A brain–computer interface (BCI) enables a user to communicate with a computer directly
using brain signals. The most common noninvasive BCI modality, electroencephalogram …

Currently, the most used method to measure brain activity under a non-invasive procedure is
the electroencephalogram (EEG). This is because of its high temporal resolution, ease of …

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 performance of brain-computer interfaces (BCIs) improves with the amount of available
training data; the statistical distribution of this data, however, varies across subjects as well …

This paper presents a new classification framework for brain-computer interface (BCI) based
on motor imagery. This framework involves the concept of Riemannian geometry in the …

The use of spatial covariance matrix as a feature is investigated for motor imagery EEG-
based classification in brain–computer interface applications. A new kernel is derived by …

Recognition of electroencephalographic (EEG) signals highly affect the efficiency of non-
invasive brain-computer interfaces (BCIs). While recent advances of deep-learning (DL) …

Cross-subject variability problems hinder practical usages of Brain-Computer Interfaces.
Recently, deep learning has been introduced into the BCI community due to its better …

A brain–computer interface (BCI) can connect humans and machines directly and has
achieved successful applications in the past few decades. Many new BCI paradigms and …

Emotion plays a vital role in human daily life, and EEG signals are widely used in emotion
recognition. Due to individual variability, training a generic emotion recognition model …