Objective. Most current electroencephalography (EEG)-based brain–computer interfaces
(BCIs) are based on machine learning algorithms. There is a large diversity of classifier …

Although promising from numerous applications, current brain-computer interfaces (BCIs)
still suffer from a number of limitations. In particular, they are sensitive to noise, outliers and …

Despite its short history, the use of Riemannian geometry in brain-computer interface (BCI)
decoding is currently attracting increasing attention, due to accumulating documentation of …

Covariance matrices have attracted attention for machine learning applications due to their
capacity to capture interesting structure in the data. The main challenge is that one needs to …

Challenges for the next generation of Brain Computer Interfaces (BCI) are to mitigate the
common sources of variability (electronic, electrical, biological) and to develop online and …

The motor imagery (MI) classification has been a prominent research topic in brain–
computer interfaces (BCIs) based on electroencephalography (EEG). Over the past few …

Symmetric positive definite (SPD) matrices, eg covariance matrices, are ubiquitous in
machine learning applications. However, because their size grows as n^ 2 (where n is the …

In recent years, researchers have paid more attention to the hybrid applications of quantum
computing and brain-computer interfaces. With the development of neural technology and …

This paper presents an empirical comparison of covariance matrix averaging methods for
EEG signal classification. Indeed, averaging EEG signal covariance matrices is a key step in …

Introduction: A brain-computer interface (BCI) is a rapidly growing cutting-edge technology
in which a communication pathway is built between the human brain and computer. The BCI …