Predicting Motor Imagery Performance From Resting-State EEG Using Dynamic Causal Modeling.

ABSTRACT: Motor imagery-based brain-computer interfaces (MI-BCIs) send commands to a computer using the brain activity registered when a subject imagines-but does not perform-a given movement. However, inconsistent MI-BCI performance occurs in variations of brain signals across subjects and experiments; this is considered to be a significant problem in practical BCI. Moreover, some subjects exhibit a phenomenon referred to as "BCI-inefficiency," in which they are unable to generate brain signals for BCI control. These subjects have significant difficulties in using BCI. The primary goal of this study is to identify the connections of the resting-state network that affect MI performance and predict MI performance using these connections. We used a public database of MI, which includes the results of

SUBMITTER: Lee M 

PROVIDER: S-EPMC7438792 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature