Brain-machine interface to control a prosthetic arm with monkey ECoGs during periodic movements

Brain-machine interfaces (BMIs) are promising technologies for rehabilitation of upperlimb functions in patients with severe paralysis. We previously developed a BMI prostheticarm for a monkey implanted with electrocorticography(ECoG) electrodes, and trained it in a reaching task. The stability of the BMI prevente correct movements due to misclassification of ECoG patterns. As a trade-off for the stability, however, the latecy (the time gap between the monkey's actual motion and the prosthetic arm movement) was about 200ms. Therefore , in this study, We aimed to improve the response time of the BMI prosthetic arm. We focused on the generatio of a trigger event by decoding muscle activity in order to predict integrated electromyograms (iEMGS) from the ECoGs. We verified the achievability of our method by conducting a performance test of the achievability of our method by conducting performance test of the proposed with actual achieved iEMGS eliminated the time delay. in addition, We found that motor intention is better reflected by muscle activity estimated from brain activity rather tahn actual muscle activity. Therefore, We propose that using predicted iEMGS to guide arm movements results delay and excellent performance.