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Int J Neural Syst. 2019 Feb;29(1):1850014. doi: 10.1142/S0129065718500144. Epub 2018 Apr 2.

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

Author information

1
1 Inria, Aramis project-team, F-75013, Paris, France.
2
2 Institut du Cerveau et de la Moelle épinière, ICM, F-75013, Paris, France.
3
3 Inserm, U 1127, F-75013, Paris, France.
4
4 CNRS, UMR 7225, F-75013, Paris, France.
5
5 Sorbonne Université, F-75013, Paris, France.
6
6 Centre de NeuroImagerie de Recherche - CENIR, Centre de Recherche de l'Institut du Cerveau et de la Moelle Epinère, Université Pierre et Marie Curie-Paris 6 UMR-S975, INSERM U975, CNRS UMR7225, Groupe Hospitalier Pitié-Salpêtrière, Paris, France.
7
7 Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA.
8
8 Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA.
9
9 Department of Physics, University of Pennsylvania, Philadelphia, PA 19104, USA.
10
10 Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA.

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.

KEYWORDS:

Classifier fusion; EEG; MEG; brain–computer interface; motor imagery

PMID:
29768971
DOI:
10.1142/S0129065718500144
[Indexed for MEDLINE]

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