Decoding electroencephalography (EEG) signals with deep learning remains challenging due to strong interand intra-individual variability, low signal-to-noise ratio, and the absence of an optimal architecture. While deep neural networks have …
In this chapter, we present the main characteristics of electroencephalography (EEG) and magnetoencephalography (MEG). More specifically, this chapter is dedicated to the presentation of the data, the way they can be acquired and analyzed. Then, we …
In the recent years, brain computer interfaces (BCI) using motor imagery have shown some limitations regarding the quality of control. In an effort to improve this promising technology, some studies intended to develop hybrid BCI with other …
We adopted a fusion approach that combines features from simultaneously recorded electroencephalogram (EEG) and magnetoencephalogram (MEG) signals to improve classification performances in motor imagery-based brain-computer interfaces (BCIs). We …