Medical data refers to health-related information associated with regular patient care or as
part of a clinical trial program. There are many categories of such data, such as clinical …

Electroencephalogram (EEG) is a non-invasive technique to record bioelectrical signals.
Integrating supervised deep learning techniques with EEG signals has recently facilitated …

Recently, deep learning-based electroencephalogram (EEG) analysis and decoding have
attracted widespread attention for monitoring the clinical condition of users and identifying …

Over the last decade, deep learning applications in biomedical research have exploded,
demonstrating their ability to often outperform previous machine learning approaches in …

Electroencephalogram (EEG) signals are widely used in the field of emotion recognition
since it is resistant to camouflage and contains abundant physiological information …

Due to the abundant neurophysiological information in the electroencephalogram (EEG)
signal, EEG signals integrated with deep learning methods have gained substantial traction …

Motor imagery (MI) based brain-computer interfaces (BCIs) enable the direct control of
external devices through the imagined movements of various body parts. Unlike previous …

Thanks to the fast evolution of electroencephalography (EEG)-based brain-computer
interfaces (BCIs) and computing technologies, as well as the availability of large EEG …

Abstract Machine learning (ML) is widely used in classification tasks aimed at detecting
various cognitive states or neurological diseases using noninvasive electroencephalogram …

Self-supervised learning addresses the challenge encountered by many supervised
methods, ie the requirement of large amounts of annotated data. This challenge is …