Integrating EEG and MEG Signals to Improve Motor Imagery Classification in Brain–Computer Interface-Reference-Cited by-同舟云学术

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

Published:2019-01-10 Issue:01 Volume:29 Page:1850014
ISSN:0129-0657
Container-title:International Journal of Neural Systems
language:en
Short-container-title:Int. J. Neur. Syst.

Author:

Corsi Marie-Constance¹²³⁴⁵,Chavez Mario⁴,Schwartz Denis⁶,Hugueville Laurent⁶,Khambhati Ankit N.⁷,Bassett Danielle S.⁷⁸⁹¹⁰,De Vico Fallani Fabrizio¹²³⁴⁵

Affiliation:

1. Inria, Aramis project-team, F-75013, Paris, France

2. Institut du Cerveau et de la Moelle épinière, ICM, F-75013, Paris, France

3. Inserm, U 1127, F-75013, Paris, France

4. CNRS, UMR 7225, F-75013, Paris, France

5. Sorbonne Université, F-75013, Paris, France

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. Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA

8. Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA

9. Department of Physics, University of Pennsylvania, Philadelphia, PA 19104, USA

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.

Publisher

World Scientific Pub Co Pte Lt

Subject

Computer Networks and Communications,General Medicine

Link

https://www.worldscientific.com/doi/pdf/10.1142/S0129065718500144

Reference86 articles.

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