Sun, Jinggao (East China University of Science and Technology), Yang, Jiaxiong (East China University of Science and Technology), Wang, Shuo (East China University of Science and Technology), Yan, Huaicheng (ECUST)

Decoder Design and Performance Comparison of Closed-Loop Brain Machine Interface

Scheduled for presentation during the Regular Session "System Modelling and Identification" (TuAOr), Tuesday, December 19, 2017, 10:45−11:00, Room 7

2017 Australian and New Zealand Control Conference, December 17-20, 2017, Gold Coast Convention Centre, Australia

This information is tentative and subject to change. Compiled on April 26, 2024

Abstract

In this paper, the spontaneous motion of the single joint is studied on the basis of the cortical neuron firing activity model, and the working principle of the closed-loop brain machine interface is analyzed from the perspective of control theory. The Kalman filter and artificial neural network are used to design system decoder to replace the spinal cord current in original system. According to the result, the performance of decoder design based on neural network is better than that based on Kalman filter.