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 …

Natural communication often occurs in dialogue, differentially engaging auditory and
sensorimotor brain regions during listening and speaking. However, previous attempts to …

Objective. Brain–computer interfaces (BCI) are becoming important tools for assistive
technology, particularly through the use of motor imagery (MI) for aiding task completion …

This paper explores the well-known identification of the manifold of rank p positive-
semidefinite matrices of size n with the quotient of the set of full-rank n-by-p matrices by the …

Electroencephalographic (EEG) recordings are contaminated by instrumental,
environmental, and biological artifacts, resulting in low signal-to-noise ratio. Artifact …

On image data, data augmentation is becoming less relevant due to the large amount of
available training data and regularization techniques. Common approaches are moving …

The main objective of this paper is to use deep neural networks to decode the
electroencephalography (EEG) signals evoked when individuals perceive four types of …

ABSTRACT Electroencephalography (EEG)-based brain–computer interface (BCI) systems
infer brain signals recorded via EEG without using common neuromuscular pathways. User …