摘要
共空域模式(CSP)在运动想象脑机接口(BCI)中得到了广泛的应用。但是传统的CSP存在3个缺陷,包括样本协方差估计的噪声敏感性、被试特异的时间窗选择以及被试特异的频带选择。针对CSP的前两个问题,分别提出了两种改进方法。第一种方法,首先提取脑电(EEG)每个通道信号的方差作为特征,分别使用Fisher线性判别分析(FLDA)和贝叶斯线性判别分析(BLDA)方法进行分类,得到通道权重分布,选择权重较大的通道再进行CSP变换。通过剔除包含噪声的通道,降低了CSP的噪声敏感性。第二种方法,基于被试持续进行运动想象的时间和强度存在差异的假设,提出一种新的被试特异的时间窗选择方法,仍然使用FLDA和BLDA方法进行分类。为验证改进算法的有效性,使用2005年BCI竞赛数据集IVa进行实验,两种改进方法分别取得了87.77%和81.23%的最高平均分类准确率。实验结果表明,所提出的两种改进方法优于传统的CSP方法。同时在两种改进方法中,BLDA方法的分类效果都优于FLDA。
The common spatial pattern(CSP) has been widely used in the motor imagery based brain-computer interface(BCI). However, the traditional CSP has three defects, including the noise sensitivity of the sample covariance estimation, the subject-specific time window selection and the subject-specific band selection. For the first two problems of CSP, two improved methods were proposed. In the first method, the variance of EEG signals in each channel is extracted as the feature, and Fisher linear discriminant analysis(FLDA) and Bayesian linear discriminant analysis(BLDA) methods are used to classify, then the channel weight distribution can be obtained, channels with larger weight are used to CSP transformation. The noise sensitivity of the CSP is reduced by eliminating the channel containing noise. In the second method, based on the assumption that the time and intensity of the subject’s motor imagery is different, a new method of time window selection was proposed, FLDA and BLDA methods are still used for classification. In order to verify the effectiveness of the two improved algorithms, experiments were conducted using the 2005 BCI competition dataset IVa. The highest average classification accuracy of 87.77% and 81.23% are achieved by the two improved methods respectively. The experiment results show that the proposed methods are superior to the traditional CSP method. At the same time, in the two improved methods, the classification effect of the BLDA method is better than FLDA.
作者
赵紫宁
李智
张绍荣
Zhao Zining;Li Zhi;Zhang Shaorong(School of Electronic Engineering and Automation,Guilin University of Electronic Technology,Guilin 541004,China;School of Electronic Information and Automation,Guilin University of Aerospace Technology,Guilin 541004,China)
出处
《电子测量与仪器学报》
CSCD
北大核心
2019年第12期64-70,共7页
Journal of Electronic Measurement and Instrumentation
基金
国家自然科学基金(61967004,11901137,81960324)
广西区自然科学基金(2018GXNSFBA281023,2016GXNSFBA380160)
广西区自动检测技术与仪器重点实验室基金(YQ19209,YQ18107)
桂林电子科技大学研究生教育创新计划(2019YCXB03)资助项目。
关键词
运动想象脑电
脑机接口
共空域模式
通道选择
时间窗选择
motor imagery EEG
brain-computer interface
common spatial pattern
channel selection
time window selection
