期刊
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
卷 29, 期 1, 页码 -出版社
WORLD SCIENTIFIC PUBL CO PTE LTD
DOI: 10.1142/S0129065718500144
关键词
Classifier fusion; EEG; MEG; brain-computer interface; motor imagery
资金
- French Program Investissements d'Avenir [ANR-10-IAIHU-06]
- Army Research Office [W911NF-14-1-0679]
- ANR-NIH CRCNS [ANR-15-NEUC-0006-02]
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.
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