Morishita Soichiro, Sato Keita, Watanabe Hidenori, Nishimura Yukio, Isa Tadashi, Kato Ryu, Nakamura Tatsuhiro, Yokoi Hiroshi
Brain Science Inspired Life Support Research Center, The University of Electro-Communications Chofu, Japan.
Department of Mechanical Engineering and Intelligent Systems, The University of Electro-Communications Chofu, Japan.
Front Neurosci. 2014 Dec 12;8:417. doi: 10.3389/fnins.2014.00417. eCollection 2014.
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-off for 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.
脑机接口（BMI）是用于严重瘫痪患者上肢功能康复的有前景的技术。我们之前为一只植入了皮层脑电图（ECoG）电极的猴子开发了一种BMI假臂，并对其进行了伸手任务训练。BMI的稳定性防止了由于ECoG模式误分类导致的错误动作。然而，作为稳定性的一种权衡，延迟（猴子实际动作与假臂动作之间的时间间隔）约为200毫秒。因此，在本研究中，我们旨在提高BMI假臂的响应时间。我们专注于通过解码肌肉活动来生成触发事件，以便从ECoG预测整合肌电图（iEMG）。我们通过使用实际获得的iEMG而非预测的iEMG对所提出的方法进行性能测试，验证了我们方法的可实现性。我们的结果证实，使用预测iEMG的所提出方法消除了时间延迟。此外，我们发现运动意图通过从大脑活动估计的肌肉活动比实际肌肉活动能得到更好的反映。因此，我们提出使用预测iEMG来指导假臂运动可导致最小延迟和出色性能。
