Schwartz Andrew B
Departments of Neurobiology and Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15203, USA.
Annu Rev Neurosci. 2004;27:487-507. doi: 10.1146/annurev.neuro.27.070203.144233.
Control of prostheses using cortical signals is based on three elements: chronic microelectrode arrays, extraction algorithms, and prosthetic effectors. Arrays of microelectrodes are permanently implanted in cerebral cortex. These arrays must record populations of single- and multiunit activity indefinitely. Information containing position and velocity correlates of animate movement needs to be extracted continuously in real time from the recorded activity. Prosthetic arms, the current effectors used in this work, need to have the agility and configuration of natural arms. Demonstrations using closed-loop control show that subjects change their neural activity to improve performance with these devices. Adaptive-learning algorithms that capitalize on these improvements show that this technology has the capability of restoring much of the arm movement lost with immobilizing deficits.
慢性微电极阵列、提取算法和假肢效应器。微电极阵列被永久植入大脑皮层。这些阵列必须无限期记录单单元和多单元活动群体。需要从记录的活动中实时连续提取包含有生命运动的位置和速度相关性的信息。假肢手臂是这项工作中目前使用的效应器，需要具备自然手臂的灵活性和构造。使用闭环控制的演示表明，受试者会改变其神经活动以提高使用这些设备的表现。利用这些改进的自适应学习算法表明，这项技术有能力恢复因固定性缺陷而丧失的大部分手臂运动。
