Search Results (4,801)

Deep learning holds considerable promise for electroencephalography (EEG) analysis but faces challenges due to scarce and noisy EEG data, and the limited generality of existing data augmentation techniques. To address these issues, we propose an end-to-end EEG augmentation framework with an adaptive mechanism. This approach utilizes contrastive learning to mitigate representational distortions caused by augmentation, thereby strengthening the encoder’s feature learning. A selective augmentation strategy is further incorporated to dynamically determine optimal augmentation combinations based on performance. We also introduce NeuroBrain, a novel neural architecture specifically designed for auditory EEG decoding. It effectively captures both local and global dependencies within EEG signals. Comprehensive evaluations on the SparrKULee and WithMe datasets confirm the superiority of our proposed framework and architecture, demonstrating a 29.42% performance gain over HappyQuokka and a 5.45% accuracy improvement compared to EEGNet. These results validate our method’s efficacy in tackling key challenges in EEG analysis and advancing the state of the art. Full article

Industry 5.0 challenges higher education to adopt human-centred and sustainable uses of artificial intelligence, yet many current deployments still treat generative AI as a stand-alone tool, neurophysiological sensing as largely laboratory-bound, and governance as an external add-on rather than a design constraint. This article introduces Nested Learning as a neuro-adaptive ecosystem design in which generative-AI agents, IoT infrastructures and multimodal deep learning orchestrate instructional support while preserving student agency and a “pedagogy of hope”. We report an exploratory two-phase mixed-methods study as an initial empirical illustration. First, a neuro-experimental calibration with 18 undergraduate students used mobile EEG while they interacted with ChatGPT in problem-solving tasks structured as challenge–support–reflection micro-cycles. Second, a field implementation at a university in Madrid involved 380 participants (300 students and 80 lecturers), embedding the Nested Learning ecosystem into regular courses. Data sources included EEG (P300) signals, interaction logs, self-report measures of engagement, self-regulated learning and cognitive safety (with strong internal consistency; $\alpha /\omega \ge 0.82$), and open-ended responses capturing emotional experience and ethical concerns. In Phase 1, P300 dynamics aligned with key instructional micro-events, providing feasibility evidence that low-cost neuro-adaptive pipelines can be sensitive to pedagogical flow in ecologically relevant tasks. In Phase 2, participants reported high levels of perceived nested support and cognitive safety, and observed associations between perceived Nested Learning, perceived neuro-adaptive adjustments, engagement and self-regulation were moderate to strong ($r=0.41$–$0.63$, $p<0.001$). Qualitative data converged on themes of clarity, adaptive support and non-punitive error culture, alongside recurring concerns about privacy and cognitive sovereignty. We argue that, under robust ethical, data-protection and sustainability-by-design constraints, Nested Learning can strengthen academic resilience, learner autonomy and human-centred uses of AI in higher education. Full article

Recent advances in automated learning techniques have enhanced the analysis of biomedical signals for detecting sleep stages and related health abnormalities. However, many existing models face challenges with imbalanced datasets and the dynamic nature of evolving sleep states. In this study, we present a robust algorithm for classifying sleep states using electroencephalogram (EEG) data collected from 33 healthy participants. We extracted dynamic, brain-inspired features, such as microstates and Lempel–Ziv complexity, which replicate intrinsic neural processing patterns and reflect temporal changes in brain activity during sleep. An optimal feature set was identified based on significant spectral ranges and classification performance. The classifier was developed using a convolutional neural network (CNN) combined with gated recurrent units (GRUs) within a reinforcement learning framework, which models adaptive decision-making processes similar to those in biological neural systems. Our proposed biomimetic framework illustrates that a multivariate feature set provides strong discriminative power for sleep state classification. Benchmark comparisons with established approaches revealed a classification accuracy of 98% using the optimized feature set, with the framework utilizing fewer EEG channels and reducing processing time, underscoring its potential for real-time deployment. These findings indicate that applying biomimetic principles in feature extraction and model design can improve automated sleep monitoring and facilitate the development of novel therapeutic and diagnostic tools for sleep-related disorders. Full article

We investigate whether representation-level, semantic diagnostics expose structure in electroencephalography (EEG) beyond conventional accuracy when contrasting perception and imagination and relating outcomes to self-reported imagery ability. Using a task-independent encoder that preserves scalp topology and temporal dependencies, we learn semantic features from multi-subject, multi-modal EEG (pictorial, orthographic, auditory) and evaluate subject-independent decoding with lightweight heads, achieving state-of-the-art or better accuracy with low variance across subjects. To probe the latent space directly, we introduce threshold-resolved correlation pruning and derive the Semantic Sensitivity Index (SSI) and cross-modal overlap (CMO). While correlations between Vividness of Visual Imagery Questionnaire (VVIQ)/Bucknell Auditory Imagery Scale (BAIS) and leave-one-subject-out (LOSO) accuracy are small and imprecise at n = 12, the semantic diagnostics reveal interpretable geometry: for several subjects, imagination retains a more compact, non-redundant latent subset than perception (positive SSI), and a substantial cross-modal core emerges (CMO ≈ 0.5–0.8). These effects suggest that accuracy alone under-reports cognitive organization in the learned space and that semantic compactness and redundancy patterns capture person-specific phase preferences. Given the small cohort and the subjectivity of questionnaires, the findings argue for semantic, representation-aware evaluation as a necessary complement to accuracy in EEG-based decoding and trait linkage. Full article

Background/Objectives: Post-traumatic epileptogenesis is a frequent and clinically relevant consequence of traumatic brain injury (TBI), often contributing to worsened neurological and functional outcomes. In patients experiencing early post-injury seizures, rehabilitative strategies that support recovery while considering increased epileptogenic risk are needed. This case study explores the potential benefits of combining neurofeedback (NFB) with motor therapy on cognitive and motor recovery. Methods: A patient hospitalized for severe TBI who experienced an acute symptomatic seizure in the early post-injury phase underwent baseline quantitative EEG (qEEG), neuromotor, functional, and neuropsychological assessments. The patient then completed a three-week rehabilitation program (five days/week) including 30 sensorimotor rhythm (SMR) NFB sessions (35 min each) combined with daily one-hour motor therapy. qEEG and clinical assessments were repeated post-intervention and at 6-month follow-up. Results: Post-intervention qEEG showed significant reductions in Delta and Theta power, reflecting decreased cortical slowing and enhanced neural activation. Relative power analysis indicated reduced Theta activity and Alpha normalization, suggesting improved cortical stability. Increases were observed in Beta and High-beta activity, alongside significant reductions in the Theta/Beta ratio, consistent with improved attentional regulation. Neuropsychological outcomes revealed reliable improvements in global cognition, memory, and visuospatial abilities, mostly maintained or enhanced at follow-up. Depressive and anxiety symptoms decreased markedly. Motor and functional assessments demonstrated meaningful improvements in motor performance, coordination, and functional independence. Conclusions: Findings suggest that integrating NFB with motor therapy may support recovery processes and be associated with sustained neuroplastic changes in the early post-injury phase after TBI, a condition associated with elevated risk for post-traumatic epilepsy. Full article

Background and Objective: The accurate detection of sleep stages and disorders in older adults is essential for the effective diagnosis and treatment of sleep disorders affecting millions worldwide. Although Polysomnography (PSG) remains the primary method for monitoring sleep in medical settings, it is costly and time-consuming. Recent automated models have not fully explored and effectively fused the sleep features that are essential to identify sleep stages and disorders. This study proposes a novel automated model for detecting sleep stages and disorders in older adults by analyzing PSG recordings. PSG data include multiple channels, and the use of our proposed advanced methods reveals the potential correlations and complementary features across EEG, EOG, and EMG signals. Methods: In this study, we employed three novel advanced architectures, (1) CNNs, (2) CNNs with Bi-LSTM, and (3) CNNs with a transformer encoder, for the automatic classification of sleep stages and disorders using multichannel PSG data. The CNN extracts local features from RGB spectrogram images of EEG, EOG, and EMG signals individually, followed by an appropriate column-wise feature fusion block. The Bi-LSTM and transformer encoder are then used to learn and capture intra-epoch feature transition rules and dependencies. A residual connection is also applied to preserve the characteristics of the original joint feature maps and prevent gradient vanishing. Results: The experimental results in the CAP sleep database demonstrated that our proposed CNN with transformer encoder method outperformed standalone CNN, CNN with Bi-LSTM, and other advanced state-of-the-art methods in sleep stages and disorders classification. It achieves an accuracy of 95.2%, Cohen’s kappa of 93.6%, MF1 of 91.3%, and MGm of 95% for sleep staging, and an accuracy of 99.3%, Cohen’s kappa of 99.1%, MF1 of 99.2%, and MGm of 99.6% for disorder detection. Our model also achieves superior performance to other state-of-the-art approaches in the classification of N1, a stage known for its classification difficulty. Conclusions: To the best of our knowledge, we are the first group going beyond the standard to investigate and innovate a model architecture which is accurate and robust for classifying sleep stages and disorders in the elderly for both patient and non-patient subjects. Given its high performance, our method has the potential to be integrated and deployed into clinical routine care settings. Full article

Real-time emotion monitoring is increasingly relevant in healthcare, automotive, and workplace applications, where adaptive systems can enhance user experience and well-being. This study investigates the feasibility of classifying emotions along the valence–arousal dimensions of the Circumplex Model of Affect using EEG signals acquired from a single mastoid channel positioned near the ear. Twenty-four participants viewed emotion-eliciting videos and self-reported their affective states using the Self-Assessment Manikin. EEG data were recorded with an OpenBCI Cyton board and both spectral and temporal features (including power in multiple frequency bands and entropy-based complexity measures) were extracted from the single ear-channel. A dual analytical framework was adopted: classical statistical analyses (ANOVA, Mann–Whitney U) and artificial neural networks combined with explainable AI methods (Gradient × Input, Integrated Gradients) were used to identify features associated with valence and arousal. Results confirmed the physiological validity of single-channel ear-EEG, and showed that absolute $\beta$- and $\gamma$-band power, spectral ratios, and entropy-based metrics consistently contributed to emotion classification. Overall, the findings demonstrate that reliable and interpretable affective information can be extracted from minimal EEG configurations, supporting their potential for wearable, real-world emotion monitoring. Nonetheless, practical considerations—such as long-term comfort, stability, and wearability of ear-EEG devices—remain important challenges and motivate future research on sustained use in naturalistic environments. Full article

Due to the damage happening in the nervous system, neuropathic pain occurs and it affects the quality of life of the patient to a great extent. Therefore, some clinical evaluations are required to assess the diagnostic outcomes precisely. A lot of information about the activities of the brain is provided by Electroencephalography (EEG) signals and neuropathic pain can be assessed and classified with the aid of EEG and machine learning. In this work, two approaches are proposed in terms of efficient blended models for the classification of neuropathic pain through EEG signals. In the first blended model, once the features are extracted using Discrete Wavelet Transform (DWT), statistical features, and Fuzzy C-Means (FCM) clustering techniques, the features are selected using Grey Wolf Optimization (GWO), Feature Correlation Clustering Technique (FCCT), F-test, and Bayesian Optimization Algorithm (BOA) and it is classified with the help of three hybrid classification models like Spider Monkey Optimization-based Gradient Boosting Machine (SMO-GBM) classifier, hybrid deep kernel learning with Support Vector Machine (DKL-SVM) classifier, and CatBoost classifier. In the second blended model, once the features are extracted, the features are selected using Hybrid Feature Selection—Majority Voting System (HFS-MVS), Hybrid Salp Swarm Optimization—Particle Swarm Optimization (SSO-PSO), Pearson Correlation Coefficient (PCC), and Mutual Information (MI) and it is classified with the help of three hybrid classification models like Partial Least Squares (PLS) variant classification models combined with Kernel-based SVM, ensemble classification model with soft voting strategy, and Extreme Gradient Boosting (XGBoost) classifier. The proposed blended models are evaluated on a publicly available dataset and the best results are shown when the FCM features are selected with SSO-PSO feature selection technique and classified with Polynomial Kernel-based PLS-SVM Classifier, reporting a high classification accuracy of 92.68% in this work. Full article

Scalp high-frequency oscillations (HFOs) are promising noninvasive biomarkers of epileptogenicity, but their phenotypic diversity and clinical relevance in absence epilepsy (AE) remain unclear. This study aimed to classify scalp HFOs in AE using k-means clustering based on multiple morphological characteristics, and to evaluate their distribution across electroencephalogram (EEG) epochs and seizure control statuses. We analyzed scalp EEG recordings from 14 children and adolescents with AE. After excluding outliers, 163 scalp HFOs were characterized by average frequency, duration, amplitude, and number of cycles. Amplitude and cycle count were log-transformed prior to clustering, and k-means clustering was applied to identify distinct HFO phenotypes. Three clusters were identified: Cluster 1 (short duration, low amplitude), Cluster 2 (low frequency), and Cluster 3 (long duration, high cycle count). Cluster 2 and Cluster 3 were significant predictors of ictal HFOs in active AE, with odds ratios (ORs) of 0.33 (95% confidence interval [CI]: 0.14–0.74) and 5.00 (CI: 2.02–17.73), respectively. Cluster 2 also predicted interictal HFOs in active AE (OR [95% CI] = 2.71 [1.23–5.67]). These findings support the utility of scalp HFO phenotypes as EEG-based biomarkers for seizure detection and disease monitoring, potentially guiding treatment strategies in pediatric AE. Full article

Background/Objectives: Biopsychosocial pain neuroscience education (PNE) has recently gained attention in preparing patients for surgery. PNE is expected to influence pain coping strategies and descending nociceptive inhibition. The goal of this study was to compare cortical evoked responses during experimental pain processing using a conditioned pain modulation (CPM) paradigm between patients receiving perioperative PNE (PPNE) or perioperative biomedical back school education (PBSE). Methods: This predefined EEG subgroup analysis included only participants with complete EEG recordings at baseline and 6 weeks. Of these, twenty-three patients with low back-related leg pain, scheduled for lumbar spine surgery, were randomized to either two sessions of PPNE or two sessions of PBSE. All patients were stimulated electrically at the median nerve of the symptomatic side and the sural nerve of the symptomatic and non-symptomatic side before and 6 weeks after the educational sessions, while evoked potentials were recorded by electroencephalography (EEG). Subsequently, this protocol was repeated during the application of the CPM paradigm by immersing the hand contralateral to the symptomatic side into cold water. Results: A significant decrease in the amplitude of the waveforms during CPM was found compared to the waveforms before CPM at the non-symptomatic sural nerve. No significant differences were found at the other test locations. For the waveforms of the CPM effect (subtracted waveforms), no significant treatment effects were revealed between the PPNE and PBSE groups. Conclusions: These exploratory findings suggest that PPNE was not associated with differential modulation of EEG evoked potentials during CPM compared with PBSE at 6 weeks post-surgery. Full article

Background: Aging is traditionally characterized by progressive structural and cognitive decline; however, increasing evidence shows that the aging brain retains a remarkable capacity for reorganization. This adaptive neuroplasticity supports cognitive resilience—defined as the ability to maintain efficient cognitive performance despite age-related neural vulnerability. Objective: To synthesize current molecular, cellular, neuroimaging, and electrophysiological neuromarkers that characterize adaptive neuroplasticity and to examine how these mechanisms contribute to cognitive resilience across aging. Methods: This narrative review integrates findings from molecular neuroscience, multimodal neuroimaging (fMRI, DTI, PET), electrophysiology (EEG, MEG, TMS), and behavioral research to outline multiscale biomarkers associated with compensatory and efficient neural reorganization in older adults. Results: Adaptive neuroplasticity emerges from the coordinated interaction of neurotrophic signaling (BDNF, CREB, IGF-1), glial modulation (astrocytic lactate metabolism, regulated microglial activity), synaptic remodeling, and neurovascular support (VEGF, nitric oxide). Multimodal neuromarkers—including preserved frontoparietal connectivity, DMN–FPCN coupling, synaptic density (SV2A-PET), theta–gamma coherence, and LTP-like excitability—consistently correlate with resilience in executive functions, memory, and processing speed. Behavioral enrichment, physical activity, and cognitive training further enhance these biomarkers, creating a bidirectional loop between experience and neural adaptability. Conclusions: Adaptive neuroplasticity represents a fundamental mechanism through which older adults maintain cognitive function despite biological aging. Integrating molecular, imaging, electrophysiological, and behavioral neuromarkers provides a comprehensive framework to identify resilience trajectories and to guide personalized interventions aimed at preserving cognition. Understanding these multilevel adaptive mechanisms reframes aging not as passive decline but as a dynamic continuum of biological compensation and cognitive preservation. Full article

Background: Automatic detection of abnormal electroencephalogram (EEG) signals is essential for supporting clinical screening and reducing human error in EEG interpretation. Although deep learning architectures such as CNN–LSTM have shown promising performance in EEG classification, challenges related to feature variability, non-stationarity, and sensitivity to pathological patterns remain. Our previous work with windowing-based CNN-LSTM architecture achieved strong performance but it did not achieve sufficient sensitivity for reliable clinical application. Methods: To overcome these limitations, we propose an enhanced voting-based ensemble framework that combines five CNN-LSTM base classifiers with a Random Forest (RF) meta-classifier, evaluated using 10-fold cross-validation. Results: The proposed ensemble model achieved a sensitivity of 92.86%, a specificity of 72.3%, and an overall accuracy of 83%, demonstrating competitive and clinically meaningful sensitivity for abnormal EEG detection under the adopted evaluation protocol. Conclusions: These findings demonstrate that integrating multi-model feature extraction with an RF-based voting ensemble improves diagnostic reliability, reduces false negatives, and supports early and accurate detection of brain disorders. This framework not only surpasses existing approaches but also provides a flexible foundation for future advancements in clinical decision support systems. Full article

REM sleep behavioral disorder (RBD) is of increasing interest in Parkinson’s disease (PD). Previous studies exploring the association between REM sleep without atonia (RWA) and clinical PD features or other objective sleep metrics are scarce and have used PSG findings. A mobile electroencephalography (EEG)/electrooculography (EOG) recording system with two channels can objectively measure sleep parameters, including RWA, during natural sleep at home. We investigated whether RWA measured on a portable recording device at home could be associated with clinical PD features or other sleep metrics using baseline data from the ZEAL study. Differences between patients with and without RWA was analyzed using ANCOVA test. REM sleep length was significantly longer in patients with RWA than in those without RWA. A multivariate comparison using ANCOVA showed a significant difference in log-transformed REM sleep duration of patients with RWA after adjustment for potential confounders (adjusted mean difference of 1.203; 95% confidence interval 0.468 to 1.937; p = 0.003). The strength of this study was that it evaluated the association between RWA during natural sleep at home and clinical variables as well as other sleep metrics. The major result was that patients with and without RWA did not differ in their clinical variables, and there was no relation between RWA and objective sleep metrics other than REM sleep. The duration of REM sleep may be associated with RWA during natural sleep at home. Full article

Background: Emotion recognition using electroencephalography (EEG) offers a non-invasive means of measuring brain responses to affective stimuli. However, since EEG signals can vary significantly between subjects, developing a deep learning model capable of accurately predicting emotions is challenging. Methods: To address that challenge, this study proposes a deep learning approach that fuses EEG features with demographic information, specifically age, sex, and nationality, using an attention-based mechanism that learns to weigh each modality during classification. The method was evaluated using three benchmark datasets: SEED, SEED-FRA, and SEED-GER, which include EEG recordings of 31 subjects of different demographic backgrounds. Results: We compared a baseline model trained solely on the EEG-derived features against an extended model that fused the subjects’ EEG and demographic information. Including demographic information improved the performance, achieving 80.2%, 80.5%, and 88.8% for negative, neutral, and positive classes. The attention weights also revealed different contributions of EEG and demographic inputs, suggesting that the model learns to adapt based on subjects’ demographic information. Conclusions: These findings support integrating demographic data to enhance the performance and fairness of subject-independent EEG-based emotion recognition models. Full article

Driver fatigue detection based on electroencephalography (EEG) signals has gained increasing attention for enhancing road safety. However, existing deep learning models often treat EEG data as generic time-series inputs, neglecting the inherent hierarchical and spatial–temporal structure of brain activity, which limits their interpretability and generalization. To address this, we propose a novel Residual Polynomial Network (RPN) that explicitly models the positive and negative activation patterns in EEG signals through a polarity-aware architecture. The RPN integrates polarity decomposition, residual learning, and hierarchical feature fusion to capture discriminative neurophysiological dynamics while maintaining model transparency. Extensive experiments are conducted on a real-world driving fatigue dataset using a subject-wise 10-fold cross-validation protocol. Results show that the proposed RPN achieves an average classification accuracy of 97.65%, outperforming conventional machine learning and deep learning baselines including SVM, KNN, DT, and LSTM. Ablation studies confirm the effectiveness of each component, and Sankey diagram analysis provides interpretable insights into feature-to-class mappings. This work not only advances the state of the art in EEG-based fatigue detection but also offers a more transparent and physiologically plausible deep learning framework for brain signal analysis. Full article