A Distribution Adaptive Feedback Training Method to Improve Human Motor Imagery Ability

Abstract

A brain-computer interface (BCI) based on motor imagery (MI) can translate users’ subjective movement-related mental state without external stimulus, which has been successfully used for replacing and repairing motor function. In contrast with studies about decoding methods, less work was reported about training users to improve the performance of MI-BCIs. This study aimed to develop a novel MI feedback training method to enhance the ability of humans to use the MI-BCI system. In this study, an adaptive MI feedback training method was proposed to improve the effectiveness of the training process. The method updated the feedback model during training process and assigned different weights to the samples to better adapt the changes in the distribution of the Electroencephalograms (EEGs). An online feedback training system was established. Each of ten subjects participated in a three-day experiment involving three different feedback methods: no feedback algorithm update, feedback algorithm update, and feedback algorithm update using the proposed adaptive method. Comparison experiments were conducted on three different feedback methods. The experimental results showed that the feedback algorithm using the proposed method can most quickly improve the MI classification accuracy and has the largest increase in accuracy. This indicates that the proposed method can enhance the effectiveness of feedback training and improve the practicality of MI-BCI systems.