Search Results (656)

Aim: To investigate the impact of pain on some electroencephalographic (EEG) features at term equivalent age (TEA) and, second, to assess if the proposed EEG analysis may be predictive of the neurodevelopmental outcome at 24 months corrected age. Methodology: Infants born <32 weeks of gestational age, without major brain injury, were studied with an 8-channel EEG recording at TEA. The number of skin-breaking procedures from birth to the EEG recording was collected, as well as opioid administration. The following EEG-based indexes were investigated: Brain Simmetry Index (BSI) and Circular Omega Complexity (COC). Multivariate statistical analysis was performed. Results: Seventy-seven preterm newborns were enrolled. The multivariate models showed that higher pain exposure resulted in higher BSI, lower COC μ (mean), and lower COC values related to δ waves (all p < 0.05). Fentanyl was associated with increased BSI values related to α and β waves (all p < 0.05). Morphine showed a positive effect on BSI and a negative effect on OC μ and COC on all frequency bands (all p < 0.05). COC related to δ waves was positively associated with cognitive outcomes (p = 0.034). Conclusion: Pain and opioids might impact brain dynamics in preterm infants. Quantitative multivariate EEG indexes may be helpful to characterize the neurodevelopmental outcomes. Full article

Traditional lie detection technologies, such as the polygraph and event-related potential (ERP)-based approaches, often face limitations in real-world applicability due to their sensitivity to psychological states and the complex, nonlinear nature of electroencephalogram (EEG) signals. In this study, we propose SparseMambaNet, a novel neural architecture that integrates the recently developed Bi-Mamba model with a Sparsely Activated Mixture of Experts (MoE) structure to effectively model the intricate spatio-temporal dynamics of EEG data. By leveraging the near-linear computational complexity of Mamba and the bidirectional contextual modeling of Bi-Mamba, the proposed framework efficiently processes long EEG sequences while maximizing representational power through the selective activation of expert networks tailored to diverse input characteristics. Experiments were conducted with 46 healthy subjects using a simulated criminal scenario based on the Comparison Question Technique (CQT) with monetary incentives to induce realistic psychological tension. We extracted nine statistical and neural complexity features, including Hjorth parameters, Sample Entropy, and Spectral Entropy. The results demonstrated that Sample entropy and Hjorth parameters achieved exceptional classification performance, recording F1 scores of 0.9963 and 0.9935, respectively. Statistical analyses further revealed that the post-response “answer” interval provided significantly higher discriminative power compared to the “question” interval. Furthermore, channel-level analysis identified core neural loci for deception in the frontal and fronto-central regions, specifically at channels E54 and E63. These findings suggest that SparseMambaNet offers a highly efficient and precise solution for EEG-based lie detection, providing a robust foundation for the development of personalized brain–computer interface (BCI) systems in forensic and clinical settings. Full article

Background/Objectives: Accurate sleep stage classification is essential for evaluating sleep quality and diagnosing sleep disorders. Despite recent advances in deep learning, existing models inadequately represent complex brain dynamics, particularly the time-lag effects inherent in neural signal propagation and regional variations in cortical activation patterns. Methods: We propose the MFST-GCN, a graph-based deep learning framework that models these neurobiological phenomena through three complementary modules. The Dynamic Dual-Scale Functional Connectivity Modeling (DDFCM) module constructs time-varying adjacency matrices using Pearson correlation across 1 s and 5 s windows, capturing both transient signal transmission and sustained connectivity states. This dual-scale approach reflects the biological reality that neural information propagates with measurable delays across brain regions. The Multi-Scale Morphological Feature Extraction Network (MMFEN) employs parallel convolutional branches with varying kernel sizes to extract frequency-specific features corresponding to different EEG rhythms, addressing regional heterogeneity in neural activation. The Adaptive Spatio-Temporal Graph Convolutional Network (ASTGCN) integrates spatial and temporal features through Chebyshev graph convolutions with attention mechanisms, encoding evolving functional dependencies across sleep stages. Results: Evaluation on ISRUC-S1 and ISRUC-S3 datasets demonstrates F1-scores of 0.823 and 0.835, respectively, outperforming state-of-the-art methods. Conclusions: Ablation studies confirm that explicit time-lag modeling contributes substantially to performance gains, particularly in discriminating transitional sleep stages. Full article

Biallelic variants in NDUFS6, encoding an accessory subunit of mitochondrial complex I, were initially associated with lethal neonatal mitochondrial encephalopathy and Leigh syndrome. Recent studies have demonstrated that NDUFS6 variants can also cause childhood- or adolescent-onset axonal neuropathy and Charcot–Marie–Tooth (CMT)-like phenotypes, indicating marked clinical heterogeneity. Here, we report a patient with a novel homozygous truncating NDUFS6 variant presenting with a neuropathy-predominant phenotype accompanied by epilepsy, in the absence of neonatal metabolic decompensation. The patient presented with childhood-onset progressive gait abnormality, pes cavus deformity, distal weakness requiring Achilles tendon-release surgery, pyramidal signs, urinary incontinence, and focal epileptiform EEG findings. Brain MRI showed bilateral lenticular nucleus abnormalities. Whole-exome sequencing identified a novel homozygous NDUFS6 nonsense variant (c.130C>T, p.Gln44*). While neuropathy has previously been reported primarily in association with the recurrent splice-site variant c.309+5G>A, our findings demonstrate that truncating NDUFS6 mutations can also underlie a neuropathy-predominant phenotype. Together with previously published cases, our findings support a phenotypic heterogeneity ranging from lethal encephalopathy to neuropathy and reinforce the role of NDUFS6 as a disease-causing gene for inherited peripheral neuropathy. These data support inclusion of NDUFS6 among established neuropathy and Charcot–Marie–Tooth genes. Full article

Smart devices and multimodal biosignal systems, including electroencephalography (EEG/MEG), ECG-derived heart rate variability (HRV), and electromyography (EMG), increasingly supported by artificial intelligence (AI), are being explored to improve the assessment and longitudinal monitoring of mental health conditions. Despite rapid growth, the available evidence remains heterogeneous, and clinical translation is limited by variability in acquisition protocols, analytical pipelines, and validation quality. This systematic review synthesizes current applications, signal-processing approaches, and methodological limitations of biosignal-based smart systems for mental health monitoring. Methods: A PRISMA 2020-guided systematic review was conducted across PubMed/MEDLINE, Scopus, the Web of Science Core Collection, IEEE Xplore, and the ACM Digital Library for studies published between 2013 and 2026. Eligible records reported human applications of wearable/smart devices or multimodal biosignals (e.g., EEG/MEG, ECG/HRV, EMG, EDA/GSR, and sleep/activity) for the detection, monitoring, or management of mental health outcomes. The reviewed literature after predefined inclusion/exclusion criteria clustered into six themes: depression detection and monitoring (37%), stress/anxiety management (18%), post-traumatic stress disorder (PTSD)/trauma (5%), technological innovations for monitoring (25%), brain-state-dependent stimulation/interventions (3%), and socioeconomic context (7%). Across modalities, common analytical pipelines included artifact suppression, feature extraction (time/frequency/nonlinear indices such as entropy and complexity), and machine learning/deep learning models (e.g., SVM, random forests, CNNs, and transformers) for classification or prediction. However, 67% of studies involved sample sizes below 100 participants, limited ecological validity, and lacked external validation; heterogeneity in protocols and outcomes constrained comparability. Conclusions: Overall, multimodal systems demonstrate strong potential to augment conventional mental health assessment, particularly via wearable cardiac metrics and passive sensing approaches, but current evidence is dominated by proof-of-concept studies. Future work should prioritize standardized reporting, rigorous validation in diverse real-world cohorts, transparent model evaluations, and ethics-by-design principles (privacy, fairness, and clinical governance) to support translation into practice. Full article

Background/Objectives: Smith–Magenis syndrome (SMS) is a rare neurogenetic disorder caused by a microdeletion in chromosome region 17p11.2 or by pathogenic variants in the RAI1 gene. The syndrome is characterized by a distinctive neurobehavioral profile, including cognitive deficits, sleep disturbances, self-injurious behavior, and typical dysmorphic features. A characteristic diagnostic hallmark is paradoxical melatonin secretion, with increased daytime levels instead of the normal nocturnal peak. This article aims to summarize current knowledge on the etiology, diagnostics, EEG findings, therapy, and prognosis of SMS from a neuropediatric perspective. Methods: A narrative review of the literature on Smith–Magenis syndrome was conducted, focusing on genetic background, clinical features, diagnostic approaches, EEG characteristics, therapeutic strategies, and prognosis. In addition, a detailed clinical case of a 16-year-old female patient with SMS is presented. Results: The reviewed data confirm that SMS is associated with characteristic neurobehavioral abnormalities and sleep–wake rhythm disturbances. EEG findings may include epileptiform activity without overt epilepsy. In the presented case, “Rolandic-type” spike–sharp wave complexes were observed on EEG and are interpreted as an expression of congenital disturbances in brain maturation processes. Therapeutic recommendations addressing behavioral symptoms and sleep regulation are discussed. Conclusions: Smith–Magenis syndrome represents a complex neurodevelopmental disorder with distinctive clinical, neurophysiological, and genetic features. Early recognition of characteristic signs, including sleep disturbances and EEG abnormalities, is essential for appropriate management. A multidisciplinary, individualized therapeutic approach may improve quality of life and long-term outcomes. Full article

Gaming Disorder (GD) is becoming more widely acknowledged as a behavioral addiction characterized by impaired control and functional impairment. While resting-state impairments are well understood, the neurophysiological dynamics during active gameplay remain underexplored. This study identified task-based occipital EEG biomarkers of GD and assessed their diagnostic utility. Occipital EEG (O1/O2) data from 30 participants (15 with GD, 15 controls) collected during active mobile gaming were used in this study. Spectral, temporal, and nonlinear complexity features were extracted. Feature relevance was ranked using Random Forest, and classification performance was evaluated using Leave-One-Subject-Out (LOSO) cross-validation to ensure subject-independent generalization across five models (Random Forest, KNN, SVM, Decision Tree, ANN). The GD group exhibited paradoxical “spectral slowing” during gameplay, characterized by increased Delta/Theta power and decreased Beta activity relative to controls. Beta variability was identified as a key biomarker, reflecting altered attentional stability, while elevated Alpha power suggested potential neural habituation or sensory gating. The Decision Tree classifier emerged as the most robust model, achieving a classification accuracy of 80.0%. Results suggest distinct neurophysiological patterns in GD, where increased low-frequency power may reflect automatized processing or “Neural Efficiency” despite active task engagement. These findings highlight the potential of occipital biomarkers as accessible and objective screening metrics for Gaming Disorder. Full article

Brain electrical activity, as recorded through electroencephalography (EEG), displays scale-free temporal fluctuations indicative of fractal behavior and complex dynamics. This study explores the use of the Higuchi Fractal Dimension (HFD) as a proxy of two complementary aspects of EEG temporal organization: local signal irregularity, interpreted within a Kolmogorov-type framework, and persistence related to temporal structure, associated with statistical complexity. The latter can be used to evidence persistence in the EEG signal, serving as an alternative to previously used approaches for estimating the Hurst exponent. Thirty-eight healthy participants underwent resting-state EEG recordings in open- and closed-eyes conditions. HFD was computed for the original signals to assess Kolmogorov complexity and for the signals’ cumulative envelopes to evaluate statistical complexity and, consequently, persistence. The results confirmed that HFD values align with theoretical expectations: higher for random noise in the Kolmogorov model (~2) and lower in the statistical model (~1.5). EEG data showed condition-dependent and topographically specific variations in HFD, with parieto-occipital regions exhibiting greater complexity and persistence. The HFD values in the statistical model fall within the 1–1.5 range, indicating long-term correlation. These findings support HFD as a reliable tool for assessing both the local roughness and global temporal structure of brain activity, with implications for physiological modeling and clinical applications. Full article

In electroencephalogram (EEG)-based emotion recognition tasks, existing end-to-end approaches predominantly rely on real-valued neural networks, which mainly operate in the time–amplitude domain. However, EEG signals are a type of wave, intrinsically including frequency, phase, and amplitude characteristics. Real-valued architectures may struggle to capture amplitude–phase coupling and spectral structures that are crucial for emotion decoding. To the best of our knowledge, this work is the first to introduce complex-valued neural networks for EEG-based emotion recognition, upon which we design a new end-to-end architecture named Complex-valued EEGNet (CV-EEGNet). Beginning with raw EEG signals, CV-EEGNet transforms them into complex-valued spectra via the Fast Fourier Transform, then sequentially applies complex-valued spectral, spatial, and depthwise-separable convolution modules to extract frequency structures, spatial topologies, and high-level semantic representations while preserving amplitude–phase relationships. Finally, a complex-valued, fully connected classifier generates complex logits, and the final emotion predictions are derived from their magnitudes. Experiments on the SEED (three-class) and SEED-IV (four-class) datasets validate the effectiveness of the proposed method, with t-SNE visualizations further confirming the discriminability of the learned representations. These results show the potential of complex-valued neural networks for raw-signal EEG emotion recognition. Full article

Background: Major Depressive Disorder (MDD) is a pervasive psychiatric condition. Electroencephalography (EEG) is employed to detect MDD-specific neural patterns because it is non-invasive and temporally precise. However, manual interpretation of EEG signals is labor-intensive and subjective. This problem was addressed by proposing machine learning (ML) and deep learning (DL) methods. Although DL methods are promising for MDD detection, they face limitations, including high model complexity, overfitting due to subject-specific noise, excessive channel requirements, and limited interpretability. Methods: To address these challenges, we propose MS-MDDNet, a new lightweight CNN model specifically designed for EEG-based MDD detection, along with an ensemble-like method built on it. The architecture of MS-MDDNet incorporates spatial, temporal, and depth-wise separable convolutions, along with average pooling, to enhance discriminative feature extraction while maintaining computational efficiency with a small number of learnable parameters. Results: The method was evaluated using 10-fold Cross-Subjects Cross-Validation (CS-CV), which mitigates the risks of overfitting associated with subject-specific noise, thereby contributing to generalization robustness. Across three public datasets, the proposed method achieved performance comparable to state-of-the-art approaches while maintaining lower computational complexity. It achieved a 9% improvement on the MODMA dataset, with an accuracy of 99.33%, whereas on MUMTAZ and PRED + CT it achieved accuracies of 98.59% and 96.61%, respectively. Conclusions: The predictions of the proposed method are interpretable, with interpretability achieved through correlation analysis between gamma energy and learned features. This makes it a valuable tool for assisting clinicians and individuals in diagnosing MDD with confidence, thereby enhancing transparency in decision-making and promoting clinical credibility. Full article

Understanding consciousness requires bridging theoretical models and clinically measurable brain dynamics. This review integrates three complementary frameworks that converge on a dynamical view of conscious processing: continuous formulations of Integrated Information Theory (IIT), attractor-landscape modeling of brain-state transitions, and perturbational complexity metrics from transcranial magnetic stimulation combined with electroencephalography (TMS-EEG). Continuous-time IIT formalizes how integrated information evolves across temporal hierarchies, while dynamical-systems approaches show that consciousness emerges near criticality, where metastable attractors enable flexible transitions between partially synchronized states. Perturbational-complexity indices capture these properties empirically, quantifying the brain’s capacity for integration and differentiation even without behavioral responsiveness. Across anesthesia, disorders of consciousness, epilepsy, and neurodegeneration, TMS-EEG biomarkers reveal reduced complexity and altered synchronization consistent with structural and functional disconnection. Integrating multimodal data—diffusion MRI, fMRI, EEG, and causal perturbations—is consistent with individualized modeling of consciousness-related dynamics. Standardized protocols, mechanistically interpretable machine learning, and longitudinal validation are essential for clinical translation. By uniting information-theoretic, dynamical, and empirical perspectives, this framework offers a reproducible foundation for consciousness biomarkers that mechanistically link brain dynamics to subjective experience, paving the way for precision applications in neurology, psychiatry, and anesthesia. Full article

Epileptic seizures arise from abnormally synchronized neural activity and remain a major global health challenge, affecting more than 50 million people worldwide. Despite advances in pharmacological interventions, a significant proportion of patients continue to experience uncontrolled seizures, underscoring the need for alternative neuromodulation strategies. Rhythmic neural entrainment has recently emerged as a promising mechanism for disrupting pathological synchrony, but most existing systems rely on complex analog electronics or high-power stimulation hardware. This study investigates a proof-of-concept digital custom-designed chip that generates a stable 6 Hz oscillation capable of imposing a stable rhythmic pattern onto digitized seizure-like EEG dynamics. Using a publicly available EEG seizure dataset, we extracted and averaged analog seizure waveforms, digitized them to emulate neural front-ends, and directly interfaced the digitized signals with digital output recordings acquired from the chip using a Saleae Logic analyser. The chip’s pulse train was resampled and low-pass-reconstructed to produce an analog 6 Hz waveform, allowing direct comparison between seizure morphology, its digitized representation, and the entrained output. Frequency-domain and time-domain analyses demonstrate that the chip imposes a narrow-band 6 Hz rhythm that overrides the broadband spectral profile of seizure activity. These results provide a proof-of-concept for low-power digital custom-designed entrainment as a potential pathway toward simplified, wearable neuromodulation device for future healthcare diagnostics. Full article

Reliable and low-latency seizure detection from electroencephalography (EEG) is critical for continuous clinical monitoring and emerging wearable health technologies. Spiking neural networks (SNNs) provide an event-driven computational paradigm that is well suited to real-time signal processing, yet achieving competitive seizure detection performance with constrained model complexity remains challenging. This work introduces a hybrid spike encoding scheme that combines Delta–Sigma (change-based) and stochastic rate representations, together with two spiking architectures designed for real-time EEG analysis: a compact feed-forward HybridSNN and a convolution-enhanced ConvSNN incorporating depthwise-separable convolutions and temporal self-attention. The architectures are intentionally designed to operate on short EEG segments and to balance detection performance with computational practicality for continuous inference. Experiments on the CHB–MIT dataset show that the HybridSNN attains 91.8% accuracy with an F1-score of 0.834 for seizure detection, while the ConvSNN further improves detection performance to 94.7% accuracy and an F1-score of 0.893. Event-level evaluation on continuous EEG recordings yields false-alarm rates of 0.82 and 0.62 per day for the HybridSNN and ConvSNN, respectively. Both models exhibit inference latencies of approximately 1.2 ms per 0.5 s window on standard CPU hardware, supporting continuous real-time operation. These results demonstrate that hybrid spike encoding enables spiking architectures with controlled complexity to achieve seizure detection performance comparable to larger deep learning models reported in the literature, while maintaining low latency and suitability for real-time clinical and wearable EEG monitoring. Full article

Background: Accurate assessment of prolonged disorders of consciousness (pDOC) is a critical clinical challenge. Misdiagnosis in pDOC can occur in up to 40% of cases, highlighting the need for more objective and reproducible biomarkers to support neurophysiological scales, thereby improving diagnosis and guiding therapeutic and prognostic decisions. Electroencephalography (EEG) microstate analysis is a promising, non-invasive method for tracking large-scale brain dynamics, but research in pDOC has predominantly relied on a canonical 4-class model. This methodological constraint may limit the ability to capture the full complexity of neural alterations present in these patients. Objective: This pilot study aimed to offer an objective method for assessing consciousness, complementing and enhancing the existing approaches established in the literature. The classical 4-class and an extended 7-class microstate model were compared to determine which more accurately characterizes the complexity of resting-state brain dynamics across different levels of consciousness in pDOC patients and healthy controls (HCs). Methods: Retrospective resting-state EEG (rsEEG) data from a cohort of pDOC patients and HC subjects were analyzed. Microstate analysis was performed using both 4-class and 7-class templates. The models were evaluated and compared based on three criteria: spatial correspondence with canonical maps (shared variance), the number of significant intra-group correlations between temporal features (Spearman test), and their ability to discriminate between the pDOC and HC groups (Wilcoxon test). Results: The 7-class microstate model provided a more accurate description of brain activity for most participants, with a greater number of microstate classes exceeding the 50% shared variance threshold compared to the 4-class model. In the pDOC group, both the 4-class and 7-class models showed a mean shared variance <50% in class D, which is associated with executive functioning across both templates. For the HC group, a prevalence of classes B and D emerged in both models, indicating higher engagement of executive functions. Furthermore, the 7-class model allowed for a group-specific analysis, which demonstrated that microstates A and F were consistently shared among 86% of pDOC patients. This suggests the potential preservation of specific intrinsic brain networks, particularly the sensory and default networks, even in the presence of severely impaired consciousness. Moreover, the 7-class model yielded a higher number of significant correlations within both groups and identified a broader set of temporal features that were significantly different between pDOC patients and HCs. These results highlight the enhanced sensitivity of the 7-class model in distinguishing subtle brain dynamics and improving the diagnostic capability for pDOC. Conclusions: The 7-class microstate model provides a more fine-grained and sensitive characterization of brain activity in both pDOC patients and healthy individuals. It demonstrated better performance in capturing individual brain dynamics, identifying shared network patterns, and discriminating between clinical populations. These findings suggest that the extended 7-class model holds greater potential for clinical utility and could lead to the development of more robust biomarkers for assessing consciousness. Full article

Background: The interplay between neuronutrition, physical activity, and mental health for enhancing brain resilience to stress and overall human health is widely recognized. The use of brain mapping via quantitative-EEG (qEEG) comparative analysis enables researchers to identify deviations or abnormalities and track the changes in neurological patterns when a targeted drug or specific nutrition is administered over time. High-functioning mild-to-borderline intellectual disorders (MBID) and autism spectrum disorder (ASD) constitute leading global public health challenges due to their high prevalence, chronicity, and profound cognitive and functional impact. Objective: The objectives of the present study were twofold: first, to characterize an extremely vulnerable group of children with functioning autism symptoms, disclosing their overall pattern of cognitive abilities and areas of difficulty, and second, to investigate the relevance of the effects of a mushroom (Hericium erinaceus) biomass dietary supplement on improvement on neurocognitive behavior. Methods: This study used qEEG to compare raw data with a normative database to track the changes in neurological brain patterns in 147 children with high-functioning autistic attributes when mushroom H. erinaceus biomass supplement was consumed over 6 and 12 months. Conclusions: H. erinaceus biomass in children with pervasive developmental disorders significantly improved the maturation of the CNS after 6 to 12 months of oral use, decreased the dominant slow-wave activity, and converted slow-wave activity to optimal beta1 frequency. Therefore, despite the lack of randomization, blinding, and risk of bias, due to a limited number of observations, it may be concluded that the H. erinaceus biomass may generate a complex effect on the deficits of the autism spectrum when applied to high-functioning MBID children, representing a safe and effective adjunctive strategy for supporting neurodevelopment in children. Full article