Proprioceptive afferent activities recorded by a multichannel microelectrode have been used to decode limb movements to provide sensory feedback signals for closed-loop control in a functional electrical stimulation (FES) system. However, analyzing the high dimensionality of neural activity is one of the major challenges in real-time applications. This paper proposes a linear feature projection method for the real-time decoding of ankle and knee joint angles. Single-unit activity was extracted as a feature vector from proprioceptive afferent signals that were recorded from the L7 dorsal root ganglion during passive movements of ankle and knee joints. The dimensionality of this feature vector was then reduced using a linear feature projection composed of projection pursuit and negentropy maximization (PP/NEM). Finally, a time-delayed Kalman filter was used to estimate the ankle and knee joint angles. The PP/NEM approach had a better decoding performance than did other feature projection methods, and all processes were completed within the real-time constraints. These results suggested that the proposed method could be a useful decoding method to provide real-time feedback signals in closed-loop FES systems.

中文翻译：

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基于线性特征投影的背根神经节记录对肢体状态的实时解码。

多通道微电极记录的本体感受传入活动已被用于解码肢体运动，以提供感觉反馈信号，用于功能性电刺激（FES）系统中的闭环控制。然而，分析神经活动的高维度是实时应用中的主要挑战之一。本文提出了一种用于踝关节和膝关节角度实时解码的线性特征投影方法。从踝关节和膝关节被动运动期间L7背根神经节记录的本体感受传入信号中提取单个单元活动作为特征向量。然后，使用由投影追踪和负熵最大化（PP / NEM）组成的线性特征投影来减小此特征向量的维数。最后，使用延时卡尔曼滤波器估计踝关节和膝关节的角度。PP / NEM方法具有比其他特征投影方法更好的解码性能，并且所有过程都在实时约束下完成。这些结果表明，所提出的方法可能是一种有用的解码方法，可以在闭环FES系统中提供实时反馈信号。