Search Results (204)

Background and Objectives: A useful strategy to study the dynamic functioning of the human brain is to focus on the relationship between the brain’s functional integration and topological complexity. Theoretical analysis using information-theoretic metrics shows that the functional topological complexity of the brain has a curvilinear inverted-U relationship with its functional integration. This relationship is supported by simulation studies of the functional integration and topological complexity of cortical electroencephalographic (EEG) dipole sources. However, scalp-level EEG functional topological complexity follows an inverse relationship with functional integration. The present study investigated this discrepancy for the case of alpha-range (7–13 Hz) EEG signals predominant during wakeful resting brain states. Methods: Three different EEG source factors were explored that could affect the integration–complexity relationship for scalp-level EEG: volume conduction, regularity, and modularity. The EEG simulations parametrically manipulated the regularity and modularity of cortical EEG dipole source signals and their scalp-level projections to identify how EEG functional integration and topological complexity depended on these factors at the cortical and scalp levels before and after the spatial mixing effects of volume conduction. Simulation findings were compared with results obtained from empirically measured resting state EEG signals. Results: Simulated source-level EEG functional integration and topological complexity followed a curvilinear inverted-U relationship, whereas scalp-level functional topological complexity had an inverse relationship with functional integration, with this difference occurring for irregular (aperiodic) sources but not regular (periodic) sources. EEG source modularity was related to the functional integration–complexity relationship only for regular EEG signals. These findings were consistent with the results obtained from the empirical EEG signals. Conclusions: The discrepancy between source- and scalp-level EEG functional integration–complexity relationships primarily reflects the spatial mixing effects of volume conduction and its secondary effects on scalp EEG regularity. Full article

Background: Stroke is a leading cause of neurological disability worldwide, with upper limb impairment affecting approximately 70% of survivors and only 5–20% achieving complete dexterity recovery at six months. Brain–computer interface (BCI) neurorehabilitation decodes motor intentions from electroencephalographic (EEG) signals to deliver synchronized functional electrical stimulation (FES) and virtual reality feedback, creating a closed-loop neurofeedback system that reinforces motor learning. While existing evidence supports BCI efficacy and safety in chronic stroke, its feasibility, safety, and cost-effectiveness during the acute and subacute phase (2 to 12 weeks post-stroke), when neuroplasticity is heightened, remain underexplored. Furthermore, there is a paucity of data regarding preliminary health economic analyses for BCI rehabilitation in acute stroke rehabilitation settings. Methods: This prospective, open-label, single-arm pragmatic feasibility pilot trial will recruit 12 patients with hemorrhagic or ischemic stroke (2–12 weeks post-stroke) undergoing inpatient rehabilitation from a public healthcare institution. Up to 15 sessions of BCI-rehabilitation of 30 min each using the recoveriX system will be supervised by a trained therapist or clinical research assistant (4–5 sessions/week over 3–4 weeks), followed by standard occupational therapy within 30–60 min of BCI-rehabilitation. Primary outcomes assessing feasibility and adherence include eligibility and recruitment rate (%/screened); tolerability using self-rated System Usability Scale (SUS) score; within-session adherence > 80%/240 trials, summated for completed trials per patient; programme completion number > 80% of scheduled (>12/15) sessions; and training-related adverse events per patient ≤ 17% (≤2/12 sessions). Secondary outcome measures include clinical efficacy by arm impairment scale using hemiplegic Upper Limb Fugl–Meyer Motor Assessment (FMA-UE), hand function using Action Research Arm Test (ARAT), admission and discharge functional status (Functional Independence Measure-FIM (18–126), Modified Barthel Index-MBI (0–100), stroke impact scale (SIS_3.0), arm, participation domains), and economic analysis. All outcomes will be measured by trained therapists/researchers at baseline week 0, week 3–4 (post-BCI-rehabilitation), and week 12 and 24 (follow-up). BCI-rehabilitation EEG-derived electrophysiological correlates of recovery will be extracted to better understand participant progress over time. An incremental cost-utility analysis will compare the BCI-rehabilitation participants against propensity-matched historical controls from the TTSH stroke rehabilitation registry (2017 to 2025), stratified by baseline motor severity. Discussion: This study will provide preliminary evidence on feasibility, tolerability, safety, clinical efficacy, and cost-effectiveness of early BCI-rehabilitation in acute/subacute stroke to better inform clinicians on its implementation. Full article

Wearable vibrotactile devices are increasingly used in virtual reality, teleoperation and neurorehabilitation, but objective EEG evaluation of glove-mediated touch remains limited. We compared EEG recorded during natural object interaction with EEG recorded when tactile feedback was reproduced through a vibrotactile smart glove. Data were collected with an eight-channel wireless headset while participants interacted with three object types (bottle, cube, and sphere) in natural-touch and glove-mediated conditions. An exploratory model trained on natural-touch data and tested on glove-mediated trials yielded rounded cross-condition accuracies of 83%, 78%, and 68% for bottle vs. rest, cube vs. rest, and sphere vs. rest, respectively. These findings suggest that some object-related EEG patterns may carry across conditions, but they should not be interpreted as evidence of physiological equivalence. Supplementary analyses using repeated-run evaluation, band-power and ERD/ERS summaries, temporal-window inspection, and channel ablation were included as cautious interpretability checks. The results underscore the need for larger subject-independent studies, stronger artifact-control pipelines, formal statistical testing, and richer haptic conditions before asserting equivalence to natural touch. Full article

Sleep monitoring and analysis are essential for understanding overall health, improving sleep quality, and detecting potential disorders early. This study presents a multimodal approach for automatic sleep staging and quality assessment using a reduced set of bio-signals: a single electroencephalographic (EEG) lead (F4–F3), a single EOG lead, and the photo-plethysmographic (PPG) signal. The proposed methodology includes a hierarchical sleep staging classifier, an automatic sleep staging algorithm, and a subject-specific Sleep Quality Index (SQI) for objective sleep quality assessment. The 5-class sleep staging classifier employs a cascaded architecture of two sequential 3-class models (Wake-REM-NREM and N1-N2-N3), trained and tested on multimodal features derived from physiological signals (EEG, EOG, and PPG) of the BOAS (Bitbrain Open Access Sleep) dataset. The resulting 5-class classifier achieved 90.8% accuracy with a reduced memory footprint (3.14 MB). To assess subject-independent generalization and prevent data leakage between training and test sets, a Leave-One-Subject-Out (LOSO) validation was performed, confirming the robustness of the proposed classifier across unseen subjects. The classifier was subsequently integrated into an automatic sleep staging algorithm. Validation on 14 unseen subjects yielded accuracies ranging from 80.26% to 91.99% using heuristic post-processing rules, while a Hidden Markov Model (HMM)-based approach further improved performance, reaching a peak accuracy of 91.99%. The proposed SQI combines sleep-related metrics extracted from staging, considering multiple sleep aspects (i.e., duration, intensity, and continuity-fragmentation). A calibration strategy was proposed to customize the SQI based on sleep scoring parameters and the subjective quality score derived from sleep diaries and questionnaires (PSQI). This subject-specific strategy was validated on a public dataset, optimizing weights across multiple nights, followed by an independent test on a subsequent night and demonstrating strong alignment between the calculated SQI and the subjective sleep quality score (MAE = 10.81). Finally, the framework provides resource-efficient sleep staging and custom quality estimation, validating its readiness for practical, long-term sleep monitoring. Full article

Understanding how brain activity varies across neurological and neurodevelopmental disorders requires tools capable of revealing patterns hidden in complex electroencephalographic (EEG) data. Conditions such as epilepsy, Alzheimer’s disease, dementia, and autism exhibit distinct alterations in neural oscillations and connectivity, which remain difficult to interpret in real time; therefore, this study proposes an interactive interface for intuitive exploration and analysis of disease-specific EEG dynamics. The system integrates classical signal processing techniques and computational modeling to extract spectral features, inter-electrode coherence, and spatial activation patterns, which are visualized through spectrograms, topographic maps, and connectivity graphs that update continuously. In addition, a web-based platform is incorporated to enable clinicians and technicians to store and manage patient information, including diagnosis, severity level, number of recordings, sampling frequency, recording duration, and acquisition dates, supporting structured data organization and longitudinal monitoring. The results demonstrate that the interface captures meaningful differences between disorders, with epileptic patterns showing strong synchronization and burst activity, while neurodegenerative conditions exhibit spectral slowing and reduced connectivity. Overall, the proposed framework provides an effective and accessible tool for EEG visualization, combining interactive analysis with clinical data management to support research, education, and potential clinical applications. Full article

Background: Electroencephalographic (EEG) functional connectivity analysis requires multiple signal-processing, source-modelling, and statistical steps that can limit its adoption in clinician-led randomised controlled trials (RCTs). NeuroStat was developed as a prototype research tool to integrate this workflow; formal usability validation with clinician end-users has not yet been conducted. Methods: NeuroStat is an open-source Python/PyQt6 desktop application that integrates automated artefact removal (a Generalised Eigenvalue Decomposition for Artefact Identification [GEDAI] pathway and a traditional Artefact Subspace Reconstruction (ASR)/Independent Component Analysis (ICA)/ICLabel pathway), boundary element model (BEM) source localisation using the Desikan–Killiany atlas (68 cortical regions), Phase Lag Index (PLI) connectivity estimation across five canonical frequency bands, and RCT-oriented statistical analysis. Evaluation separated sensor-space and source-space claims: a sensor-level simulation (repeated across five independent random seeds) tested preprocessing robustness, a repeated source-space simulation tested recovery of a known cortical parcel-pair contrast after forward projection and inverse reconstruction, a PhysioNet benchmark tested posterior Desikan–Killiany alpha PLI in 20 healthy adults, and an illustrative application to 20 sessions from a published chiropractic RCT demonstrated real-world workflow applicability. Results: In the sensor-level simulation benchmark, the Traditional pathway achieved a mean absolute error of 0.168 ± 0.017 PLI units and root mean squared error of 0.219 ± 0.045 (mean ± SD across five independent random seeds) across all artefact conditions. In the source-space simulation, reconstructed alpha PLI for the known bilateral lateral-occipital parcel pair exceeded anterior control edges across 60 repeated condition runs (mean known-control difference = 0.105 PLI units, 95% CI 0.096–0.114; t(59) = 22.61, p < 0.001). In the PhysioNet source-space benchmark, posterior Desikan–Killiany alpha PLI was higher during eyes-closed than eyes-open rest (Cohen’s d = 0.85, p = 0.001; 16/20 subjects showing the expected direction) after ICLabel-enabled preprocessing. In the pilot RCT application, all 20 sessions completed processing without manual intervention, with default-mode network alpha PLI showing a pre-to-post change of +0.071 in the intervention group versus +0.015 in the active control group. Conclusions: NeuroStat integrates preprocessing, source-space construction, connectivity estimation, and statistical reporting within a parameter-logged desktop workflow for EEG functional connectivity studies. Current evidence supports initial technical feasibility, sensor-level preprocessing robustness for one pathway in controlled simulations, source-space recovery of a known parcel-level contrast, source-space sensitivity to an expected posterior alpha resting-state contrast, and error-free processing across 20 real RCT sessions in a pilot workflow demonstration. Formal usability testing, test–retest reliability analysis, participant-specific source-model validation, and clinical-population validation remain necessary before clinician-facing or trial-deployment claims can be made. Full article

Electroencephalographic (EEG) signals have been extensively studied for emotion detection and, more recently, as an alternative for biometric identification and authentication. Biometric methods based on physiological signals are a non-conventional approach for personal identification, and their study is currently considered an open research field. However, EEG-based biometric systems face several challenges, including the influence of emotional states, which can affect their performance. This study evaluates the effect of emotional states on the performance of an EEG-based biometric system. Four widely used databases for biometrics and emotion recognition (DEAP, MAHNOB, SEED, and LUMED-2) were selected for analysis. Feature extraction was performed using multiple strategies in the time, frequency, and time–frequency domains. The performance of various classifiers—support vector machine (SVM), random forest (RF), artificial neural networks (ANN), and k-nearest neighbors (K-NN)—was evaluated separately. Furthermore, stacking was used as a classifier fusion method. Explicit modeling of emotional states contributed to improving classifier performance. The best model based on classifier fusion achieved an accuracy of 95.73 ± 1.83%. These results indicate that incorporating information about emotional state into EEG-based biometric systems can contribute to the development of more robust and realistic identification solutions. Full article

Motor imagery (MI)-based brain–computer interfaces (BCIs) have led to great interest as a result of their potential use in neurorehabilitation, assistive robotics, and human–computer interaction. However, decoding electroencephalographic (EEG) signals with high accuracy continues to be a difficult task due to the weak signal-to-noise ratio, differences among subjects, and the complicated temporal–spectral–spatial neural dynamics. Deep learning methods recently developed, such as convolutional neural networks, recurrent architectures, graph neural networks, and adversarial transfer learning, have enhanced MI decoding performance, yet many models are still concentrating on a single representation domain or they need costly adaptation phases in terms of computation. To tackle these shortcomings, we present NeuroCrossNet, a unified tri-modal deep learning model that is able to learn the temporal, spectral, and spatial EEG features jointly for robust and calibration-free MI decoding. The suggested network combines a Temporal HyperMixer Block for capturing long-range temporal dependencies, a wavelet transformer for learning localized time–frequency representation, and a Graph Attention Network for EEG topology-aware spatial reasoning. Additionally, a Dynamic Residual Attention Gate (DRAG) has been developed to adaptively merge heterogeneous feature streams, and a compact subject-aware normalization (SAN) method enhances cross-subject generalization without the use of labeled target-domain calibration data. Our proposed model was tested following the rigorous leave-one-subject-out (LOSO) approach on BCI Competition IV-2a and High-Gamma datasets. NeuroCrossNet reached a classification accuracy of 91.30%, surpassing several strong benchmark methods, including CNN-LSTM, EEGNet, DeepConvNet, spectral CNN, and graph-based EEG decoding frameworks. Furthermore, a large number of ablation studies reveal that the integration of temporally, spectrally, and spatially complementary representations considerably boosts robustness and inter-subject consistency. Full article

Brain–computer interfaces (BCIs) based on motor imagery (MI) aim to convert electroencephalographic (EEG) activity into reliable device commands across users and recording setups. However, low signal-to-noise ratio and strong inter-subject variability still limit true “plug-and-play” deployment without lengthy calibration. To address these challenges, we propose a multi-branch convolutional long short-term memory (CNN-LSTM) architecture that jointly performs multi-scale temporal feature extraction and within-trial sequence modeling. The model employs four parallel 1D convolutional branches with distinct kernel sizes, each followed by an LSTM module and late fusion, combined with group normalization and supervision over sequences of sub-windows within each trial. We evaluate the approach on the EEG Motor Movement/Imagery (EEGMMI) dataset from PhysioNet under strictly subject-independent conditions, and on the ISLab-MI Dataset, a 32-channel wearable-EEG collection designed to assess cross-setup robustness. On EEGMMI, the network achieves up to 82.63% accuracy for binary left/right MI and 74.10% for a four-class task using 4 s trials under 5-fold cross-validation, outperforming an EEGNet-style baseline by 1–10% depending on class count and window length. Under a leave-one-subject-out protocol, the model attains 74.9% mean accuracy for a three-class MI task. Zero-shot transfer to ISLab-MI yields 64.60% and 63.02% accuracy in three- and four-class settings, respectively, while brief subject-specific fine-tuning using only 20% of each session improves performance to 81.38% and 73.48%. These findings show that combining multi-scale convolutional feature extraction with explicit sequence modeling and robust normalization yields accurate, data-efficient, and portable MI decoders suitable for practical BCI applications. Full article

Electroencephalographic (EEG) signals provide macroscopic observables of complex neural dynamics. We introduce a horizon-inspired framework in which measured EEG signals are modeled as projections of a complex wave-like representation constrained by an effective boundary analogous to an event horizon. In this formulation the signal amplitude obeys a renormalization-group scaling relation while EEG spectral entropy parameterizes the accessibility of observable modes. The resulting solutions generate oscillatory structures whose geometry and spectral signatures can be explored through signal analysis and sonification. This mapping between entropy-based neural observables and wave-like signal representations provides a physically motivated framework linking entropy measures, scale-dependent dynamics, and observable neural oscillations. The work is intentionally conceptual. It provides a falsifiable framework intended to stimulate future empirical investigations. Full article

Driving with assistive devices creates complex cognitive and emotional demands that require systematic investigation. This study uses a multivariate approach based on subjective and objective measures to evaluate mental workload (MWL), stress and emotional state during simulated driving with an assistive device. Thirty healthy adults ($42\pm 13$ years of age, 7 females) completed four driving tasks combining two levels of difficulty (Easy vs. Hard) and two steering tools (wheel vs. single-pin aid). Subjective measures from NASA Task Load Index and Self-Assessment Manikin were collected, as well as physiological parameters from electroencephalographic, electrocardiographic, and electrodermal activity signals. The results revealed that the assistive device significantly induced increases in perceived physical demand, frustration, loss of emotional control and stress, yet reducing intrinsic sympathetic response represented by electrodermal activity parameters. Multivariate analyses highlighted that combining different physiological predictors improved MWL estimation. This study marks an initial step towards understanding the impact of assistive devices on MWL and stress in post-acute individuals returning to driving. Full article

Background/Objectives: Genetic generalized epilepsies (GGE) often remit in childhood, yet a subset of adults remain pharmacoresistant with substantial morbidity. The genetic basis of adult pharmacoresistant GGE is poorly defined. This descriptive study used whole-genome sequencing (WGS) to identify recurrent coding variants and pathways associated with pharmacoresistant adult GGE. Methods: WGS was performed in ten racially diverse adults (mean age 37.2 years; range 20–52) with electroencephalographically confirmed, pharmacoresistant GGE (mean onset 13.7 years). Analysis prioritized variants present in at least 80% of participants and which were either (i) missense variants predicted deleterious with ANNOVAR or (ii) loss-of-function variants predicted high-impact from snpEff. Pathway enrichment and overlap with a commercial clinical epilepsy gene panel were assessed. Results: Filtering identified 133 unique, deleterious coding variants across 69 genes shared by at least eight participants. Four genes (APOL4, KMT2C, SON, VDR) overlapped a clinical epilepsy panel, supporting the capacity of WGS to recover clinically relevant loci. Prioritized loci implicated gastrointestinal and metabolic regulators (e.g., MUC6, PNLIPRP2), chemosensory receptors (OR10D3, OR8U1, TAS2R19), neuroimmune mediators (LILRA2, SIGLEC12, OAS2), and ion transporters (KCNJ12, P2RX5, RHBG), consistent with multifactorial mechanisms of pharmacoresistance. Conclusions: This exploratory WGS study focused exclusively on adults with pharmacoresistant GGE, revealing shared high-impact variants and convergent pathways spanning absorption/metabolism, vitamin D signaling, immunity, and ion transport. Findings broaden the genetic landscape of pharmacoresistant GGE while motivating validation in larger, multiethnic cohorts. Full article

This study presents a sensor-based condition monitoring framework for the diagnosis and prognosis of neurophysiological states using electroencephalographic (EEG) signals. Leveraging a comparative deep learning architecture, we evaluate a baseline Variational Autoencoder against a Transformer-based Autoencoder to model latent representations of EEG dynamics across three therapeutic phases: pre-intervention, during intervention, and post-intervention. The proposed methodology aligns with sensor-based fault diagnosis principles by treating deviations from stable neurophysiological states as diagnostic indicators and temporal phase transitions as markers of therapeutic stage progression. Using a dataset of 94 EEG sessions from six subjects with diverse neurological conditions, we demonstrate that the Transformer Autoencoder, through its self-attention mechanism, captures cross-band spectral relationships more effectively than the VAE, resulting in denser within-phase clusters and improved separation between therapeutic stages. Quantitative evaluation reveals small but statistically significant effects between pre- and during-intervention phases (${\eta }_{\mathrm{partial}}^2=0.0388$) and pre- and post-intervention phases (${\eta }_{\mathrm{partial}}^2=0.0470$), predominantly driven by delta, theta, beta, and gamma rhythms. These findings illustrate how sensor-based latent state monitoring can provide interpretable, data-driven insights for condition assessment and phase transition assessment between sessions in complex dynamic systems, with potential applicability beyond clinical domains to industrial condition monitoring and fault diagnosis tasks. The framework confirms that it offers qualitative indicators, rather than predictive clinical outputs. Full article

Background/Objectives: Alterations in excitation/inhibition (E/I) balance, involving both inhibitory and excitatory signaling, have been implicated in the pathophysiology of autism spectrum disorder (ASD). Volatile anesthetics, including sevoflurane, act on multiple molecular and network targets, and anesthetic sensitivity may therefore differ in ASD. This study investigated whether sevoflurane sensitivity is altered in BTBR T+Itpr3tf/J (BTBR) mice, a widely used mouse model of ASD. Methods: Sevoflurane sensitivity was compared between BTBR mice and C57BL/6J (B6) control mice using behavioral and electroencephalographic (EEG) analyses. The minimum alveolar concentration required to abolish nociceptive responses (MACsevo) and the sevoflurane concentration associated with recovery of the righting reflex (RRsevo) were measured. Dose-dependent EEG changes, including burst suppression and theta power distribution, were also evaluated. Results: MACsevo did not differ significantly between BTBR and B6 mice. However, RRsevo was significantly lower in BTBR mice (1.10 ± 0.10%) compared with B6 mice (1.65 ± 0.13%; p < 0.001). EEG analyses demonstrated that burst suppression occurred at lower sevoflurane concentrations in BTBR mice (2.0%) than in B6 mice (2.4%). In addition, topographical mapping revealed distinct theta power dynamics between the two strains during anesthesia. Conclusions: BTBR mice exhibit increased sensitivity to sevoflurane during emergence from anesthesia and show distinct EEG patterns compared with control mice. These findings suggest altered anesthetic responsiveness in a mouse model of ASD and support the possibility that network-level neurophysiological differences may influence anesthetic responses. Further studies are needed to clarify whether similar alterations are present across other ASD models and human ASD populations. Full article

The complexity of physiological signals provides insight into the maturation and functional organization of the developing brain. This study investigated age-related changes in electroencephalographic (EEG) signal complexity and their association with behavioral variability in 240 participants aged 6 to 29 years. EEG signals were recorded during the resting state, and Multiscale Entropy (MSE) was computed across 34 temporal scales, grouped into fine, medium, and coarse scales. Behavioral variability was assessed using measures from Oddball and Delayed Match-to-Sample tasks. Quadratic regression analyses characterized age-related changes in MSE across scalp regions, and Pearson correlations evaluated associations between age-adjusted residuals of MSE and behavioral variability. The results showed that MSE changed with age across temporal scales in all cortical regions. Developmentally, MSE showed a significant age-related increase at fine scales across the entire scalp, region-specific decreases at medium scales, and a generalized decrease at coarse scales. Behavioral variability decreased with age across both tasks. Notably, fine-scale age residual MSE in central and posterior regions was negatively correlated with the coefficient of variation in the Oddball task, indicating that higher neural complexity supports more stable performance. These findings suggest scale- and region-specific age-related changes in neural complexity and suggest that fine-scale MSE captures aspects of brain maturation related to behavioral stability beyond traditional variability measures. Full article