IntroductionVirtual reality (VR) provides an immersive environment for inducing emotional experiences, offering a naturalistic framework for investigating brain network dynamics. However, traditional emotional neuroscience has largely focused on regional activations, leaving the topological robustness and adaptive capacity of integrated brain networks underexplored. This study addresses this gap by applying a graph-theoretical framework to quantify how different emotional states modulate the resilience of functional architectures against systematic disruptions.MethodsIn this study, we examined the resilience of EEG-based functional brain networks during negative, neutral, and positive emotional states induced by VR stimuli. Functional connectivity was computed using coherence across six frequency bands (delta to high gamma), and graph-theoretical measures were applied to characterize network topology. To assess resilience, we simulated network disruptions using two complementary approaches: targeted attacks (removing high-centrality nodes) and random failures (removing nodes randomly). Changes in global efficiency and the largest connected component were tracked as nodes were progressively removed.ResultsOur findings revealed emotion-specific resilience profiles. In the alpha band, both negative and positive emotions demonstrated enhanced resilience to targeted attacks compared to neutral states, maintaining higher efficiency and greater network integrity, with positive emotions showing particularly strong preservation of large-scale connectivity. In the high gamma band, networks during negative emotional states exhibited greater robustness than those during positive emotions, indicating enhanced capacity to withstand targeted disruptions.DiscussionThese findings suggest that emotional experiences are associated with differences in functional brain architecture that affect network robustness and adaptability, providing insights into neural mechanisms of emotion regulation and potential applications for emotion-aware VR systems.

Hypertension is a major risk factor for cardiovascular disease and requires effective long-term monitoring. Photoplethysmography (PPG), acquired from wearable optical sensors, offers a convenient and non-invasive signal source for cuffless blood pressure (BP) estimation, but existing studies have mainly emphasized model architecture optimization, with limited systematic investigation of signal representation. This study systematically compares seven one-dimensional-to-two-dimensional signal transformation methods and evaluates multiple architectural variants for PPG-based cuffless BP estimation under a unified framework. Experiments were conducted using PPG and arterial BP signals from the UCI Open Blood Pressure Database. The best-performing configuration, based on continuous wavelet transform (CWT), achieved estimation errors of 3.80 ± 5.02 mmHg for systolic BP and 1.65 ± 2.70 mmHg for diastolic BP. Further real-world validation on 26 participants using an Omron cuff-based monitor as the reference showed good consistency, with correlation coefficients of R = 0.96 for SBP and R = 0.74 for DBP. The results demonstrate that appropriate signal representation, particularly CWT, plays a critical role in improving estimation accuracy and robustness, and may facilitate the development of wearable cuffless BP monitoring systems.

Post-stroke rehabilitation strategies rely heavily on accurate motor function assessment. Scale-based assessments face inherent limitations, such as inter-rater variability and long assessment time. Neurophysiological biomarkers are emerging as more objective, complementary assessment tools. However, few studies have integrated multi-states functional near-infrared spectroscopy (fNIRS) data to fit the Fugl-Meyer Assessment (FMA) scores of stroke patients with machine learning. This study enrolled 57 stroke patients and acquired fNIRS signals covering the sensorimotor cortex during both resting-state and grip-task conditions. A modified sequential forward selection algorithm was employed to select optimal features from three domains: functional connectivities (FCs) and graph theory metrics of resting-state fNIRS and task-state fNIRS, and clinical characteristics. Support vector regression was then applied on the multi-domain feature set to predict FMA scores. Both for cortical and subcortical lesion-type stroke patients, the resting-state + task-state fNIRS fused model demonstrated significantly reduced prediction errors compared to single-state models. Optimal performance was achieved at 45

Conventional neuroimaging tools for post-stroke motor function evaluation (e.g., EEG, fMRI) have some constraints. Conversely, functional near-infrared spectroscopy (fNIRS) offers a viable compromise. Nevertheless, few studies have yet quantitatively assessed the current motor function scores based on fNIRS data. This study proposed a Graph Convolutional Network (GCN) and Support Vector Regression (SVR) fusion model to fit Fugl-Meyer Assessment (FMA) scores by leveraging a multi-state integration of fNIRS metrics and clinical indicators. After preprocessing, brain network features and GCN features were extracted from the fNIRS data. Then a modified forward search method was used for SVR model training and feature selection from three feature sets: resting-state/ task-state fNIRS feature sets and clinical feature set. Finally, the SVR model was employed to estimate the FMA scores. The coefficient of determination (R ${}^{{2}}\text {)}$ , root mean square error (RMSE), and mean absolute error (MAE) were utilized to evaluate the models. The GCN-SVR fusion model demonstrated good goodness-of-fit, and exhibited limited variability. The multi-state model demonstrated better performance than both single-state models (P< 0.001). For cortical cases, Aggregate measures over the nine sparsity levels confirmed both high accuracy and stable fitting (R ${}^{{2}}= 0.8397\pm 0.0487$ , RMSE $= 6.75\pm 1.06$ , MAE $= 4.91\pm 0.94$ ), with R2 consistently above 0.76 across sparsity levels. In subcortical patients, the multi-state model achieved a mean R2 of $0.7562\pm 0.0185$ , with RMSE $= 10.51\pm 0.40$ and MAE $= 7.74\pm 0.53$ . The proposed GCN-SVR fusion algorithm based on fNIRS data achieved high accuracy and stable performance in fitting FMA scores, while subset-based sequential forward selection enhances multi-dataset feature selection.

Background and Objective:Robust single-channel electroencephalography (EEG) acquisition is fundamental to wearable sleep monitoring, yet signal quality is highly susceptible to electrode placement and lacks an objective evaluation criterion. This study aimed to optimize forehead electrode configuration and establish a quantitative framework for evaluating EEG signal quality.Methods:A Differential-Transmission Stable-Triangle Principle was proposed to optimize the geometric arrangement of the Active, Reference, and Ground electrodes for improved impedance symmetry and common-mode noise rejection. A data-driven signal quality assessment framework was developed by constructing a weighted distance metric between wakefulness and deep-sleep feature prototypes. Twenty electrode configurations were evaluated using an orthogonal experimental design involving 27 healthy participants under controlled pre-sleep resting conditions. Repeated-measures analysis of variance (ANOVA) and paired \emph{t}-tests were performed for statistical analysis.Results:Electrode configuration had a significant effect on signal quality ($F(19,494)=86.14$, $P<0.001$). Compact nasion-referenced layouts, particularly Fp1--Nz--Nz and Fp2--Nz--Nz, consistently achieved the highest signal quality scores, significantly outperforming conventional mastoid-referenced configurations. No statistically significant difference was observed between ipsilateral and contralateral reference placements ($P=0.452$). The proposed assessment framework effectively quantified signal quality differences among electrode configurations.Conclusions:The proposed Differential-Transmission Stable-Triangle Principle provides an effective strategy for optimizing wearable forehead EEG acquisition. The identified nasion-referenced configurations offer superior signal fidelity and provide practical design guidelines for next-generation wearable sleep monitoring systems before clinical overnight validation.\end{abstract}

The monosynaptic cortico-motoneuronal connections suggest the possibility of individual motor units (MUs) receiving independent commands from motor cortex. However, previous studies that used corticomuscular coherence (CMC) between electroencephalogram (EEG) signals and electromyogram (EMG) signals have not directly explored the corticospinal functionality at the single motoneuron level. The objective of this study is to find out whether synchronous activities exist between the motor cortex and individual MUs. Corticomuscular coherence was calculated between the EEG signals and the MU firing event trains which were extracted using the EMG decomposition technique. The results showed that some but not all MUs indeed had significant coherent activities with the contralateral motor cortex, which we named the cortico-motoneuronal coherence (CMnC). In contrast to the CMC only occurring in β and γ bands, CMnC occurred across the four common EEG frequency bands (θ, α, β and γ). Further, we identified individual MUs that showed significant interactions with the motor cortex. These coherent MUs (CohMU) could still be found even when the EMG signals were not coupled with the cortical activities. Compared with conventional CMC, our preliminary results indicated that the CMnC could potentially help to investigate the complex coupling between cortical and muscular activities due to its ability to separate different correlated components. This study proves that corticomuscular coherence exists at a single MU level, which provides a new perspective for the research on corticomuscular coupling. Further study on the CMnC could help deepen our understanding of the neural control of movement.

Background: The susceptibility of different individuals to the same dosage of the same anesthetic drug is influenced by many factors. In addition to basic conditions such as age and gender, susceptibility to anesthesia is also related to brain activity during the resting state. However, the specific physiological mechanisms involved are still poorly understood. Methods: Twenty healthy volunteers who participated in propofol-induced sedation were divided into two groups according to their susceptibility to anesthesia and the electroencephalogram were recorded in baseline and moderate sedation states. Functional connectivity in the baseline was measured by the debiased weighted phase lag index between different brain regions to find band-specific differences in source space and sensor space respectively. Classifiers for anesthesia susceptibility were trained according to connectivity and based on bi-encoder autoencoder and convolutional neural network. Results: In the baseline state, the specific frequency band was mainly in the low alpha band, and showed that the subjects more sensitive to propofol had more weaker brain activities. Sourcespace functional connectivity in the specific band during the resting state could successfully assess the individual's susceptibility to propofol with an accuracy of 82.52%. Conclusions: The source-space functional connectivity in the specific frequency band during the resting state serves as a reliable biomarker that can effectively assess the susceptibility to propofol during anesthesia. This study offers novel insights to help anesthesiologists enable precision anesthesia.

The human brain exhibits a complex organization into functional communities, with interconnected regions of interest (ROIs) playing a critical role in emotional processing. However, traditional transformer models for EEG-based emotion recognition often treat all ROIs equally, neglecting the crucial role of these communities. To address this limitation, we propose the brain network community-aware global-local transformer (BN-BrainTF) model. BN-BrainTF employs source localization to identify brain activity origins within functional communities derived from EEG data. The model then extracts local features specific to each community and global features capturing whole-brain context using a spectral-spatial attention module and a dynamical graph convolutional network based on functional connectivity. A global-local transformer with cross-attention integrates these features within each community, while a fusion transformer captures interactions between all communities. We evaluated BN-BrainTF on two benchmark datasets with distinct emotional classification paradigms. On the SEED dataset (three emotional states: positive, negative, and neutral), our model achieved 77.92% average accuracy across all subjects. For the SEED-IV dataset (four emotional states: happy, sad, fear, and neutral), BN-BrainTF achieved 59.41% average accuracy across all subjects. These results demonstrate the effectiveness of incorporating functional brain community structure for EEG-based emotion recognition, with consistent performance across different emotional classification tasks. The comprehensive representation of brain activity informed by functional communities provides superior emotion recognition performance compared to traditional approaches that ignore the underlying brain network organization.

Motor unit (MU) discharge information extracted via real-time electromyogram (EMG) decomposition shows superiority in dexterous finger motion decoding. The variation of excitation levels can, however, lead to MU recruitment/de-recruitment, resulting in nonstationary EMG activities and then degraded decomposition and decoding performance; therefore, a novel online decomposition approach based on the multiple separation vector strategy was developed in this study. First, the separation vectors corresponding to different excitation levels were extracted offline via the fast independent component analysis (FastICA) algorithm and then merged to construct the separation vector pool for each MU via a previous MU action potential classification network. Under the online condition, a dual self-attention residual network was proposed to identify the excitation level, and the separation vectors were alternated correspondingly [termed alternating strategy (AS) method]. The conventional method that always used a fixed separation (FS) vector was compared. The 30-min synthetic EMG and the 15-min experiment EMG with the neural drive and the contraction strength, respectively, varying between 0% and 45% maximum voluntary contraction (MVC) were used. The experiment involved dexterous multifinger extension with isometric contractions. The results showed that the AS method obtained a higher spike consistency (87.55% +/- 3.75 % versus 84.05% +/- 4.11 %) with the true spike trains using the synthetic EMG and improved force prediction performance using the experiment EMG, i.e., a higher correlation ( R-2 : 0.82 +/- 0.06 versus 0.76 +/- 0.07 ) and a lower prediction error root-mean-square error (RMSE): 8.87% +/- 1.53 % versus 13.61%MVC +/- 0.92 %MVC) compared with the FS method. Further development of the proposed method could potentially provide a robust humanmachine interface for dexterous finger force prediction in realistic applications.

With the rapid development of the construction industry, the safety and stability requirements of formwork support systems during construction have become increasingly strict. The purpose of this study is to integrate BIM (Building Information Modeling) and FEM (Finite Element Method) to develop an intelligent force analysis and safety assessment model to improve the design and construction safety of formwork support systems. Through BIM technology, the accurate modelling of the bracket system is realized and combined with FEM for force analysis, the model shows high-precision performance under various load conditions, and the force analysis error is reduced by 15% compared with traditional methods. After the machine learning algorithm is introduced, the model can automatically optimize the parameters of the bracket, and the experimental results show that the displacement of the optimized bracket under the ultimate load is reduced by 20%, significantly improving the bracket's stability. In the comprehensive safety assessment test, the model successfully identified three potential safety hazards and provided effective rectification suggestions, effectively preventing safety accidents in practical application. This study not only promotes the application of BIM and FEM in formwork support systems but also provides a new technical path for the intelligent development of the construction industry.

Major depressive disorder (MDD) is a serious psychiatric disorder characterized by persistent feelings of sadness, hopelessness, and lack of interest or pleasure in daily activities. Yet, reliable diagnostic tools for this brain disorder remain lacking. Functional near-infrared spectroscopy (fNIRS), an optical brain imaging technique, offers a promising approach for monitoring cerebral hemodynamic activity associated with MDD. In this study, we propose a novel algorithm based on wavelet coherence and a state-pathology separation network (WCSN) to automatically detect MDD using a dual-channel fNIRS system. The fNIRS signals were first preprocessed and transformed into two-dimensional feature maps using a wavelet coherence method. Following this, a wrapped exhaustive search was applied to select the optimal subset of feature maps, which was then utilized to reconstruct the dataset. Finally, samples were classified using the state-pathology separation network that employed a dual-encoder convolutional autoencoder (DCoAE) module to separate the feature maps into state features and pathology features, while a Transformer module distinguished MDD patients from healthy controls based solely on pathology features. The WCSN algorithm achieved exceptional performance with an accuracy of 0.923 +/- 0.068 and a subject accuracy of 0.918 +/- 0.076. Our result highlights the WCSN algorithm's ability to isolate pure pathology features, enhancing classification robustness and generalizability under dual-channel data conditions. Taken together, the proposed WCSN algorithm is well-suited for home-based MDD screening applications.

Since sudden and recurrent epileptic seizures seriously affect people's lives, computer-aided automatic seizure detection is crucial for precise diagnosis and prompt treatment. A novel seizure detection algorithm named channel selection-based temporal convolutional network (CS-TCN) was proposed in this article. First, electroencephalogram (EEG) recordings were segmented into 2-s intervals and features were extracted from both the time and frequency domains. Then, the expanded fisher score channel selection method was employed to select channels that contribute the most to seizure detection. Finally, the features from selected EEG channels were fed into the TCN to capture inherent temporal dependencies of EEG signals and detect seizure events. Children Hospital Boston and Massachusetts Institute of Technology (CHB-MIT) and Siena datasets were used to verify the detection performance of the CS-TCN algorithm, achieving sensitivities of 98.56% and 98.88%, and specificities of 99.80% and 99.88% in samplewise analysis, respectively. In eventwise analysis, the algorithm achieved sensitivities of 97.57% and 95.00%, with delays of 6.91 and 18.62 s, and FDR/h of 0.11 and 0.39, respectively. These results surpassed state-of-the-art few-channel algorithms for both datasets. CS-TCN algorithm offers excellent performance while simplifying model complexity and computational requirements, thus showcasing its potential for facilitating seizure detection in home environments.

OBJECTIVE:Wearable devices are effective for detecting generalized tonic-clonic seizures (GTCS). However, many daily activities are often misclassified as GTCS, leading to a decline in user confidence. This study recommends utilizing wristband three-axis accelerometer (ACC), three-axis gyroscope (GYRO), and surface electromyography (sEMG) signals for GTCS detection and presents a novel seizure detection algorithm that offers high sensitivity and a reduced false alarm rate (FAR). METHODS:Inpatients with epilepsy and out-of-hospital healthy subjects were recruited and required to wear a wristband device to collect wristband signals. The proposed algorithm comprises five steps: preprocessing, motion filtering, feature extraction, classification, and postprocessing. The variations in performance across different signal combinations were compared. Additionally, the impact of training the model using only inpatient data versus the complete dataset on the algorithm's performance was also investigated. RESULTS:Wristband signals were collected from 45 patients and 30 healthy subjects, encompassing a total of 3367.3 h and including 60 GTCS. The proposed algorithm achieved 100 % sensitivity and a FAR of 0.1070/24 h. It demonstrated higher sensitivity and lower FAR compared to combinations with fewer signal modalities. In addition, the model trained on only in-hospital data demonstrates high sensitivity (98.33 %) and high FAR (0.9845/24 h). SIGNIFICANCE:The algorithm proposed for detecting GTCS using wristband ACC, GYRO, and sEMG signals achieved encouraging results, demonstrating the feasibility of this signal combination. Furthermore, incorporating out-of-hospital data into model training proved to be an effective solution for reducing FAR, which could facilitate the clinical application of seizure detection algorithms.

BACKGROUND:The compensatory pattern between the two hemispheres after stroke has been the focus of research. Some evidence suggests bilateral stimulation more effectively engages networks across both hemispheres compared to the affected side only. OBJECTIVES:To explore whether the stimulating at bilateral limbs of stroke patients by acupuncture may better engage compensatory reorganization between the hemispheres compared to stimulating at the hemiplegic limb. METHODS:Conscious patients with hemiplegia were screened. Brain activity was assessed by the functional near-infrared spectroscopy(fNIRS) in three states: no treatment, acupuncture on the affected side, and then acupuncture on both sides. Brain activation and directed functional connectivity(FC) was analyzed between the two acupuncture strategies. RESULTS:Acupuncture of bilateral limbs resulted in stronger activation in the primary motor cortex(M1) of the ipsilesional hemisphere than acupuncture of the affected side only. And no significantly enhanced activation of the contralesional hemisphere was observed after acupuncture on the healthy limb. Besides, the FCs from the ipsilesional premotor cortex to the contralesional sensory-related area were significantly enhanced, and the FCs from the sensory area to motor area within the ipsilesional hemisphere were also significantly enhanced. Additionally, FCs from contralesional M1 to ipsilesional motor area were attenuated. CONCLUSION:Stimulating at bilateral limbs by acupuncture could lead to greater brain network remodeling in the motor-related areas compared to stimulating solely at the affected side, and not through more stimulation.

Proper monitoring of anesthesia stages can guarantee the safe performance of clinical surgeries. In this study, different anesthesia stages were classified using near-infrared spectroscopy (NIRS) signals with machine learning. The cerebral hemodynamic variables of right proximal oxyhemoglobin (HbO2) in maintenance (MNT), emergence (EM) and the consciousness (CON) stage were collected and then the differences between the three stages were compared by phase-amplitude coupling (PAC). Then combined with time-domain including linear (mean, standard deviation, max, min and range), nonlinear (sample entropy) and power in frequency-domain signal features, feature selection was performed and finally classification was performed by support vector machine (SVM) classifier. The results show that the PAC of the NIRS signal was gradually enhanced with the deepening of anesthesia level. A good three-classification accuracy of 69.27% was obtained, which exceeded the result of classification of any single category feature. These results indicate the feasibility of NIRS signals in performing three or even more anesthesia stage classifications, providing insight into the development of new anesthesia monitoring modalities.

Motor unit (MU) discharge information obtained via electromyogram (EMG) decomposition can be used to decode dexterous multi-finger movement intention for neural-machine interfaces (NMI). However, the variation of the motor unit action potential (MUAP) shape resulted from forearm rotation leads to the decreased performance of EMG decomposition, especially under the real-time condition and then the degradation of motion decoding accuracy. The object of this study was to develop a method to realize the accurate extraction of MU discharge information across forearm pronated/supinated positions in the real-time condition for dexterous multi-finger force prediction. The FastICA-based EMG decomposition technique was used and the proposed method obtained multiple separation vectors for each MU at different forearm positions in the initialization phase. Under the real-time condition, the MU discharge information was extracted adaptively using the separation vector extracted at the nearest forearm position. As comparison, the previous method that utilized a single constant separation vector to extract MU discharges across forearm positions and the conventional method that utilized the EMG amplitude information were also performed. The results showed that the proposed method obtained a significantly better performance compared with the other two methods, manifested in a larger coefficient of determination ( R 2 ) and a smaller root mean squared error (RMSE) between the predicted and recorded force. Our results demonstrated the feasibility and the effectiveness of the proposed method to extract MU discharge information during forearm rotation for dexterous force prediction under the real-time conditions. Further development of the proposed method could potentially promote the application of the EMG decomposition technique for continuous dexterous motion decoding in a realistic NMI application scenario.

The classification of epileptic seizures is crucial for the treatment of epilepsy, and currently, this task primarily relies on clinical doctors, which is complex and time-consuming. Our study aims to perform an eight-category classification of epileptic seizures using multi-domain features from electroencephalogram (EEG) and electromyogram (EMG) signals, combined with deep learning algorithms. In this research, EEG signals are decomposed via the Variational Mode Decomposition (VMD) method and features are extracted in the time domain, frequency domain, and from information theory, while EMG signals are processed to extract relevant features directly. Following feature fusion, the Resnet model is employed for classification. Under the evaluation criteria based on seizure events and across subjects, the accuracy of the eight-category classification of seizures is 75%, with a weighted F1 score of 0.75, where the accuracy and recall for Tonic-Clonic Seizures are 100%. Moreover, in this task, we also compared the classification effects using only EEG signals and using both EEG and EMG signals, as well as the performance differences between machine learning models and deep learning models. The results indicate that the classification performance is optimized when both EEG and EMG signals are used in conjunction with a neural network model.

Sleep staging is a crucial task in sleep monitoring and diagnosis, but clinical sleep staging is both time-consuming and subjective. In this study, we proposed a novel deep learning algorithm named feature fusion temporal convolutional network (FFTCN) for automatic sleep staging using single-channel EEG data. This algorithm employed a one-dimensional convolutional neural network (1D-CNN) to extract temporal features from raw EEG, and a two-dimensional CNN (2D-CNN) to extract time-frequency features from spectrograms generated through continuous wavelet transform (CWT) at the epoch level. These features were subsequently fused and further fed into a temporal convolutional network (TCN) to classify sleep stages at the sequence level. Moreover, a two-step training strategy was used to enhance the model's performance on an imbalanced dataset. Our proposed method exhibits superior performance in the 5-class classification task for healthy subjects, as evaluated on the SHHS-1, Sleep-EDF-153, and ISRUC-S1 datasets. This work provided a straightforward and promising method for improving the accuracy of automatic sleep staging using only single-channel EEG, and the proposed method exhibited great potential for future applications in professional sleep monitoring, which could effectively alleviate the workload of sleep technicians.