Integrating EEG and MEG Signals to Improve Motor Imagery Classification in Brain-Computer Interface.

TitleIntegrating EEG and MEG Signals to Improve Motor Imagery Classification in Brain-Computer Interface.
Publication TypeJournal Article
Year of Publication2019
AuthorsCorsi, M-C, Chavez, M, Schwartz, D, Hugueville, L, Khambhati, AN, Bassett, DS, Fallani, FDe Vico
JournalInt J Neural Syst
Volume29
Issue1
Pagination1850014
Date Published2019 Feb
ISSN1793-6462
KeywordsAdult, Alpha Rhythm, Beta Rhythm, Brain-Computer Interfaces, Cerebral Cortex, Electroencephalography, Humans, Imagination, Magnetoencephalography, Motor Activity, Signal Processing, Computer-Assisted, Young Adult
Abstract

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.

DOI10.1142/S0129065718500144
Alternate JournalInt J Neural Syst
PubMed ID29768971