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

International Journal of Neural Systems
Marie-Constance CorsiFabrizio De Vico Fallani

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.

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Citations

Nov 13, 2019·International Journal of Neural Systems·Pramod GaurGirijesh Prasad
Mar 3, 2020·International Journal of Neural Systems·Huirang HouQinghao Meng
May 30, 2020·International Journal of Neural Systems·Francisco J Martinez-MurciaJuan Luis Luque
Sep 13, 2020·International Journal of Neural Systems·Manuel GrañaXavier Garmendia
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Nov 5, 2020·Journal of Neural Engineering·Juliana Gonzalez-AstudilloFabrizio De Vico Fallani
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Mar 17, 2021·Journal of Neural Engineering·Marie-Constance CorsiFabrizio De Vico Fallani
May 11, 2021·International Journal of Neural Systems·Yubo WangLiyu Huang
Mar 30, 2021·International Journal of Neural Systems·Xiaoyi Jiang
May 18, 2021·Frontiers in Human Neuroscience·So-Hyeon YooKeum-Shik Hong
Jun 29, 2021·International Journal of Neural Systems·Jing JinAndrzej Cichocki
Jun 26, 2021·International Journal of Neural Systems·Qin TaoPeng Xu
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Aug 12, 2021·International Journal of Neural Systems·Hao SunAndrzej Cichocki
Aug 12, 2021·International Journal of Neural Systems·Yuxi ShiNatsue Yoshimura
Sep 24, 2021·International Journal of Neural Systems·Jana ZweeringsKlaus Mathiak
Oct 1, 2021·International Journal of Neural Systems·Ujwal ChaudharyNiels Birbaumer

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