文献类型：期刊文献

英文题名：Improvement of brain - Machine interface model on dynamic reaching task

作者：Sun, Jinggao[1]; Xue, Rui[1]; Pan, Hongguang[2]; Yang, Jiaxiong[1]; Wang, Shuo[1]

机构：[1] East China University of Science and Technology, Key Laboratory of Advanced Control and Optimization Technology of Ministry of Education, Shanghai, 200237, China; [2] Xi'An University of Science and Technology, School of Electrical and Control Engineering, Xi'an, 710049, China

年份：2018

卷号：2018-July

起止页码：1690

外文期刊名：Chinese Control Conference, CCC

收录：EI(收录号：20184606055357)

语种：英文

外文关键词：Neurons - Brain computer interface - Decoding - Motion estimation

摘要：Based on the relative and required velocity integration to endpoint (RRVITE) model, we extend the neuroprosthetic model for voluntary single joint reaching task by adding the vector models related to relative speed information, which improves the model performance in dynamic reaching task. Through the synthetic simulation, we show that the extended model performs much better than the original one in motion prediction and steady state accuracy. Using synthetic data obtained through the simulation of the original model, a linear decoder based on Weiner filter has been trained and introduced into the improved model to test its performance. Both the evaluation results and the online test results show that the improved model can also enhance the performance of the BMI system with decoder. ? 2018 Technical Committee on Control Theory, Chinese Association of Automation.