Aggarwal Vikram, Singhal Girish, He Jiping, Schieber Marc H, Thakor Nitish V
Department of Biomedical Engineering at The Johns Hopkins University, Baltimore, MD, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2008;2008:1703-6. doi: 10.1109/IEMBS.2008.4649504.
It has been shown that Brain-Computer Interfaces (BCIs) involving closed-loop control of an external device, while receiving visual feedback, allows subjects to adaptively correct errors and improve the accuracy of control. Although closed-loop cortical control of gross arm movements has been demonstrated, closed-loop decoding of more dexterous movements such as individual fingers has not been shown. Neural recordings were obtained from rhesus monkeys in three different experiments involving individuated flexion/extension of each finger, wrist rotation, and dexterous grasps. Separate decoding filters were implemented in Matlab's Simulink environment to independently decode this suite of dexterous movements in real-time. Average real-time decoding accuracies of 80% was achieved for all dexterous tasks with as few as 15 neurons for individual finger flexion/extension, 41 neurons for wrist rotation, and 79 neurons for grasps. In lieu of the availability of advanced multi-fingered prosthetic hands, real-time visual feedback of the decoded output was provided through actuation of a virtual prosthetic hand in a Virtual Integration Environment. This work lays the foundation for future closed-loop experiments with monkeys in the loop and dexterous control of an actual prosthetic limb.
研究表明，涉及外部设备闭环控制的脑机接口（BCIs）在接收视觉反馈时，能让受试者自适应地纠正错误并提高控制精度。尽管已证明对粗大手臂运动进行闭环皮层控制，但尚未实现对诸如单个手指等更灵活运动的闭环解码。在三个不同实验中，从恒河猴获取神经记录，这些实验涉及每个手指的个体化屈伸、手腕旋转和灵活抓握。在Matlab的Simulink环境中实现单独的解码滤波器，以实时独立解码这一系列灵活运动。对于所有灵活任务，平均实时解码准确率达到80%，单个手指屈伸只需15个神经元，手腕旋转需41个神经元，抓握需79个神经元。由于没有先进的多指假手，通过在虚拟集成环境中驱动虚拟假手，提供解码输出的实时视觉反馈。这项工作为未来让猴子参与的闭环实验以及对实际假肢的灵活控制奠定了基础。
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