猴子PMd區腦電解碼抓握手勢及機械手實時控制

摘 要：過去的10年，腦機接口中對上肢有關的伸解碼取得了非常大的成功，這給殘障人士運動康復帶來了希望。但與日常生活息息相關的手部的抓握動作的研究卻很少涉及。當前，在解碼手勢方面有很多初步的工作，但是實時的抓握手勢的解碼工作還沒有被系統地研究過。該研究首先建立了基于非人靈長類動物的植入式腦機接口平臺，訓練獼猴完成伸抓動作并記錄PMd區的神經信號。通過FKNN算法異步解碼出4種抓握手勢和休息狀態。然后，利用共享控制策略驅動靈巧的機械手完成與猴子相同的動作。結果表明大部分PMd區的神經元對伸抓動作具有調和特性，利用PMd區的神經元的解碼正確率可以達到97.1%。在線控制模式中，猴子手的瞬時狀態能夠被成功解碼出來并用于機械手的控制，正確率可以達到85.1%。我們的研究為殘疾人士抓握運動的康復提供了新的思路和方法。

關鍵詞：抓握解碼 運動皮層 假肢手控制 實時 腦機接口

Abstract：Brain machine interfaces （BMIs） have demonstrated lots of successful arm-related reach decoding in past decades， which provide 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.

Key Words：Grasp decoding； Monkey motor cortex； Prosthetic hand control； Real-time； Brain machine interfaces

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