Training in Use of Brain-Machine Interface-Controlled Robotic Hand Improves Accuracy Decoding Two Types of Hand Movements.

ABSTRACT: Objective: Brain-machine interfaces (BMIs) are useful for inducing plastic changes in cortical representation. A BMI first decodes hand movements using cortical signals and then converts the decoded information into movements of a robotic hand. By using the BMI robotic hand, the cortical representation decoded by the BMI is modulated to improve decoding accuracy. We developed a BMI based on real-time magnetoencephalography (MEG) signals to control a robotic hand using decoded hand movements. Subjects were trained to use the BMI robotic hand freely for 10 min to evaluate plastic changes in the cortical representation due to the training. Method: We trained nine young healthy subjects with normal motor function. In open-loop conditions, they were instructed to grasp or open the

SUBMITTER: Fukuma R 

PROVIDER: S-EPMC6050372 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature