基于正则化共空间模式的运动想象脑电信号解码

Motor imagery EEG signal decoding based on regularized common space pattern

运动想象⁃脑机接口(MI⁃BCI)是一种能对使用者的运动意图进行解码,无需任何外部刺激就能产生指令,可以为无法自主运动患者提供一种额外交互控制通道,辅助或改善其生活方式。公共空间模式(CSP)是MI⁃BCI的EEG信号解码的一种有效方法,但是还存在一些不足。CSP对滤波器频带、时间窗口和通道的选择非常敏感,因此文中提出了新的正则化CSP(RCSP)方法对传统的CSP进行优化。通过在公共BCI数据集上进行验证,得到了82.92%的分类准确率,相比经典的CSP算法准确率提升6%。研究结果表明RCSP是一种更加有效的MI⁃EEG信号解码方法,可以为后续MI⁃EEG在线系统的研究提供新的思路。

Motor imagery brain computer interface(MI⁃BCI)can decode the user′s motion intention.It can generate commands without any external stimulation,and can provide an additional interactive control channel for patients who cannot move autonomously to assist or improve their lifestyle.Common space pattern(CSP)is an effective method of EEG signal decoding in MI⁃BCI,but it still has some shortcomings.CSP is very sensitive to the selection of filter frequency band,time window and channel,so a new regularized CSP(RCSP)method is proposed in this paper to optimize the traditional CSP.The method was verified on the public BCI dataset.The classification accuracy of the method is 82.7%,which is 6%higher than that of the classical CSP algorithm.The research results show that RCSP is an effective MI⁃EEG signal decoding method,which can provide a new idea for the subsequent research of MI⁃EEG online system.