Electroencephalography (EEG)-based brain-computer interfaces (BCIs), particularly those
using motor-imagery (MI) data, have the potential to become groundbreaking technologies …

Background and objective: We have cast the net into the ocean of knowledge to retrieve the
latest scientific research on deep learning methods for physiological signals. We found 53 …

Objective. Signal classification is an important issue in brain computer interface (BCI)
systems. Deep learning approaches have been used successfully in many recent studies to …

Motor imagery (MI) based brain–computer interface (BCI) aims to provide a means of
communication through the utilization of neural activity generated due to kinesthetic …

Abstract Brain Computer Interfaces (BCIs) and Extended Reality (XR) have seen significant
advances as independent disciplines over the past 50 years. XR has been developed as an …

Motor imagery classification is an important topic in brain–computer interface (BCI) research
that enables the recognition of a subject's intension to, eg, implement prosthesis control. The …

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

Successful applications of brain-computer interface (BCI) approaches to motor imagery (MI)
are still limited. In this paper, we propose a classification framework for MI …

The Common Spatial Pattern (CSP) algorithm is an effective and popular method for
classifying 2-class motor imagery electroencephalogram (EEG) data, but its effectiveness …

The BCI competition IV stands in the tradition of prior BCI competitions that aim to provide
high quality neuroscientific data for open access to the scientific community. As experienced …