Prediction of movement intention using connectivity within motor-related network: An electrocorticography study.

ABSTRACT: Most brain-machine interface (BMI) studies have focused only on the active state of which a BMI user performs specific movement tasks. Therefore, models developed for predicting movements were optimized only for the active state. The models may not be suitable in the idle state during resting. This potential maladaptation could lead to a sudden accident or unintended movement resulting from prediction error. Prediction of movement intention is important to develop a more efficient and reasonable BMI system which could be selectively operated depending on the user's intention. Physical movement is performed through the serial change of brain states: idle, planning, execution, and recovery. The motor networks in the primary motor cortex and the dorsolateral prefrontal cortex are involved in

SUBMITTER: Kang BK 

PROVIDER: S-EPMC5783365 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature