Error-Related Potentials in Reinforcement Learning-Based Brain-Machine Interfaces.

ABSTRACT: The human brain has been an object of extensive investigation in different fields. While several studies have focused on understanding the neural correlates of error processing, advances in brain-machine interface systems using non-invasive techniques further enabled the use of the measured signals in different applications. The possibility of detecting these error-related potentials (ErrPs) under different experimental setups on a single-trial basis has further increased interest in their integration in closed-loop settings to improve system performance, for example, by performing error correction. Fewer works have, however, aimed at reducing future mistakes or learning. We present a review focused on the current literature using non-invasive systems that have combined the ErrPs informati

SUBMITTER: Xavier Fidencio A 

PROVIDER: S-EPMC9263570 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature