Aggarwal Vikram, Tenore Francesco, Acharya Soumyadipta, 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. 2009;2009:4535-8. doi: 10.1109/IEMBS.2009.5334129.
Previous research has shown that neuronal activity can be used to continuously decode the kinematics of gross movements involving arm and hand trajectory. However, decoding the kinematics of fine motor movements, such as the manipulation of individual fingers, has not been demonstrated. In this study, single unit activities were recorded from task-related neurons in M1 of two trained rhesus monkey as they performed individuated movements of the fingers and wrist. The primates' hand was placed in a manipulandum, and strain gauges at the tips of each finger were used to track the digit's position. Both linear and non-linear filters were designed to simultaneously predict kinematics of each digit and the wrist, and their performance compared using mean squared error and correlation coefficients. All models had high decoding accuracy, but the feedforward ANN (R = 0.76-0.86, MSE = 0.04-0.05) and Kalman filter (R = 0.68-0.86, MSE = 0.04-0.07) performed better than a simple linear regression filter (0.58-0.81, 0.05-0.07). These results suggest that individual finger and wrist kinematics can be decoded with high accuracy, and be used to control a multi-fingered prosthetic hand in real-time.
先前的研究表明，神经元活动可用于持续解码涉及手臂和手部轨迹的粗大运动的运动学。然而，尚未证实能够解码精细运动的运动学，例如单个手指的操作。在本研究中，当两只经过训练的恒河猴执行手指和手腕的个体化运动时，从它们初级运动皮层（M1）中与任务相关的神经元记录了单单元活动。将灵长类动物的手放置在一个操作装置中，并使用每个手指尖端的应变仪来跟踪手指的位置。设计了线性和非线性滤波器以同时预测每个手指和手腕的运动学，并使用均方误差和相关系数比较它们的性能。所有模型都具有较高的解码准确率，但前馈人工神经网络（R = 0.76 - 0.86，MSE = 0.04 - 0.05）和卡尔曼滤波器（R = 0.68 - 0.86，MSE = 0.04 - 0.07）的表现优于简单线性回归滤波器（0.58 - 0.81，0.05 - 0.07）。这些结果表明，单个手指和手腕的运动学能够以高精度解码，并可用于实时控制多指假手。
