Functional disconnection of associative cortical areas predicts performance during BCI training.

Abstract:

:Brain-computer interfaces (BCIs) have been largely developed to allow communication, control, and neurofeedback in human beings. Despite their great potential, BCIs perform inconsistently across individuals and the neural processes that enable humans to achieve good control remain poorly understood. To address this question, we performed simultaneous high-density electroencephalographic (EEG) and magnetoencephalographic (MEG) recordings in a motor imagery-based BCI training involving a group of healthy subjects. After reconstructing the signals at the cortical level, we showed that the reinforcement of motor-related activity during the BCI skill acquisition is paralleled by a progressive disconnection of associative areas which were not directly targeted during the experiments. Notably, these network connectivity changes reflected growing automaticity associated with BCI performance and predicted future learning rate. Altogether, our findings provide new insights into the large-scale cortical organizational mechanisms underlying BCI learning, which have implications for the improvement of this technology in a broad range of real-life applications.

journal_name

Neuroimage

journal_title

NeuroImage

authors

Corsi MC,Chavez M,Schwartz D,George N,Hugueville L,Kahn AE,Dupont S,Bassett DS,De Vico Fallani F

doi

10.1016/j.neuroimage.2019.116500

subject

Has Abstract

pub_date

2020-04-01 00:00:00

pages

116500

eissn

1053-8119

issn

1095-9572

pii

S1053-8119(19)31091-2

journal_volume

209

pub_type

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