Offline Decoding of End-Point Forces Using Neural Ensembles: Application to a Brain-Machine Interface
Offline Decoding of End-Point Forces Using Neural Ensembles: Application to a Brain-Machine Interface
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DOI：
10.1109/tnsre.2009.2023290
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发表时间：
2009-06-01
影响因子：
4.9
通讯作者：
Ashe, James
中科院分区：
文献类型：
--
作者：
Gupta, Rahul;Ashe, James
脑机接口（BMIs）有望在一定程度上恢复神经疾病或损伤患者的运动功能。目前的BMI方法分为两大类，一类是解码肢体运动的离散属性（如运动方向和运动意图），另一类是解码连续变量（如位置和速度）。然而，为了使假肢装置能够用于常见的日常任务，假肢末端（例如手）施加的力的精确控制也是必不可少的。在这里，我们使用线性回归和卡尔曼滤波方法来证明，在力场运动期间，从猴子的运动皮层记录的神经活动可以用于解码受试者施加的端点力，并且具有高保真度。此外，该模型对新的任务条件具有一定的通用性。我们还演示了如何使用相同的框架轻松实现运动学和动力学的同时预测，而不会降低解码质量。我们的研究结果代表了当前BMI技术的有用扩展，使假肢装置的动态控制在不久的将来成为一种明显的可能性。
Brain-machine interfaces (BMIs) hold a lot of promise for restoring some level of motor function to patients with neuronal disease or injury. Current BMI approaches fall into two broad categories-those that decode discrete properties of limb movement (such as movement direction and movement intent) and those that decode continuous variables (such as position and velocity). However, to enable the prosthetic devices to be useful for common everyday tasks, precise control of the forces applied by the end-point of the prosthesis (e.g., the hand) is also essential. Here, we used linear regression and Kalman filter methods to show that neural activity recorded from the motor cortex of the monkey during movements in a force field can be used to decode the end-point forces applied by the subject successfully and with high fidelity. Furthermore, the models exhibit some generalization to novel task conditions. We also demonstrate how the simultaneous prediction of kinematics and kinetics can be easily achieved using the same framework, without any degradation in decoding quality. Our results represent a useful extension of the current BMI technology, making dynamic control of a prosthetic device a distinct possibility in the near future.