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

DOI IR IR (HANDLE) HANDLE PubMed 3 Citations 19 References Open Access
  • Morishita, Soichiro
    Brain Science Inspired Life Support Research Center, The University of Electro-Communications
  • Sato, Keita
    Department of Mechanical Engineering and Intelligent Systems, The University of Electro-Communications
  • Watanabe, Hidenori
    Division of Behavioral Development, Department of Developmental Physiology, National Institute for Physiological Sciences
  • Nishimura, Yukio
    Division of Behavioral Development, Department of Developmental Physiology, National Institute for Physiological Sciences / Department of Physiological Sciences, School of Life Science, The Graduate University for Advanced Studies (SOKENDAI) / PRESTO, Japan Science and Technology Agency
  • Isa, Tadashi
    Division of Behavioral Development, Department of Developmental Physiology, National Institute for Physiological Sciences University for Advanced Studies (SOKENDAI) Department of Physiological Sciences, School of Life Science, The Graduate
  • Kato, Ryu
    Division of Systems Research, Department of Systems Design, Faculty of Engineering, The Yokohama National University
  • Nakamura, Tatsuhiro
    Integrative Brain Imaging Center, National Center of Neurology and Psychiatry
  • Yokoi, Hiroshi
    Department of Mechanical Engineering and Intelligent Systems, The University of Electro-Communications

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Description

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 trainedit in a reaching task. The stability of the BMI prevented incorrect movements due tomisclassification of ECoG patterns. As a trade-off for the stability, however, the latency(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 ofthe BMI prosthetic arm. We focused on the generation of a trigger event by decodingmuscle activity in order to predict integrated electromyograms (iEMGs) from the ECoGs.We verified the achievability of our method by conducting a performance test of theproposed method with actual achieved iEMGs instead of predicted iEMGs. Our resultsconfirmed that the proposed method with predicted iEMGs eliminated the time delay. Inaddition, we found that motor intention is better reflected by muscle activity estimatedfrom brain activity rather than actual muscle activity. Therefore, we propose that usingpredicted iEMGs to guide prosthetic arm movement results in minimal delay and excellentperformance.

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