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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 for enhancing attention among individuals with attention-deficit hyperactivity disorder (ADHD). However, existing systems often lack adaptive difficulty regulation and rely solely on single-modal assessments, thereby limiting personalization and susta...

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 stroke recovery. Despite substantial progress, challenges such as inter-subject variability, lack of training data for specific subject, and the need for time-consuming calibration still hinder the practical deployment of MI-BCI systems. NEW METHOD: In th...

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...

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...

Transcriptomic analysis and machine learning have identified shared diagnostic genes and a possible mechanism linking bipolar disorder and epilepsy.

It is well known that bipolar disorder (BD) and epilepsy (EP) are common neurological diseases. The objective of this study was to screen for potentia...

Inferring preoperative cognitive function from intraoperative electroencephalography in elderly patients using machine learning.

To develop and evaluate machine learning (ML) models that infer preoperative cognitive function from intraoperative electroencephalography (EEG). This...

Differential quadruple pattern: A new EEG signal classification framework.

EEG signals are the letters of the brain and reflect neural activity. Abnormal EEG patterns indicate brain disorders such as epilepsy. Recently, machi...

Time-frequency-spatial channel attention network for semantic decoding: an exploratory EEG study.

Semantic decoding is a crucial approach for investigating the neural mechanisms underlying language processing and representation. Informed by brain-c...

Cognitive reinforcement: capturing tacit knowledge and enhancing expertise with a biofeedback interface for visual attention.

Objective.Tacit or implicit knowledge refers to know-how that experts possess but often cannot articulate, codify, or explicitly transfer to others. T...