AIMC Topic: Electroencephalography

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Data-driven sleep structure deciphering based on cardiorespiratory signals.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Cardiorespiratory signals provide a novel perspective for understanding sleep structure through the physiological mechanism of cardiopulmonary coupling. This mechanism divides the coupling spectrum into high-frequency (HF) a...

Challenges and Opportunities: Nanomaterials in Epilepsy Diagnosis.

ACS nano
Epilepsy is a common neurological disorder characterized by a significant rate of disability. Accurate early diagnosis and precise localization of the epileptogenic zone are essential for timely intervention, seizure prevention, and personalized trea...

AdamGraph: Adaptive Attention-Modulated Graph Network for EEG Emotion Recognition.

IEEE transactions on cybernetics
The underlying time-variant and subject-specific brain dynamics lead to inconsistent distributions in electroencephalogram (EEG) topology and representations within and between individuals. However, current works primarily align the distributions of ...

Dynamic Hierarchical Convolutional Attention Network for Recognizing Motor Imagery Intention.

IEEE transactions on cybernetics
The neural activity patterns of localized brain regions are crucial for recognizing brain intentions. However, existing electroencephalogram (EEG) decoding models, especially those based on deep learning, predominantly focus on global spatial feature...

HATNet: EEG-Based Hybrid Attention Transfer Learning Network for Train Driver State Detection.

IEEE transactions on cybernetics
Electroencephalography (EEG) is widely utilized for train driver state detection due to its high accuracy and low latency. However, existing methods for driver status detection rarely use the rich physiological information in EEG to improve detection...

Enhancing motor imagery EEG classification with a Riemannian geometry-based spatial filtering (RSF) method.

Neural networks : the official journal of the International Neural Network Society
Motor imagery (MI) refers to the mental simulation of movements without physical execution, and it can be captured using electroencephalography (EEG). This area has garnered significant research interest due to its substantial potential in brain-comp...

Functional connectivity anomalies in medication-naive children with ADHD: Diagnostic potential, symptoms interpretation, and a mediation model.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVE: To identify reliable electroencephalography (EEG) biomarkers for attention deficit/hyperactivity disorder (ADHD) by investigating anomalous functional connectivity patterns and their clinical relevance.

Brain-Computer Interface-controlled Upper Limb Robotic System for Enhancing Daily Activities in Stroke Patients.

Journal of visualized experiments : JoVE
This study introduces a Brain-Computer Interface (BCI)-controlled upper limb assistive robot for post-stroke rehabilitation. The system utilizes electroencephalogram (EEG) and electrooculogram (EOG) signals to help users assist upper limb function in...

Integrative neurorehabilitation using brain-computer interface: From motor function to mental health after stroke.

Bioscience trends
Stroke remains a leading cause of mortality and long-term disability worldwide, frequently resulting in impairments in motor control, cognition, and emotional regulation. Conventional rehabilitation approaches, while partially effective, often lack i...

Deep learning-based EEG source imaging is robust under varying electrode configurations.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVES: Previous research has underscored the necessity of high-density EEG for accurate and reliable EEG source imaging (ESI) results with conventional ESI methods, limiting their utility in clinical settings with only low-density EEG available....