Recent research has reached a consensus on the feasibility of motor imagery brain-
computer interface (MI-BCI) for different applications, especially in stroke rehabilitation. Most …

Electroencephalography (EEG) is increasingly important in Brain-Computer Interface (BCI)
systems due to its portability and simplicity. In this paper, we provide a comprehensive …

Objective. Learning the structures and unknown correlations of a motor imagery
electroencephalogram (MI-EEG) signal is important for its classification. It is also a major …

Feature selection is an important step in building classifiers for high-dimensional data
problems, such as EEG classification for BCI applications. This paper proposes a new …

A review of physical and mathematical methods for reconstructing the functional networks of
the brain based on recorded brain activity is presented. Various methods are considered, as …

Electroencephalography (EEG) datasets are often small and high dimensional, owing to
cumbersome recording processes. In these conditions, powerful machine learning …

In this paper, we present an optimal channel selection method to improve common spatial
pattern (CSP) related features for motor imagery (MI) classification. In contrast to existing …

In recent years, most of the researchers are develo** brain–computer interface (BCI)
applications for the physically disabled to be able to interconnect with peripheral devices …

The development of new approaches to detect motor-related brain activity is key in many
aspects of science, especially in brain–computer interface applications. Even though some …

In this study, cognitive and behavioral emotion regulation strategies (ERS) are classified by
using machine learning models driven by a new local EEG complexity approach so called …