Section: New Results

Integrating EEG and MEG signals to improve motor imagery classification in brain-computer interfaces

Participants : Marie-Constance Corsi, Mario Chavez, Denis Schwartz, Laurent Hugueville, Ankit Khambhati, Danielle Bassett, Fabrizio de Vico Fallani [Correspondant] .

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 applied our approach to a group of 15 healthy subjects and found a significant classification performance enhancement as compared to standard single-modality approaches in the alpha and beta bands. Taken together, our findings demonstrate the advantage of considering multimodal approaches as complementary tools for improving the impact of noninvasive BCIs.

More details in [10].