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

Marie-Constance Corsi, Mario Chavez, Denis Schwartz, Laurent Hugueville, Ankit N. Khambhati, Danielle S. Bassett, Fabrizio De Vico Fallani
2018 arXiv   pre-print
We propose a fusion approach that combines features from simultaneously recorded electroencephalographic (EEG) and magnetoencephalographic (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
more » ... tage of considering multimodal approaches as complementary tools for improving the impact of non-invasive BCIs.
arXiv:1711.07258v2 fatcat:jagr3rkrkbf3pe7u6yunafn6hm