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 …

The main purpose of this paper is to provide information on how to create a convolutional
neural network (CNN) for extracting features from EEG signals. Our task was to understand …

The success of deep learning (DL) methods in the Brain-Computer Interfaces (BCI) field for
classification of electroencephalographic (EEG) recordings has been restricted by the lack of …

Artifact Subspace Reconstruction (ASR) is an adaptive method for the online or offline
correction of artifacts comprising multichannel electroencephalography (EEG) recordings. It …

Abstract Motor Imagery Brain Computer Interface (MI-BCI) has become a promising
technology in the field of neurorehabilitation. However, the performance and computational …

Brain-computer interface (BCI) is a new promising technology for control and
communication, the BCI system aims to decode the measured brain activity into a command …

Objective. While promising for many applications, electroencephalography (EEG)-based
brain–computer interfaces (BCIs) are still scarcely used outside laboratories, due to a poor …

Riemannian geometry has been successfully used in many brain-computer interface (BCI)
classification problems and demonstrated superior performance. In this paper, for the first …