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

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Abstract

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.

Journal

  • Frontiers in Neuroscience

    Frontiers in Neuroscience 8(417), 1-9, 2014-12-12

Codes

  • NII Article ID (NAID)
    120005549829
  • Text Lang
    ENG
  • Article Type
    journal article
  • ISSN
    1662-4548
  • Data Source
    IR 
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