High-accuracy brain-machine interfaces using feedback information.

ABSTRACT: Sensory feedback is very important for movement control. However, feedback information has not been directly used to update movement prediction model in the previous BMI studies, although the closed-loop BMI system provides the visual feedback to users. Here, we propose a BMI framework combining image processing as the feedback information with a novel prediction method. The feedback-prediction algorithm (FPA) generates feedback information from the positions of objects and modifies movement prediction according to the information. The FPA predicts a target among objects based on the movement direction predicted from the neural activity. After the target selection, the FPA modifies the predicted direction toward the target and modulates the magnitude of the predicted vector to easily reach

SUBMITTER: Yeom HG 

PROVIDER: S-EPMC4116198 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature