Teaching brain-machine interfaces as an alternative paradigm to neuroprosthetics control.

ABSTRACT: Brain-machine interfaces (BMI) usually decode movement parameters from cortical activity to control neuroprostheses. This requires subjects to learn to modulate their brain activity to convey all necessary information, thus imposing natural limits on the complexity of tasks that can be performed. Here we demonstrate an alternative and complementary BMI paradigm that overcomes that limitation by decoding cognitive brain signals associated with monitoring processes relevant for achieving goals. In our approach the neuroprosthesis executes actions that the subject evaluates as erroneous or correct, and exploits the brain correlates of this assessment to learn suitable motor behaviours. Results show that, after a short user's training period, this teaching BMI paradigm operated three different

SUBMITTER: Iturrate I 

PROVIDER: S-EPMC4564803 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature