TY - JOUR
T1 - Brain-machine interface to control a prosthetic arm with monkey ECoGs during periodic movements
AU - Morishita, Soichiro
AU - Sato, Keita
AU - Watanabe, Hidenori
AU - Nishimura, Yukio
AU - Isa, Tadashi
AU - Kato, Ryu
AU - Nakamura, Tatsuhiro
AU - Yokoi, Hiroshi
N1 - Publisher Copyright:
© 2014 Morishita, Sato, Watanabe, Nishimura, Isa, Kato, Nakamura and Yokoi.
PY - 2014
Y1 - 2014
N2 - Brain-machine interfaces (BMIs) are promising technologies for rehabilitation of upper limb functions in patients with severe paralysis. We previously developed a BMI prosthetic arm for a monkey implanted with electrocorticography (ECoG) electrodes, and trained it in a reaching task. The stability of the BMI prevented incorrect movements due to misclassification of ECoG patterns. As a trade-offfor the stability, however, the latency (the time gap between the monkey's actual motion and the prosthetic arm movement) was about 200 ms. Therefore, in this study, we aimed to improve the response time of the BMI prosthetic arm. We focused on the generation 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 proposed method with actual achieved iEMGs instead of predicted iEMGs. Our results confirmed that the proposed method with predicted iEMGs eliminated the time delay. In addition, we found that motor intention is better reflected by muscle activity estimated from brain activity rather than actual muscle activity. Therefore, we propose that using predicted iEMGs to guide prosthetic arm movement results in minimal delay and excellent performance.
AB - Brain-machine interfaces (BMIs) are promising technologies for rehabilitation of upper limb functions in patients with severe paralysis. We previously developed a BMI prosthetic arm for a monkey implanted with electrocorticography (ECoG) electrodes, and trained it in a reaching task. The stability of the BMI prevented incorrect movements due to misclassification of ECoG patterns. As a trade-offfor the stability, however, the latency (the time gap between the monkey's actual motion and the prosthetic arm movement) was about 200 ms. Therefore, in this study, we aimed to improve the response time of the BMI prosthetic arm. We focused on the generation 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 proposed method with actual achieved iEMGs instead of predicted iEMGs. Our results confirmed that the proposed method with predicted iEMGs eliminated the time delay. In addition, we found that motor intention is better reflected by muscle activity estimated from brain activity rather than actual muscle activity. Therefore, we propose that using predicted iEMGs to guide prosthetic arm movement results in minimal delay and excellent performance.
KW - Brain-machine interfaces
KW - Electrocorticography
KW - Electromyography
KW - Prosthetic arm
KW - Reaching task
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U2 - 10.3389/fnins.2014.00417
DO - 10.3389/fnins.2014.00417
M3 - Article
AN - SCOPUS:84920624885
VL - 8
JO - Frontiers in Neuroscience
JF - Frontiers in Neuroscience
SN - 1662-4548
IS - DEC
M1 - 417
ER -