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

In this study we propose a new machine learning classification method to distinguish brain
activity patterns for healthy subjects. We used ElectroEncephaloGraphic (EEG) data …

We applied transcranial alternating current stimulation (tACS) to the primary motor cortex
(M1) at different frequencies during an index–thumb pinch-grip observation task. To estimate …

Imaginary motor tasks cause brain oscillations that can be detected through the analysis of
electroencephalographic (EEG) recordings. The imagination of hands movement allows …

To apply EEG-based brain-machine interfaces during rehabilitation, separating various
tasks during motor imagery (MI) and assimilating MI into motor execution (ME) are needed …

Motor imagery (MI) allows the design of self-paced brain–computer interfaces (BCIs), which
can potentially afford an intuitive and continuous interaction. However, the implementation of …

Brain-Computer Interfaces (BCIs) replace the natural nervous system outputs by artificial
ones that do not require the use of peripheral nerves, allowing people with severe motor …

In recent years, the field of Brain-Computer Interface (BCI) has experienced significant
growth, largely due to the availability of benchmark datasets and BCI competitions [1]–[3] …

In this dissertation, I focused on EEG classification for high-level cognitive activities. I first
reviewed the machine learning and deep learning algorithms that already have been …

Résumé Les interfaces cerveau-ordinateur (ou BCI en anglais pour Brain-Computer
Interfaces) mettent en place depuis le système nerveux central un circuit artificiel secondaire …