Seizures

Developmental Profile of Physiological High-Frequency Oscillations in the Human Brain.

High-frequency oscillations (HFOs), transient burst of ≥ 80 Hz activity, are increasingly recognized as promising EEG biomarkers of the epileptogenic zone for guiding epilepsy surgery. However, the presence of physiological HFOs in healthy cortex complicates their interpretation. The developmental profile of physiological HFOs in children remains poorly characterized, despite profound maturational...

EEG microstate dynamics and metal metabolism in Parkinson's disease dementia: an exploratory study with machine learning-assisted sensitivity analysis.

BACKGROUND: Electroencephalogram (EEG) microstates effectively characterise cognitive-related brain networks, and metal homeostasis is crucial for maintaining cognitive function. However, the relationship between these factors in Parkinson's disease (PD) with dementia (PDD) remains unclear. We investigated this association and evaluated the performance of these combined features in classifying PDD...

Isolating Eye-Movement Artifacts from EEG Signals.

The electroencephalogram (EEG) provides a direct measure of brain electrical activity but is typically contaminated by artifacts, most notably those a...

Prediction and diagnosis of post-stroke epilepsy using artificial intelligence approaches: a systematic review and meta-analysis.

INTRODUCTION: Post-stroke epilepsy (PSE) is a common complication following a stroke and is a major cause of epilepsy in the elderly. Artificial intel...

AI-Integrated EEG Decision Support for Neurocritical Care: A Conceptual and Feasibility Framework.

The clinical use of electroencephalography (EEG) for neuro-prognostication in neurocritical care remains limited, despite its ability to provide non-i...

Spatio-Temporal Attention with Spiking Neural Networks for Seizure Detection from Electroencephalogram Signals.

Timely and accurate detection of seizures from Electroencephalogram (EEG) signals is critical for the effective management of epilepsy. Although deep ...

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. T...

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 d...

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...