多类核共空间模式特征提取方法研究

Research on the Methods for Multi-class Kernel CSP-based Feature Extraction

为了缓解共空间模式(CSP)下,对脑内的源信号和记录的脑电(EEG)信号之间严格的线性模式的假设关系,需要研究一种核共空间模式(KCSP)的特征提取方法。考虑到脑-机接口(BCI)研究已经逐渐从两类的模式识别发展为多类的模式识别,因而提出了多类核共空间模式(MKCSP)的方法,该方法将KCSP方法和多类CSP方法结合起来。我们用Logistic线性分类器对提取的特征进行了分类。实验使用的数据是2005年BCI竞赛Ⅲ的数据集Ⅲ_3a。通过实验表明,本文中的方法能够从多类别的单次试验的EEG数据中提取相应的特征,并得到了较好分类结果。

To relax the presumption of strictly linear patterns in the common spatial patterns（CSP）,we studied the kernel CSP（KCSP）.A new multi-class KCSP（MKCSP） approach was proposed in this paper,which combines the kernel approach with multi-class CSP technique.In this approach,we used kernel spatial patterns for each class against all others,and extracted signal components specific to one condition from EEG data sets of multiple conditions.Then we performed classification using the Logistic linear classifier.Brain computer interface（BCI） competition Ⅲ_3a was used in the experiment.Through the experiment,it can be proved that this approach could decompose the raw EEG singles into spatial patterns extracted from multi-class of single trial EEG,and could obtain good classification results.