Detection of Error-Related Potentials in Stroke Patients from EEG Using an Artificial Neural Network.

ABSTRACT: Error-related potentials (ErrPs) have been proposed as a means for improving brain-computer interface (BCI) performance by either correcting an incorrect action performed by the BCI or label data for continuous adaptation of the BCI to improve the performance. The latter approach could be relevant within stroke rehabilitation where BCI calibration time could be minimized by using a generalized classifier that is continuously being individualized throughout the rehabilitation session. This may be achieved if data are correctly labelled. Therefore, the aims of this study were: (1) classify single-trial ErrPs produced by individuals with stroke, (2) investigate test-retest reliability, and (3) compare different classifier calibration schemes with different classification methods (artificial n

SUBMITTER: Usama N 

PROVIDER: S-EPMC8472485 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature