Electroencephalography (EEG) is a non-invasive technique used to record the brain's
evoked and induced electrical activity from the scalp. Artificial intelligence, particularly …

Objective. Processing strategies are analyzed with respect to the classification of
electroencephalographic signals related to brain-computer interfaces (BCIs) based on motor …

Objective. Brain-computer interface (BCI) aims to establish communication paths between
the brain processes and external devices. Different methods have been used to extract …

Motor imagery brain-computer interface (MI-BCI) provides a novel way for human-computer
interaction. Traditional neural networks often use serial structure to extract spatial features …

Classification of electroencephalogram-based motor imagery (MI-EEG) tasks is crucial in
brain–computer interface (BCI). EEG signals require a large number of channels in the …

This review article provides a deep insight into the Brain–Computer Interface (BCI) and the
application of Machine Learning (ML) technology in BCIs. It investigates the various types of …

Abstract In Brain Computer Interface (BCI), achieving a reliable motor‐imagery classification
is a challenging task. The set of discriminative and relevant feature vectors plays a crucial …

Electroencephalographic measurement of cortical activity subserving motor behavior varies
among different individuals, restricting the potential of brain computer interfaces (BCIs) …

Many studies applying Brain-Computer Interfaces (BCIs) based on Motor Imagery (MI) tasks
for rehabilitation have demonstrated the important role of detecting the Event-Related …

Recently, more and more studies have begun to use deep learning to decode and classify
EEG signals. The use of deep learning has led to an increase in the classification accuracy …