A Practical EEG-Based Human-Machine Interface to Online Control an Upper-Limb Assist Robot - Song, Yonghao; Cai, Siqi; Yang, Lie; Li, Guofeng; Wu, Weifeng; Xie, Longhan (Frontiers in Neurorobotics, 2020)
Background and objective: Electroencephalography (EEG) can be used as an interface to control machines with human intention, especially for paralyzed people in rehabilitation exercises or daily activities. Some effort was put into but still not enough. To improve the practicality, this study aims to propose an efficient control method based on P300, a special EEG component. Moreover, we also develop an upper-limb assist robot system with thorough security measures for verification. Methods: We concentrated on P300 that is highly available and easily accepted by users. Preprocessing and spatial enhancement were firstly implemented on raw data. Then, three approaches– linear discriminant analysis, support vector machine and multilayer perceptron –were compared in detail to accomplish a robust P300 detector, that was employed in accessing the user’s intention to control the assist robot. Results: The method we used achieved an accuracy of 94.43% in the offline test with the data from 8 participants. Furthermore, it showed sufficient reliability and efficiency with an accuracy of 80.83% and an information transfer rate of 15.42 in the online test. Conclusion: From the results, we can see that the proposed method has great potential for helping paralyzed people easily control an upper-limb assist robot to do numbers of things.