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Integrating EEG and MEG signals to improve motor imagery classification in brain-computer interfaces
[article]
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
arXiv:1711.07258v2
fatcat:jagr3rkrkbf3pe7u6yunafn6hm