Unsupervised adaptation of an ECoG based brain-computer interface using neural correlates of task performance.

ABSTRACT: Brain-computer interfaces (BCIs) translate brain signals into commands to external effectors, and mainly target severely disabled users. The usability of BCIs may be improved by reducing their major constraints, such as the necessity for special training sessions to initially calibrate and later keep up to date the neural signal decoders. In this study, we show that it is possible to train and update BCI decoders during free use of motor BCIs. In addition to the neural signal decoder controlling effectors (control decoder), one more classifier is proposed to detect neural correlates of BCI motor task performances (MTP). MTP decoders reveal whether the actions performed by BCI effectors matched the user's intentions. The combined outputs of MTP and control decoders allow forming training da

SUBMITTER: Rouanne V 

PROVIDER: S-EPMC9734147 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature