Objective. Electroencephalography (EEG) analysis has been an important tool in
neuroscience with applications in neuroscience, neural engineering (eg Brain–computer …

Context. Electroencephalography (EEG) is a complex signal and can require several years
of training, as well as advanced signal processing and feature extraction methodologies to …

Background Data augmentation (DA) has recently been demonstrated to achieve
considerable performance gains for deep learning (DL)—increased accuracy and stability …

In recent years, brain-based technologies that capitalise on human abilities to facilitate
human–system/robot interactions have been actively explored, especially in brain robotics …

Machine learning and its subfield deep learning techniques provide opportunities for the
development of operator mental state monitoring, especially for cognitive workload …

Classification of electroencephalogram (EEG) is a key approach to measure the rhythmic
oscillations of neural activity, which is one of the core technologies of brain-computer …

Deep learning has achieved excellent performance in a wide range of domains, especially
in speech recognition and computer vision. Relatively less work has been done for …

Fatigue driving can easily lead to road traffic accidents and bring great harm to individuals
and families. Recently, electroencephalography‐(EEG‐) based physiological and brain …

The quality and quantity of training data are crucial to the performance of a deep-learning-
based brain-computer interface (BCI) system. However, it is not practical to record EEG data …

Deep learning for electroencephalogram-based classification is confronted with data
scarcity, due to the time-consuming and expensive data collection procedure. Data …