Real-time brain-machine interface in non-human primates achieves high-velocity prosthetic finger movements using a shallow feedforward neural network decoder.
Real-time brain-machine interface in non-human primates achieves high-velocity prosthetic finger movements using a shallow feedforward neural network decoder.
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DOI：
10.1038/s41467-022-34452-w
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发表时间：
2022-11-12
影响因子：
16.6
通讯作者：
Chestek, Cynthia A.
中科院分区：
文献类型：
--
作者：
Willsey, Matthew S.;Nason-Tomaszewski, Samuel R.;Ensel, Scott R.;Temmar, Hisham;Mender, Matthew J.;Costello, Joseph T.;Patil, Parag G.;Chestek, Cynthia A.
尽管在恢复运动功能的脑机接口方面取得了快速进展和兴趣，但假肢手指和肢体的性能尚未模仿原生功能。将大脑信号转换为假肢装置的控制信号的算法是实现快速和逼真手指运动的限制之一。为了实现更真实的手指运动，我们开发了一个浅层前馈神经网络来解码两只成年雄性恒河猴的实时两自由度手指运动。使用两步训练方法，引入重新校准的反馈意图训练（ReFIT）神经网络以进一步提高性能。在对两只动物进行的7天测试中，神经网络解码器具有更高的速度和更自然的手指运动，比代表当前标准的ReFIT卡尔曼滤波器的吞吐量增加了36%。本文介绍的神经网络解码器展示了在优于当前技术水平的上级水平上对连续运动的实时解码，并且可以提供使用神经网络来开发更自然的脑控假体的起点。尽管在恢复运动功能的脑机接口方面取得了快速进展和兴趣，但假肢手指和肢体的性能尚未模仿原生功能。在这里，作者证明了浅层神经网络解码器的性能优于当前的线性解码标准，并且能够实现更高速度的手指移动。
Despite the rapid progress and interest in brain-machine interfaces that restore motor function, the performance of prosthetic fingers and limbs has yet to mimic native function. The algorithm that converts brain signals to a control signal for the prosthetic device is one of the limitations in achieving rapid and realistic finger movements. To achieve more realistic finger movements, we developed a shallow feed-forward neural network to decode real-time two-degree-of-freedom finger movements in two adult male rhesus macaques. Using a two-step training method, a recalibrated feedback intention–trained (ReFIT) neural network is introduced to further improve performance. In 7 days of testing across two animals, neural network decoders, with higher-velocity and more natural appearing finger movements, achieved a 36% increase in throughput over the ReFIT Kalman filter, which represents the current standard. The neural network decoders introduced herein demonstrate real-time decoding of continuous movements at a level superior to the current state-of-the-art and could provide a starting point to using neural networks for the development of more naturalistic brain-controlled prostheses. Despite the rapid progress and interest in brain-machine interfaces that restore motor function, the performance of prosthetic fingers and limbs has yet to mimic native function. Here, the authors demonstrate that shallow-layer neural network decoders outperform and enable higher velocity finger movements than the current linear decoding standard.
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影响因子：
4
作者：
Sachs NA;Ruiz-Torres R;Perreault EJ;Miller LE
通讯作者：
Miller LE
影响因子：
28.1
作者：
Nason SR;Vaskov AK;Willsey MS;Welle EJ;An H;Vu PP;Bullard AJ;Nu CS;Kao JC;Shenoy KV;Jang T;Kim HS;Blaauw D;Patil PG;Chestek CA
通讯作者：
Chestek CA
影响因子：
25
作者：
Kurtzer, I;Herter, TM;Scott, SH
通讯作者：
Scott, SH
影响因子：
48
作者：
Pandarinath C;O'Shea DJ;Collins J;Jozefowicz R;Stavisky SD;Kao JC;Trautmann EM;Kaufman MT;Ryu SI;Hochberg LR;Henderson JM;Shenoy KV;Abbott LF;Sussillo D
通讯作者：
Sussillo D
影响因子：
16.2
作者：
Nason SR;Mender MJ;Vaskov AK;Willsey MS;Ganesh Kumar N;Kung TA;Patil PG;Chestek CA
通讯作者：
Chestek CA