Algorithm Contest of Calibration-free Motor Imagery BCI in the BCI Controlled Robot Contest in World Robot Contest 2021:A survey  

作　　者：Jing Luo Qi Mao Yaojie Wang Zhenghao Shi Xinhong Hei 

机构地区：[1]Shaanxi Key Laboratory for Network Computing and Security Technology,School of Computer Science and Engineering,Xi’an University of Technology,Xi’an 710054,Shaanxi,China

出　　处：《Brain Science Advances》2022年第2期127-141,共15页神经科学（英文）

基　　金：This work is supported by the National Natural Science Foundation of China(Grant Nos.61906152 and 62076198);Key Research and Development Program of Shaanxi(Program Nos.2021GY-080 and 2020GXLH-Y005)。

摘　　要：Objective:From September 10 to 13,2021,the finals of the BCI Controlled Robot Contest in World Robot Contest 2021 were held in Beijing,China.Eleven teams participated in the Algorithm Contest of Calibration-free Motor Imagery BCI.The participants employed both traditional electroencephalograph(EEG)analysis methods and deep learning-based methods in the contest.In this paper,we reviewed the algorithms utilized by the participants,extracted the trends and highlighted interesting approaches from these methods to inform future contests and research recommendations.Method:First,we analyzed the algorithms in separate steps,including EEG channel and signal segment setup,prepossessing technology,and classification model.Then,we emphasized the highlights of each algorithm.Finally,we compared the competition algorithm with the SOTA algorithm.Results:The algorithm employed in the finals performed better than that of the SOTA algorithm.During the final stage of the contest,four of the top five teams used convolutional neural network models,suggesting that with the rapid development of deep learning,convolutional neural network-based models have been the most popular methods in the field of motor imagery BCI.

关 键 词：brain-computer interface motor imagery con-volutional neural network World Robot Contest 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置] TP391.41[自动化与计算机技术—控制科学与工程]