脑机接口中基于BISVM的EEG分类

针对脑电信号(EEG)分类问题,提出基于批处理增量式支持向量机(BISVM)的分类方法.将所有数据通过批处理进行分组,采用第1组数据在SVM中建立初始分类器模型,将剩余组内数据顺序作为新增样本,对满足卡罗需-库恩-塔克(KKT)条件的样本进行增量学习和减量去学习,不断判断KKT条件并更新参数,丢弃错误样本,对初始分类器模型进行更新.对2008年脑机接口竞赛数据及本实验室采集数据,用小波包分解(WPD)结合共空间模式(CSP)进行特征提取,SVM、ISVM及BISVM分类.结果表明，BISVM的平均分类准确率相对SVM及ISVM分别提高了3.3%及0.3%,BISVM平均训练时间相对ISVM从1.076 s减少到0.793 s.BISVM为改善计算机对大脑的适应性,实现快速实时在线的脑机接口系统奠定基础.

EEG classification based on batch incremental SVM in brain computer interfaces

Aiming at the Electroencephalogram (EEG) classification, a classification method based on batch incremental support vector machine (BISVM) was proposed in brain computer interfaces (BCIs). All training data were grouped by batch processing and the initial SVM classifier model was set up using the first group. The remaining groups of data were added into the first group orderly as new samples. An incremental learning and decremental learning process was adopted to samples which meet the KKT condition. The initial SVM classifier model was updated by continuously estimating KKT condition, updating the parameters, discarding the error samples. Based on the 2008 BCI Competition Dataset and our experimental EEG data, features were extracted by wavelet packet decomposition (WPD) and common spatial patterns (CSP). The SVM, ISVM and BISVM were used to classify these features. Experimental results show that the average classification accuracy of the BISVM is 3.3% and 0.3% higher than the SVM and ISVM respectively. The average training time of the BISVM is shortened from 1.076 s seconds to 0.073 s seconds compared to the ISVM. The BISVM can not only improve the adaptability of a computer to human brain but also lay the foundation to the realization of a fast, real-time and online BCI system.