Seizures

Subject-independent emotion recognition with EEG bispectral quadratic phase coupling features and explainable machine learning.

Subject-independent emotion recognition from electroencephalography (EEG) is constrained by nonlinear neural dynamics and inter-subject variability. This study characterises nine bispectral quadratic phase coupling (QPC) descriptors extracted from frontal EEG rhythms of the DEAP dataset, selects a compact subset via a genetic algorithm under nested leave-one-subject-out (LOSO) cross-validation, an...

Altered EEG markers of reward learning during abstinence in alcohol dependence: A probabilistic reversal learning study.

OBJECTIVE: This study investigated neurophysiological and behavioural adaptations in reward learning and decision making which may contribute to the development and persistence of alcohol use disorder. METHODS: 20 abstinent alcohol dependent participants (mean abstinence: 20 months, range 1-76) and 26 healthy controls completed an electroencephalography (EEG) probabilistic reversal learning paradi...

A translational multimodal machine-learning prototype predicting valproate response in epilepsy treatment.

OBJECTIVE: Epilepsy affects ~1% of the global population and often requires lifelong antiseizure medication (ASM) therapy. Valproic acid (VPA) is a co...

HRGNN: Hierarchical region-aware graph neural network for interpretable EEG-based emotion recognition.

Neurophysiological studies have shown that cortical information processing involves complex interactions among multiple functional brain regions. Howe...

SHAP analysis of an improved EEG-based mental workload classification framework: utilizing data augmentation and explainable AI.

Mental workload (MWL) classification using electroencephalogram (EEG) signals is crucial for cognitive neuroscience and is also a challenging research...

Single-trial EEG-based classification reveals Instrument-Specific Timbre Perception via traditional Machine Learning Classifiers.

Many users of hearing aids report challenges when listening to music. In the future, it may be possible to develop hearing aids that monitor brain act...

ASEAF: attention-SincNet driven EEG-audio fused target speaker extraction network.

This study addresses the challenge of selective auditory attention in noisy environments by proposing an electroencephalography (EEG)-based target spe...

SAND: Spectral-Attention Neural Decoding of Hand Kinematics from Low-Frequency EEG Dynamics.

Brain-Computer Interface (BCI) technology, integrating neuroscience and artificial intelligence, has been widely applied in neural rehabilitation. How...

From EEG signals to quantitative assessment: predicting depression severity using a novel deep learning framework.

The assessment of depression severity still relies primarily on subjective rating scales, with a lack of objective quantitative biomarkers. This study...

EffortNet: A Deep Learning Framework for Objective Assessment of Speech Enhancement Technologies Using EEG-Based Alpha Oscillations.

This paper presents EffortNet, a novel deep learning framework for decoding listening effort at the individual level from electroencephalography (EEG)...

Performance evaluation of an EEG-guided robotic glove with machine learning models for hand rehabilitation in injured athletes.

BackgroundAssistive rehabilitation technologies play a crucial role in improving motor recovery for individuals with hand injuries particularly athlet...

Comprehensive benchmarking and explainable machine learning analysis of EEG imagery activity recognition.

Motor imagery (MI)-based brain-computer interfaces (BCIs) enable users to control external devices using EEG signals, offering great potential in assi...

Enhancing Drug Response Prediction in Epilepsy with Emerging Multimodal Models: Focus on Clinical, Pharmacologic, and Genomic Factors.

Epilepsy is a common neurological disorder, with approximately one-third of the affected population developing drug-resistant epilepsy despite the exp...

Adaptive fusion of EEG and NIRS with explainable AI reveals neurophysiological markers of cognitive flexibility.

Cognitive flexibility enables individuals to adapt to changing rules, goals, or uncertainty. This study evaluates the discriminative power of electroe...

PyramidPat explainable feature engineering for multiclass electroencephalography psychiatric disorders: Explainable feature engineering and classification.

Deep learning has dominated modern machine learning, and feature engineering has often been neglected. Many studies still focus mainly on accuracy. Th...

Evaluating the effects of regularization and cross-validation parameters on the performance of SVM-based decoding of EEG data.

Regularization has been extensively used in multivariate pattern classification (MVPA; decoding) of EEG data to mitigate the risk of overfitting. N-fo...

Mobility Function and Aperiodic Electrocortical Activity in Younger and Older Adults.

Mobility declines with age to the extent that walking speed is often considered a vital sign. Identifying electrocortical changes behind this decline ...

Identification of Mitochondrial Dysfunction-Related Candidate Biomarkers and Analysis of the Immune Cell Infiltration in Epilepsy.

Epilepsy is a severe neurological disorder with complex pathogenesis. Mitochondrial dysfunction (MitD) is increasingly recognized as a key driver of e...

EEG-Based Clustering Shows Distinct Separation of Chronic Pain Patients Before Spinal Cord Stimulation Surgery.

Chronic pain is associated with disrupted cortical activity, yet individual variability in these neural patterns remains poorly understood. Electroenc...

TF-MCL: Time-frequency fusion and multi-domain cross-loss for self-supervised depression detection.

In recent years, there has been a notable increase in the use of supervised detection methods of major depressive disorder (MDD) based on electroencep...