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《第37届中国控制会议论文集(A)》2018年
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Improvement of Brain-Machine Interface Model on Dynamic Reaching Task

Jinggao Sun  Rui Xue  Hongguang Pan  Jiaxiong Yang  Shuo Wang  
【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.

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