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

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 …

The use of transfer learning in brain-computer interfaces (BCIs) has potential applications.
As electroencephalogram (EEG) signals vary among different paradigms and subjects …

Objective: Relying on the idea that functional connectivity provides important insights on the
underlying dynamic of neuronal interactions, we propose a novel framework that combines …

In brain–computer interfaces (BCIs), the typical models of the EEG observations usually lead
to a poor estimation of the trial covariance matrices, given the high non-stationarity of the …

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

Objective. This study analyzed detection (movement vs. non-movement) and classification
(different types of movements) to decode upper-limb movement volitions in a pseudo-online …

Objective. To date, a comprehensive comparison of Riemannian decoding methods with
deep convolutional neural networks for EEG-based brain–computer interfaces remains …

Objective. This study conduct an extensive Brain-computer interfaces (BCI) reproducibility
analysis on open electroencephalography datasets, aiming to assess existing solutions and …

Designing brain–computer interface (BCI) experiments requires knowledge in many different
disciplines: from neurosciences to signal processing and machine learning, through …