a robust, interpretable framework for subject-independent EEG recognition. By addressing existing challenges, MCL-SWT could significantly advance BCI technology development.},
}

@article {pmid40426214,
year = {2025},
author = {Li, K and Liang, H and Qiu, J and Zhang, X and Cai, B and Wang, D and Zhang, D and Lin, B and Han, H and Yang, G and Zhu, Z},
title = {Reveal the mechanism of brain function with fluorescence microscopy at single-cell resolution: from neural decoding to encoding.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {22},
number = {1},
pages = {118},
pmid = {40426214},
issn = {1743-0003},
support = {2024XHSZ-Y08//Zhejiang Health Information Association Research Program/ ; 82401786//National Natural Science Foundation of China/ ; 82201637//National Natural Science Foundation of China/ ; 2024KY246//Zhejiang Provincial Medical and Health Technology Project/ ; BMI2400025//Open Research Fund of the State Key Laboratory of Brain-Machine Intelligence, Zhejiang University/ ; 2024C03150//Key R&D Program of Zhejiang Province/ ; J-202402//Qiushi Youth Program from Scientific Research Cultivation Foundation/ ; },
mesh = {*Brain/physiology/cytology ; Humans ; Microscopy, Fluorescence/methods ; Animals ; *Single-Cell Analysis/methods ; *Neurons/physiology ; Optogenetics ; *Brain Mapping/methods ; },
abstract = {As a key pathway for understanding behavior, cognition, and emotion, neural decoding and encoding provide effective tools to bridge the gap between neural mechanisms and imaging recordings, especially at single-cell resolution. While neural decoding aims to establish an interpretable theory of how complex biological behaviors are represented in neural activities, neural encoding focuses on manipulating behaviors through the stimulation of specific neurons. We thoroughly analyze the application of fluorescence imaging techniques, particularly two-photon fluorescence imaging, in decoding neural activities, showcasing the theoretical analysis and technological advancements from imaging recording to behavioral manipulation. For decoding models, we compared linear and nonlinear methods, including independent component analysis, random forests, and support vector machines, highlighting their capabilities to reveal the intricate mapping between neural activity and behavior. By employing synthetic stimuli via optogenetics, fundamental principles of neural encoding are further explored. We elucidate various encoding types based on different stimulus paradigms-quantity encoding, spatial encoding, temporal encoding, and frequency encoding-enhancing our understanding of how the brain represents and processes information. We believe that fluorescence imaging-based neural decoding and encoding techniques have deepened our understanding of the brain, and hold great potential in paving the way for future neuroscience research and clinical applications.},
}

*Brain/physiology/cytology
Humans
Microscopy, Fluorescence/methods
Animals
*Single-Cell Analysis/methods
*Neurons/physiology
Optogenetics
*Brain Mapping/methods