Objective Electroencephalogram (EEG) based motor imagery (MI) classification is an
important aspect in brain-machine interfaces (BMIs) which bridges between neural system …

This work aims to improve EEG signal binary and multiclass classification for real-time BCI
applications. Therefore, our paper discusses the results of a new real-time approach that …

Electroencephalogram (EEG) signals classification, which are important for brain computer
interfaces (BCI) systems, is extremely difficult due to the inherent complexity and tendency to …

This work aims to develop a brain–computer interface (BCI) system based on
electroencephalogram (EEG) signals, that is capable of remote controlling rehabilitation …

This paper presents an advanced approach for EEG artifact removal and motor imagery
classification using a combination of Four Class Iterative Filtering and Filter Bank Common …

Objective. Electroencephalogram (EEG) based motor imagery (MI) classification is an
important aspect in brain-machine interfaces (BMIs) which bridges between neural system …

Most of the probabilistic mixture models perform clustering by observing the eigenvectors of
the data sample and these models rely on the layout of features. Clustering ensemble based …

An electroencephalogram (EEG)-based brain–computer interface (BCI) provides a
communication link between the brain and an external device. The classification of EEG …

The Gaussian mixture model (GMM) is utilized to illustrate the possibility of applying
probabilistic models to data clustering and provide an efficient method for processing EEG …

Abstract Motor Imagery Brain-Computer Interfaces (MI-BCIs) are systems based on AI that
collect patterns of brain activities in mental movement and translate these movements …