Seizures

A time-frequency cross-attention network model for epileptic seizure detection.

Epilepsy is a chronic neurological disease that profoundly impacts patients' daily lives. Electroencephalography (EEG) serves as a crucial tool for the clinical diagnosis of epilepsy and other brain disorders. Current research methods primarily concentrate on the time domain of EEG signals, often preprocessing frequency domain information without thorough exploration or effective integration with ...

Estimating cognitive function score from mild cognitive impairment to moderate dementia using a hybrid model combining plasma biomarkers with electroencephalogram signal.

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....

Decoding epilepsy's molecular blueprint: Machine learning unravels transcriptomic subtypes and regulatory networks.

OBJECTIVE: Drug-resistant epilepsy (DRE) affects approximately one-third of patients with epilepsy. The molecular heterogeneity underlying DRE remains...

Sub-band Embedding Based EEG Spatio-Temporal Activity Representation for Emotion Recognition.

EEG-based emotion recognition is a crucial task with significant implications for mental health monitoring, affective computing, and clinical decision...

A neurofeedback-guided EEG and BCI framework for personalized attention rehabilitation in ADHD.

The integration of game-based cognitive training with electroencephalography (EEG)-based brain-computer interaction (BCI) has demonstrated potential f...

Meta-Learning Enhanced Multi-Source Domain Adaptation for zero-calibration motor imagery EEG decoding.

BACKGROUND: Motor imagery (MI) based brain-computer interface (BCI) holds promising application prospects for closed-loop neurorehabilitation in strok...

Transient frontal spectral events from EEG predict antidepressant response to sertraline in depression.

Resting-state scalp electroencephalography (EEG) is a promising method for predicting patient outcomes of antidepressant treatments. Machine-learning-...

Multimodal machine learning for major depressive disorder: Integrating EEG functional connectivity and clinical variables to enhance diagnostic accuracy.

The diagnosis of Major Depressive Disorder (MDD) relies heavily on subjective clinical assessments. This study evaluated various machine learning mode...

A dual-branch deep learning framework for emotion recognition from EEG signals.

Mental health monitoring through emotion recognition plays an important role in early intervention and personalized healthcare systems. Traditional EE...

Heart rate and sleep history encode ultradian REM sleep timing.

During sleep, the brain alternates between rapid eye movement (REM) and non-REM (NREM) sleep, with recurring REM sleep episodes forming the ultradian ...

The role of emotion in sleep: a quantitative analysis using EEG data.

STUDY OBJECTIVES: The intricate interplay between sleep and emotion has garnered increasing attention due to their profound impact on human health and...

Pediatric SleepNet: A Deep Learning Network for Reliable Pediatric Sleep Staging Across Developmental Stages.

STUDY OBJECTIVES: Manual sleep staging in pediatric populations is challenging due to developmental variability and limited scoring consistency, espec...

EEG based detection of schizophrenia using asymmetry of entropy and CNN-LSTM model.

Schizophrenia is a severe neuropsychiatric disorder with a significant impact on individual's real-life functioning. It is characterized by abnormal a...

Functional brain network modulation following conventional and optimized HD-tDCS in major depressive disorder: A machine learning prediction of treatment response.

BACKGROUND: Major depressive disorder (MDD) is a prevalent and disabling condition that remains inadequately treated in many patients. Transcranial di...

Machine learning-based prognostic analysis of patients with status epilepticus in the neurological intensive care unit.

OBJECTIVE: This study aimed to develop and validate machine learning (ML) models for predicting the prognosis of status epilepticus (SE) patients with...

EENet-RLA: An Explainable Prediction Learning Framework for Alzheimer's Disease Classification from EEG Signals.

Alzheimer's disease (AD) is a prevalent neurodegenerative disorder affecting millions worldwide. Electroencephalography (EEG), a non-invasive, cost-ef...

Short-Term Perceptual Training Modulates Neural Responses to Deepfake Speech but Does Not Improve Behavioral Discrimination.

Rapid advancements in artificial intelligence (AI) have enabled text-to-speech (TTS) systems to produce voices increasingly indistinguishable from hum...

Research on epilepsy detection methods based on interpretable features and machine learning.

Epilepsy is a prevalent neurological condition that impacts a significant number of individuals worldwide. Patients' physical and mental health, as we...

Effectiveness of meditation in the management of epilepsy: An updated mini systematic review of recent findings.

BACKGROUND: Epilepsy is a chronic neurological disorder characterized by altered cortical excitability. The disorder is often associated with psycholo...

Predicting Behavioral Reliance on AI-Based Depression Treatment Recommendations Among Engineering Graduate Students: A Pilot Study Using EEG Signals.

OCCUPATIONAL APPLICATIONSThis pilot study demonstrates the feasibility of using EEG-derived features to characterize behavioral reliance among enginee...