Genetic epilepsies include a broad spectrum of disorders caused by pathogenic variants in more than 1,000 genes. Their clinical expression is highly variable, making early phenotype-genotype interpretation challenging. Early seizure semiology and EEG features may offer clinically useful information for diagnostic orientation and management. The aim of this study was to characterize early clinical and EEG features in patients with genetic epilepsies, examine their associations with outcomes, and explore genotype-phenotype groupings through hierarchical clustering analysis (HCA). We conducted a retrospective study at Bambino Gesù Children's Hospital. Eligible participants carried pathogenic or likely pathogenic variants in epilepsy-related genes, identified through medical records and laboratory diagnostic logs. Clinical variables at seizure onset and EEG recordings performed within the first month of the initial seizure were extracted. Follow-up outcomes included seizure frequency, drug resistance, movement disorders, behavioral/autism spectrum disorder comorbidities, and developmental delay/intellectual disability (DD/ID). Associations between early features and outcomes were assessed using χ2 or Fisher tests. HCA was used to identify clusters linking early phenotype and gene-level etiology. We included 277 patients (52.3% female; median age at last follow-up 8.1 years, range 0-40). Drug resistance occurred in 58.8% and severe DD/ID in 35.4% of patients. EEG data at onset were available for 107 individuals. Neonatal onset was associated with a higher rate of drug resistance (71.4%; odds ratio [OR] 2.0, 95% CI 1.05-3.77), movement disorders (60.7%; OR 3.7, 95% CI 2.02-6.82), and severe DD/ID (71.4%; OR 7.0, 95% CI 3.66-13.49). Slow EEG background activity and multifocal epileptiform discharges were associated with both drug resistance and severe DD/ID. HCA identified genotype-phenotype groupings, including clusters involving SCN1A, PRRT2, STXBP1, KCNQ2, SCN2A, CHD2, SYNGAP1, and MECP2, each linked to specific clinical and EEG features. Early clinical and EEG features showed meaningful associations with outcomes and mapped onto specific genetic etiologies. HCA revealed coherent genotype-phenotype clusters that may support early diagnostic reasoning. Limitations include the retrospective design and small numbers per gene, warranting larger multicenter studies for validation.

EEG-based epileptic seizure prediction with patient-tailored spectral-spatial-temporal feature learning.

Mild traumatic brain injury detection: uncovering neural interaction patterns through dynamic Hilbert warping features with EEG data

Melancholia is often found to be unresponsive to standard antidepression medication. In the search for biomarkers that may also suggest possible treatment options, some attention has been given to EEG data. However, this research has largely ignored the gamma band of electrical activity in the brain (30-130 Hz). In an exploratory investigation of the association between melancholia and gamma wave activity, a standardized 8-item scale was completed by 100 community participants, who also underwent eyes-open and eyes-closed EEG data collection from 30 brain sites. There was a significant association between severity of depression, melancholia, and gamma. Individual melancholia symptoms were found to have meaningful correlations with specific EEG sites, which were then further explored via source localization (eLORETA). Most associations between gamma and melancholia items as measured via eLORETA were direct, indicating that, as gamma increased, so did participants' severity of particular melancholia symptoms, implying that depressed participants utilized increased neurocognitive activity as a possible strategy for coping with aspects of melancholia. Although these results are exploratory, they address a gap in the literature and suggest several hypotheses for future research.

Exposure to natural environments has been linked to improved well-being, reduced stress, and enhanced attention. This study examined neurophysiological and behavioral effects of multisensory nature exposure (visual, auditory, and olfactory) compared to urban environments. Thirty-two right-handed women (25-49years) performed a Go/No-Go task while EEG, electrodermal activity (EDA), and heart rate (HR) were recorded. Participants experienced images alone, images with sounds, images with odors, and images with both sounds and odors. Behavioral results showed improved task performance in natural environments, with more correct responses. However, odor-enriched conditions increased errors, suggesting that olfactory inputs may interfere with attention. Psychophysiological measures indicated lower skin conductance response (SCR) frequency in natural environments, especially under multisensory stimulation, consistent with reduced sympathetic arousal. EEG data revealed clear neural differences: the P1-N1 complex, linked to attentional effort, had greater amplitude in urban contexts, while the early posterior negativity (EPN), related to emotional processing, was stronger in natural conditions. Source localization associated EPN effects with the right occipital and inferior frontal gyrus. These findings support Attention Restoration Theory (ART) and Stress Reduction Theory (SRT), highlighting nature's restorative potential. Sensory enrichment appeared to strengthen emotional engagement while modulating attentional performance. Results emphasize the value of multisensory perspectives in environmental neuroscience and point to EEG biomarkers as sensitive indicators of cognitive and affective processes. Future work should extend these insights to real-world settings using mobile neurophysiological methods.

BackgroundPredicting cognitive function across dementia stages remains challenging. Plasma biomarkers and electroencephalogram (EEG) features may provide complementary information, but their combined predictive value requires further study.ObjectiveTo evaluate the feasibility of integrating plasma biomarkers and EEG features to predict cognitive function in dementia and examine their correlations.MethodsFrom September 2023 to October 2024, 75 patients from two medical centers with mild cognitive impairment, mild dementia, or moderate dementia were enrolled. Resting-state 19-channel EEG data yielded 2737 time-frequency and connectivity features. Plasma biomarkers included tau, p-Tau181, Aβ1-42, neurofilament light chain (NfL), brain-derived neurotrophic factor, apolipoprotein E genotype, and glial fibrillary acidic protein. Cognitive function was assessed using Cognitive Abilities Screening Instrument (CASI), Mini-Mental State Examination, Montreal Cognitive Assessment (MoCA), and Clinical Dementia Rating Sum of Boxes. Machine learning models were developed using plasma-only, EEG-only, and hybrid approaches.ResultsNfL was negatively correlated with CASI (t = -2.059, p < 0.05). Several EEG features showed moderate correlations with cognitive measures and plasma biomarkers, with delta-band relative power between left frontal and temporal regions (F7-FT7) showing the strongest correlation with MoCA. The hybrid model achieved the best performance, with R2 > 0.74 across all cognitive measures, outperforming plasma-only and EEG-only models.ConclusionsIntegrating EEG features with plasma biomarkers improves prediction of cognitive function from mild cognitive impairment to moderate dementia, pending external validation.

Dementia, particularly Alzheimer's disease (AD), constitutes a major global health concern, with AD accounting for approximately 70% of all cases. EEG-based biomarkers hold promise for early identification of individuals at risk; however, small and heterogeneous samples frequently limit generalizability. An EEG-based sample enrichment framework was developed by integrating advanced signal processing, component-level feature extraction, data harmonization (neuroHarmonize), and Propensity Score Matching (PSM). EEG data from four independent cohorts were harmonized to reduce site-related variability while preserving covariates such as age and sex. Features including power, entropy, coherence, synchronization likelihood, and cross-frequency coupling were extracted from independent components. PSM was applied at 2:1, 5:1, and 10:1 ratios to expand and balance the control group (HC) relative to the Alzheimer's risk group (ACr), composed of PSEN1-E280A mutation carriers without cognitive symptoms. Sample enrichment through PSM improved classification accuracy, with decision tree models yielding values between 0.91 and 0.96. Higher enrichment ratios enhanced model stability and generalizability, as shown by learning curves and confusion matrices. Feature selection was based on model performance and effect sizes (Cohen's d). The proposed framework addresses sample size and variability constraints in EEG-based AD risk classification. Harmonization and statistical balancing provide a replicable strategy for multicenter EEG studies targeting early AD detection.

Actions are the building blocks of our dynamic visual world, yet the neural computations supporting action perception are not well understood. How does perceptual and conceptual information unfold in the brain when we observe what others are doing? We collected EEG and fMRI data while participants viewed short videos and sentences depicting naturalistic actions. Using representational similarity analysis, we found distinct conceptual representations along the ventral, dorsal, and lateral pathways, with the target of actions specifically encoded in lateral occipitotemporal cortex (LOTC) and posterior superior temporal sulcus (pSTS). Among conceptual features, the target of actions (i.e. whether the action was directed at an object, a person, or the self) explained the most unique variance in EEG responses. Finally, EEG-fMRI fusion revealed rapid processing along the lateral and dorsal pathways. Together, our results disentangle the perceptual and conceptual components of action understanding and characterize the underlying spatiotemporal dynamics in the human brain.

Major depressive disorder (MDD) is associated with impaired emotional processing and dysregulation of large-scale brain networks. While electroencephalography (EEG) studies have reported altered functional connectivity in depression, the directional organization of information flow during emotional processing remains insufficiently characterized. This study investigated emotion-evoked directed functional connectivity (DFC) during emotional music perception in individuals with MDD using EEG. EEG data were collected from 18 individuals with MDD and 18 healthy controls while listening to music with positive, neutral, and negative emotional valence. Frequency-specific DFC patterns were estimated using the directed transfer function. Group differences were assessed using generalized estimating equations and non-parametric permutation tests, and associations between DFC features and depressive symptom severity were examined using partial correlation analysis. A subject-level validation framework was employed to evaluate the discriminative properties of DFC features that demonstrated both group-level differences and clinical relevance. The results showed a significant increase in delta-band DFC strength in individuals with MDD across all emotional music conditions, with spatially distributed alterations in the frontal, central, and parieto-occipital regions. Under the positive music condition, delta-band DFC features were significantly associated with depressive symptom severity and reliably distinguished individuals with MDD from healthy controls at the individual level. In addition, behavioral analysis revealed significant negative correlations between music liking ratings and depressive symptom severity. These findings characterize task-evoked alterations in directional brain network organization during emotional music processing in MDD and support the utility of EEG-based DFC analysis for system-level investigation of affective network dysfunctions.

Bipolar disorder (BD) is a complex mood disorder characterized by recurrent depressive and manic/hypomanic episodes, accompanied by significant cognitive dysfunction and emotional dysregulation. Accurate and timely diagnosis, especially the differentiation between subtypes, remains a challenge due to overlapping symptoms, variable onset times for more specific symptoms (e.g., psychotic features), and the reliance on subjective assessments. This study examines the use of a multimodal approach combining electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) to identify patterns of BD emotional dysregulation, aiming to enhance its diagnosis and subtype differentiation. The protocol employed an emotional visual task to evaluate the interference of emotional content on cognitive function. EEG data were collected using a whole-head cap, while fNIRS focused on hemodynamic changes in the frontal cortex. Furthermore, the feasibility of using a potential simplified, portable EEG-fNIRS system was explored by focusing the analysis on frontal regions. The cohort included BD patients [BP] of two main subtypes, and healthy controls [HC]. Behavioral analysis revealed significant performance differences between BP and HC groups. While EEG alone enabled groups' classification, integrating EEG and fNIRS improved accuracy by reducing misclassification rates. Although classification using only frontal EEG regions was slightly less accurate than the full-head cap, fNIRS integration ensured robust results, supporting the feasibility for a potential simplified system. These findings underscore the complementary strengths of EEG and fNIRS in capturing neural and vascular markers of emotional dysregulation in BD and support the development of multimodal diagnostic tools for BD.

To address the challenge of assessing cognitive load in maritime personnel, this study developed a multi-task navigation simulation paradigm. Fifteen maritime professionals, organized into groups of three, performed relevant cognitive load tasks in a navigation simulator. To overcome the difficulty of limited sample size in EEG data acquisition, this study proposes an Attention-based Long Short-Term Memory Generative Adversarial Network (Attention-LSTM-GAN). This model synthesizes highly biologically plausible multi-band EEG signals and incorporates ship behavioral data as a multimodal validation anchor, thereby establishing a neuro-behavioral synergistic framework for cognitive state assessment. The attention mechanism integrated into the model adaptively enhances the representation of key frequency band features, including theta, alpha, and beta. Experimental results demonstrate optimal performance at 4× data augmentation (θ: 92.59%, α: 86.69%, β: 90%). Multi-band fusion further improves the integrated classification accuracy to 97.78%, significantly outperforming traditional machine learning methods (SVM: 60.49%; CNN: 83.02%; RNN: 82.55%).

Small-world scale-free brain networks from EEG with application to motor imagery decoding and brain fingerprinting.

Noise degrades both EEG and gait signals, and classical IIR filters (Butterworth, Chebyshev, elliptic) involve trade-offs between passband flatness, ripple, and roll-off. This study compared a novel exponential "Reza" filter with these designs for neural and locomotor data. We analyzed an open-source mobile brain-body imaging dataset with EEG and gait data from 49 healthy adults (EEG: 256-channel, 512 Hz; IMUs: six APDM Opals, 128 Hz). EEG channels were grand-averaged and band-pass filtered at 0.5-50 Hz, while IMU axes were averaged and band-pass filtered at 0.5-5 Hz. The outcomes were signal-to-noise ratio SNR (dB) and band-integrated Welch PSD (EEG:0.5-50 Hz; IMU:0.5-5 Hz). Repeated-measures ANOVAs tested the effect of filter types (Butterworth, Chebyshev I, elliptic, Reza) with Bonferroni-adjusted post hoc tests for the six pairwise filter comparisons (αadj = 0.0083). We reported partial eta-squared (ηp2) as the ANOVA effect size. For EEG, PSD did not differ among filters (p = 0.146), whereas SNR differed strongly (p<0.001): Chebyshev and elliptic yielded the highest mean SNR and did not differ from each other, while both exceeded Butterworth, Reza was the lowest. For IMU, both SNR (p< 0.001) and PSD (p< 0.001) differed: Reza produced the highest mean SNR (significantly exceeding elliptic and Chebyshev), while Butterworth exceeded Chebyshev; meanwhile, IMU PSD showed a clear ordering with Reza retaining the most motion-band power, followed by Butterworth, then Chebyshev, with elliptic retaining the least. These results showed that filter choice materially shapes EEG and gait outcomes. For EEG, Chebyshev maximized SNR, while elliptic and Reza maintained comparable fidelity. For IMU gait signals, Reza matched Butterworth for denoising and preserved more signal power. Therefore, filter choice should be guided by the target outcome (SNR vs. band power) rather than a single default design.

Delirium occurs frequently after major surgery in older adults and is associated with long-term cognitive dysfunction. While postoperative delirium (POD) shows electroencephalographic (EEG) changes during the acute phase, it remains unclear whether quantitative EEG alterations precede or persist after POD. Identifying such patterns could reveal risk markers and mechanisms of long-term cognitive dysfunction. In this prospective multicenter, cohort study, EEG recordings were obtained in patients aged ≥65 years before and three months after major elective surgery without pre-existent cognitive dysfunction, and in non-surgical controls. We analyzed quantitative EEG measures that show alterations during acute delirium, namely: relative power, phase-based and amplitude-based functional connectivity, spectral variability and signal complexity. Linear mixed models were used to assess effects of time, surgery, and POD on these EEG measures. Of 379 enrolled surgical patients, 330 had sufficient EEG data quality, of which 59 (18%) developed POD. Fifty-seven non-surgical controls were included and served as reference. At baseline, future POD patients exhibited significantly lower beta amplitude based connectivity (β= -0.36, pcorrected=0.04). No other EEG measures showed significant differences between groups at baseline. Three months after surgery, no persistent changes in quantitative EEG characteristics were observed in relation to POD occurrence or surgery alone. This study identified reduced preoperative beta amplitude-based connectivity as a possible marker of neurophysiological vulnerability for POD. However, the absence of significant longitudinal changes in quantitative EEG measures suggests that resting-state EEG networks may functionally recover or compensate in this cohort at three months postoperative.

Background: Parkinson’s disease is a progressive neurodegenerative disorder. Early PD detection plays a key role in effective therapy. Electroencephalography is a neuroimaging technique used to analyze brain abnormalities, such as those seen in patients with PD. However, the complex nature of EEG data requires advanced signal processing and classification methods. Methods: This study systematically evaluates three time-frequency (TF) representation techniques, namely discrete wavelet transform (DWT), continuous wavelet transform (CWT), and synchrosqueezing transform (SST), along with four pretrained convolutional neural network architectures for EEG-based PD detection. The experiments were performed using the San Diego dataset. Image-wise and subject-wise 5-fold cross-validation were employed to assess performance and generalization capability. Results: CWT and SST consistently outperform DWT across all evaluated architectures in image-wise CV evaluation. At the image-wise level, the CWT-EfficientNet-B0 model achieved 97.28% accuracy for HC vs. PD-OFF classification, while SST-EfficientNet-B0 reached 97.26% accuracy for HC vs. PD-ON classification. In subject-wise evaluation, acceptable accuracies of up to 84% were achieved, indicating the ability of the framework in learning PD patterns for unseen subjects. Conclusions: These findings demonstrate that the choice of TF representation has a strong impact on classification performance and that lightweight CNN architectures can achieve high image-wise accuracy with reduced computational cost.

Treatment-resistant depression (TRD) accounts for approximately 30% of major depressive disorder cases and has been characterized by altered functional connectivity within and between the Default Mode (DMN) and Frontoparietal networks (FPN). Ketamine can be an effective treatment for TRD, and its antidepressant response has been associated with alterations in resting state functional connectivity (rsFC). Here, we evaluated the effect of a single subanesthetic dose of racemic ketamine (0.5 mg/kg) on electroencephalogram (EEG) derived source-based measures of rsFC from 24 participants with TRD (16 women; aged 44.35 ± 15.86 years). Ninety-six channel resting state EEG data were collected 24 h before and after ketamine infusion. Exact low-resolution electromagnetic tomography (eLORETA) was used to estimate theta and beta-band rsFC within and between the DMN and FPN. Ruminative symptoms were assessed using the Ruminative Response Scale. Analogous data were collected from 34 healthy control participants (25 women, aged 32.49 ± 14.07 years) who did not receive any intervention. Twenty-four hours post-infusion, depressive, anhedonic, and ruminative symptoms for the TRD sample were significantly reduced. Interestingly, symptom reduction was not correlated with any changes in rsFC but was associated with initial pre-ketamine rsFC. Moreover, individuals with TRD displayed broad increases in rsFC within the DMN and FPN as well as between these two networks. Based on preclinical findings, we posit that ketamine's synaptogenic effects may be driving this general increase in connectivity. However, these synaptogenic effects can be short lived, and future work probing the full time-course of rsFC via EEG pre- and post-ketamine administration is warranted.

Semantic decoding is a crucial approach for investigating the neural mechanisms underlying language processing and representation. Informed by brain-computer interface (BCI) technology, this study investigated methods for decoding semantic information, with an emphasis on the neural representations of semantics in language perception. Due to the limited availability of electroencephalography (EEG) datasets containing Chinese linguistic stimuli, we have specifically designed a semantic task paradigm as a promising attempt to decode language comprehension and expression in patients with aphasia using scalp EEG. This paradigm fully incorporates the processes underlying both speech perception and speech imagery by adopting tasks such as overt speech perception and silent speech imagery. Firstly, Seventeen participants of aphasia patients and healthy subjects were recruited for EEG data collection. Secondly, we constructed a deep learning model termed Time-Frequency-Spatial Channel Attention Network (TFSANet), which processes both time-domain and frequency-domain features to extract key neural signatures associated with semantics. By optimizing the model and employing multidimensional feature extraction mechanisms, we significantly improved the model's ability to decode semantically relevant EEG features. Finally, the experimental results demonstrate the proposed TFSANet could decode semantic information from EEG for ten categories of four-word phrases under an "auditory-guided" paradigm with an accuracy of 60.73% and 75.09% for aphasia patients and healthy subjects respectively.

Two major global trends shaping 21st-century society are population aging and urbanization. Consequently, the living conditions of older adults have become an increasing focus of societal attention. Social–Emotional Responses play a crucial role in the mental health, emotional well-being, and social identity of older adults. Urban streets, as key sites for walking and social activity among older adults, can be seen as extensions of their homes—places where they regularly interact with neighbors and build new connections. Compared to built environments often termed “gray spaces,” exposure to green spaces has been shown to offer greater benefits to residents’ well-being. Among streetscape features, the Spatial Openness Level is closely associated with the psychological well-being of elderly individuals. Visual-spatial features correlate with an EEG-derived proxy for emotional state during exposure to street scenes. The Gray-Green space Exposure Ratio (GER) and Spatial Openness Level (SOL) serve as key indicators for evaluating streetscape quality. Designing age-friendly streets requires evidence-based tools that link visual features to emotional well-being. This study provides such a tool by combining EEG measurements with configurational analysis of street visual dimensions: SOL and GER. In this study, conducted in Wuhan City, objective physiological monitoring of brainwave activity was employed to examine the responses of older adults to variations in GER and SOL. The results indicate that SOL significantly influences the emotional states of older adults (correlation coefficient R2 = 0.7262, p &lt; 0.01). The results indicate that the effect of GER on the emotional states of older adults was moderated by gender. Specifically, GER exerted a significant effect on the emotional states of females (correlation coefficient R2 = 0.6262, p &lt; 0.01), whereas no significant effect was observed in males (p &gt; 0.01). These results allow us to rank the nine tested scenes. For example, Scene L-3 (open space with abundant vegetation) scored highest on emotional well-being, while Scene H-1 (enclosed gray space) scored lowest. The difference is explained by the configurational logic: greenery delivers emotional benefits only when combined with sufficient openness. The findings will enable EEG data to extend beyond serving as a unique standalone outcome and integrate into a more comprehensive explanatory model. This model aims to elucidate how urban morphology influences the micro-foundations of social activity in later life. Furthermore, it seeks to equip urban designers and policymakers with an evidence-based tool for creating age-friendly environments, facilitating a shift from intuition-driven to evidence-based design. Future research should incorporate additional environmental factors to establish a more comprehensive assessment framework for age-friendly urban spaces.

Independent Component Analysis (ICA) is a blind-source separation method, meaning that it takes in a recording with multiple sensors and attempts to unmix it into the original sources. For example, suppose there are 4 people (sources) speaking in a room with 4 microphones (sensors), then ICA unmixes the recording from the 4 microphones to give tracks of the individual people called ICA components. ICA is currently used to decompose a variety of signals with many sensors, including fMRI and EEG data. However, its use in interpreting data with fewer sensors, such as the local field potential (LFP), is limited because of concerns about how it handles over-complete data (data with more sources than sensors). While there has been some success in enhancing ICA so that it can extract more sources than sensors, we focus on how ICA handles over-complete data. In this paper, we show that ICA consistently bins sources with similar spatial maps together when there are 3 sinusoidal sources and 2 sensors.

The event-related potential (ERP) analysis allows us to measure brain activity as it reflects sensory processing, attention, and a range of cognitive tasks. In this research article we introduce a comprehensive ERP-based approach for the identification of neurophysiological markers of alcoholism, using the open EEG dataset and the MNE-Python toolkit. All the EEG data recorded during a visual object recognition task were uniformly processed for both the alcoholic and control groups. After filtering and re-referencing, independent component analysis was applied, followed by segmenting the data into epochs and performing baseline correction. We focus on well-known ERP components, notably N2 and P300, occurring roughly 200-300 ms and 300-600 ms after the onset of the stimulus, respectively, which are associated with cognitive evaluation processes. We clearly see two distinct ERP profiles between the two groups. The alcoholic group shows reduced P300 and altered N2 compared with controls. This study presents a transparent and reproducible ERP analysis pipeline, developed solely with open-source tools and data, and highlights the potential of ERP markers as neurophysiological indicators of cognitive changes associated with alcohol use disorder.