中文摘要：

大脑机器接口(BMI ) 示威了大量成功的手臂相关的活动范围在过去的十年译码，它为恢复失去的马达提供新希望工作为停用。在另一方面，更复杂的手掌握运动，为日常生活更基本、关键，少些被提交。艺术的当前的状态规定了某掌握联系了大脑区域和离线的译码结果；然而，在网上译码掌握运动和即时 neuroprosthetic 控制系统地没被调查。在这研究，当猴子到达并且掌握跟随视觉暗示的四个不同地塑造的目标之一时，我们从背面的 premotor 外皮(PMd ) 获得了神经数据。有一个另外的休息状态的四种掌握手势类型用一个模糊 k 近邻居模型异步地被分类，并且一只人工的手用分享的控制策略在网上被控制。结果证明大多数在 PMd 的神经原被活动范围和掌握运动调节，使用我们哪个脱机得到高一般水准译码 97.1% 的精确性。在联机示范，猴子掌握的即时地位能成功地被提取控制人工的手，与 85.1% 的事件明智的精确性。总的来说，我们的结果检查沿着掌握并且第一次的路线启用一只修复术的手的异步的神经控制的时间的神经开火，它加重一只可行的手在 BMI 的神经修复术。

英文摘要：

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, us- ing 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.