猴子PMd区脑电解码抓握手势及机械手实时控制

Decoding Grasp Movement from Monkey Premotor Cortex for Real-time Prothetic Hand Control

过去的10年,脑机接口中对上肢有关的伸解码取得了非常大的成功,这给残障人士运动康复带来了希望。但与日常生活息息相关的手部的抓握动作的研究却很少涉及。当前,在解码手势方面有很多初步的工作,但是实时的抓握手势的解码工作还没有被系统地研究过。该研究首先建立了基于非人灵长类动物的植入式脑机接口平台,训练猕猴完成伸抓动作并记录PMd区的神经信号。通过FKNN算法异步解码出4种抓握手势和休息状态。然后,利用共享控制策略驱动灵巧的机械手完成与猴子相同的动作。结果表明大部分PMd区的神经元对伸抓动作具有调和特性,利用PMd区的神经元的解码正确率可以达到97.1%。在线控制模式中,猴子手的瞬时状态能够被成功解码出来并用于机械手的控制,正确率可以达到85.1%。我们的研究为残疾人士抓握运动的康复提供了新的思路和方法。

Brain machine interfaces （BMIs） have demonstrated lots of successful arm-related reach decoding in past decades, which provide＆amp;nbsp;a new hope for restoring the lost motor functions for the disabled. On the other hand, the more sophisticated hand grasp movement, which is more fundamental and crucial for daily life, was less referred. Current state of arts has specified some grasp related brain areas and offline decoding results; however, online decoding grasp movement and real-time neuroprosthetic control have not been systematically investigated. In this study, we obtained neural data from the dorsal premotor cortex （PMd） when monkey reaching and grasping one of four differently shaped objects following visual cues. The four grasp gesture types with an additional resting state were classified asynchronously using a fuzzy k-nearest neighbor model, and an artificial hand was controlled online using a shared control strategy. The results showed that most of the neurons in PMd are tuned by reach and grasp movement, using which we get a high average offline decoding accuracy of 97.1%. In the online demonstration, the instantaneous status of monkey grasping could be extracted successfully to control the artificial hand, with an event-wise accuracy of 85.1%. Overall, our results inspect the neural firing along the time course of grasp and for the first time enables asynchronous neural control of a prosthetic hand, which underline a feasible hand neural prosthesis in BMIs.