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

Marie-Constance Corsi (1) , Mario Chavez (2) , Denis Schwartz (3) , Laurent Hugueville (3) , Ankit Khambhati (4) , Danielle S. Bassett (4) , Fabrizio de Vico Fallani (1)

1 ARAMIS - Algorithms, models and methods for images and signals of the human brain = Algorithmes, modèles et méthodes pour les images et les signaux du cerveau humain [ICM Paris]
2 ICM - Institut du Cerveau = Paris Brain Institute
3 CENIR - Plateforme de neuroimagerie CENIR = Center for NeuroImaging Research-Human MRI Neuroimaging core facility for clinical research [ICM Paris]
4 University of Pennsylvania

We adopted a fusion approach that combines features from simultaneously recorded electroencephalographic (EEG) and magnetoencephalographic (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 non-invasive BCIs.

Mots clés: MEG; Motor imagery; Brain-computer interface; EEG; Classifier fusion

Domaines: Traitement du signal et de l'image [eess.SP]; Neurosciences

Marie-Constance Corsi, Mario Chavez, Denis Schwartz, Laurent Hugueville, Ankit Khambhati, et al.. Integrating EEG and MEG Signals to Improve Motor Imagery Classification in Brain–Computer Interface. International Journal of Neural Systems, 2018, ⟨10.1142/S0129065718500144⟩. ⟨hal-01893132⟩