A novel method to reduce the motor imagery BCI illiteracy

被引：0
作者：
Tingting Wang
Shengzhi Du
Enzeng Dong
机构：
[1] Tianjin University of Technology,Institute of Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems
[2] Tshwane University of Technology,Department of Electrical Engineering
来源：
Medical & Biological Engineering & Computing | 2021年 / 59卷
关键词：
Brain-computer interface; Motor imagery; BCI illiteracy; Classification paradigms; Sensitivity-based paradigm selection;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
To reduce the motor imagery brain-computer interface (MI-BCI) illiteracy phenomenon and improve the classification accuracy, this paper proposed a novel method combining paradigm selection and Riemann distance classification. Firstly, a novel sensitivity-based paradigm selection (SPS) algorithm is designed for the optimization of classification to find the best classification pattern through a sensitive indicator. Then, a generalized Riemann minimum distance mean (GRMDM) classifier is proposed by introducing a weight factor to fuse the Log-Euclidean Metric classifier and the Riemannian Stein divergence classifier. The experimental results show that the proposed method achieves a better performance for multi-class motor imagery tasks. The average classification accuracy on the BCI competition IV dataset2a is 80.98%, which is 11.04% higher than Stein divergence classifier on the original two-class paradigm. Furthermore, the proposed method demonstrates its capacity on reducing MI-BCI illiteracy.
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页码：2205 / 2217
页数：12
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