Seizures

Dynamic Spatio-Temporal Fusion Network Via Hierarchical Self-Attention for Seizure Prediction.

The use of deep learning for EEG-based seizure prediction has grown rapidly in recent years. However, existing studies fail to effectively encode spatial variations under temporal dynamics. This limitation impedes the modeling of complex spatiotemporal evolutionary patterns, consequently leading to suboptimal performance. To address this issue, we propose a hierarchical self-attention-based dynami...

Multiscale Coupling From Mastication to Retronasal Aroma Perception: The PG-DTCFN Model and Multiphysics Simulation.

Retronasal olfaction is central to food flavor perception, yet its multiscale mechanisms from the oral processing to the central nervous system lack systematic elucidation. This study employs grilled lamb skewers as a model, integrating multi-dimensional data and simulations to construct a research paradigm tracing the pathway from bolus release to central perception. Results reveal that extended ...

First-episode psychosis and mood disorder onset in individuals with epilepsy.

BACKGROUND: Psychiatric disorders represent a major burden for patients with epilepsy (PwE). This study examined how demographic, epilepsy-related, an...

Anchor-free temporal localization of apnea events from EEG/EOG with state-space models.

Reduced-channel polysomnography (PSG) and electroencephalography/electrooculography (EEG/EOG) signals can support obstructive sleep apnea (OSA) screen...

Deep Model Families for EEG-Based Multi-Class Dementia Classification.

Deep learning (DL) has shown considerable promise for EEG-based dementia assessment; however, rigorous cross-family comparisons under leakage-free and...

EEG-DBNet: a dual-branch framework for temporal-spectral representation learning of motor imagery electroencephalography.

PURPOSE: Motor imagery electroencephalography (MI-EEG) decoding remains challenging due to low signal-to-noise ratio and complex temporal-spectral cha...

Noninvasive Preoperative Evaluation in Drug-Resistant Epilepsy Based on the Brain Network Topological Dynamics.

Drug-resistant epilepsy (DRE) affects over 50 million individuals worldwide, yet surgical resection, the most effective treatment, achieves seizure fr...

Automated detection of trigeminal neuralgia using multi-domain EEG feature analysis and CNN-attention architecture: a comparative machine learning study.

Trigeminal neuralgia (TN) is a debilitating neuropathic pain disorder characterized by sudden, intense facial pain, with diagnosis heavily reliant on ...

Integrative single-cell and spatial transcriptomics combined with machine learning to discover complement-associated biomarkers in temporal lobe epilepsy with hippocampal sclerosis.

Temporal lobe epilepsy with hippocampal sclerosis (TLE-HS) poses significant challenges in therapeutic management. While studies have demonstrated sei...

Neuroinformed spectro-temporal-spatial attention with bi-hemispheric learning for EEG emotion recognition.

Accurate recognition of human emotions from electroencephalogram (EEG) signals is fundamental to affective computing, yet it remains challenging due t...

DST-GNN: A dynamic spatio-temporal graph neural network for motor imagery classification.

Electroencephalography (EEG)-based motor imagery classification plays an important role in brain-computer interface (BCI) systems. However, existing m...

Neurophysiological EEG Characterization of Autism Spectrum Disorder Using DWT-Based Frequency Analysis With Selective Electrodes and Brain Segmentation: An Explainable AI-Driven Approach.

Autism Spectrum Disorder (ASD) diagnosis benefits from the technical analysis of neural oscillations. The objective identification of Autism Spectrum ...

Artificial Intelligence in Neonatal Care: The Breadth of Promise, the Depth of Challenge-An Overview.

Artificial intelligence (AI) is becoming an integral tool in clinical care. The recent position statement by the Royal Australasian College of Physici...

Predicting comorbid anxiety in adolescents with major depressive disorder: an EEG-based machine learning approach with SHAP interpretability.

Comorbid anxiety in adolescents with major depressive disorder (adMDD) is linked to higher suicide risk and poorer prognosis, necessitating precise sc...

Deep-learning based electroencephalogram denoising: A literature review.

Electroencephalography (EEG) is a pivotal tool for exploring brain functions. However, the low amplitude of EEG signals renders them inherently suscep...

Multimodal quantification of cognitive load using a printed wearable facial bio-potential system.

Cognitive load refers to the amount of mental effort required to process information and perform tasks. It has a strong impact on both learning and ta...

Frequency-decomposed EEG microstate analysis reveals altered cross-frequency coordination in adolescent major depressive disorder.

Adolescent major depressive disorder (MDD) involves alterations in large‑scale brain network dynamics. However, conventional EEG microstate studies ty...

Multilayer Validation Reveals a Glia-Associated Secretome Signature in Temporal Lobe Epilepsy.

Although multi-omics studies have increasingly revealed molecular alterations associated with epilepsy, clinically accessible cerebrospinal fluid (CSF...

Prefrontal Connectivity Alterations and Oscillatory Dynamics in Cannabis Use Disorder.

Long-term cannabis use can result in the development of cannabis use disorder (CUD) and dependence via the endocannabinoid system pathway. A brain net...

Forecasting Excessive Anesthesia Depth Using EEG α-Spindle Dynamics and Machine Learning.

Accurately predicting transitions to anesthetic drugs overdosage is a critical challenge in general anesthesia as it requires the identification of EE...