Seizures

Unified Multi-Class Electroencephalogram Artifact Recognition Using Machine Learning Classifiers.

Artifacts are noisy signals that commonly contaminate electroencephalographic (EEG) recordings, mixing with underlying brain activity and degrading the quality of neurophysiological data. Previous research on epileptic Anomaly Detection has shown that this approach is also sensitive to unlabelled artifacts, often leading to an increased False Positive rate. While most methods focus on detecting or...

A Privacy-Preserving Markov Chain-Based Framework for Robust Motor Imagery EEG Classification in Brain-Computer Interfaces.

Accurate classification of motor imagery (MI)-based electroencephalogram (EEG) signals is often challenged by signal non-stationarity, subject-specific variability, and privacy concerns associated with sharing raw neural data. To address these challenges, this study proposes a hybrid Markov chain-spatial statistical (MCSS) machine learning framework for privacy-preserving MI-EEG classification. Sp...

Detecting drug-induced EEG alterations with machine learning: A methodological proof-of-concept study in rats treated with MK-801.

INTRODUCTION: Although deep learning methods for EEG analysis are rapidly advancing, architectures developed for human multichannel recordings may not...

Combining residual U-Net and data augmentation for dense temporal segmentation of spike wave discharges in single-channel EEG.

Manual annotation of spike-wave discharges (SWDs), the electrographic hallmark of absence seizures, is labor-intensive for long-term electroencephalog...

Effiformer: a unified data-efficient vision transformer-CNN framework for interpretable epileptic seizure detection.

Epileptic seizures are short episodes of abnormal electrical activity in the brain that can cause convulsions, loss of consciousness, and other simila...

EEG-X: An Integrated Framework for Automated Quantitative EEG Analysis in Epilepsy Diagnosis.

Accurate interpretation of electroencephalography (EEG) remains a major challenge in epilepsy diagnosis, particularly given that patient heterogeneity...

A Convolutional Neural Network Combining Multi-scale Convolution and Functional Connectivity for Predicting rTMS Efficacy in MUD.

PURPOSE: Repetitive transcranial magnetic stimulation (rTMS) is a promising neuromodulation approach for treating methamphetamine use disorder (MUD), ...

Hybrid self-supervised EEG emotion representation: masked reconstruction joint with mutual information bounds.

EEG signals are widely used in affective computing and brain informatics for emotion recognition due to their non-invasiveness. Deep learning and self...

SMFF-Net: Spatiotemporal-frequency Multi-domain Feature Fusion Network for EEG-based brain state detection.

The timely and reliable detection of driver fatigue is crucial for reducing driving risks and improving traffic safety. EEG-based deep learning approa...

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 spat...

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 s...

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...