Common spatial pattern(CSP) algorithm is a successful tool in feature estimate of brain-computer interface(BCI).However,CSP is sensitive to outlier and may result in poor outcomes since it is based on pooling the cova...Common spatial pattern(CSP) algorithm is a successful tool in feature estimate of brain-computer interface(BCI).However,CSP is sensitive to outlier and may result in poor outcomes since it is based on pooling the covariance matrices of trials.In this paper,we propose a simple yet effective approach,named common spatial pattern ensemble(CSPE) classifier,to improve CSP performance.Through division of recording channels,multiple CSP filters are constructed.By projection,log-operation,and subtraction on the original signal,an ensemble classifier,majority voting,is achieved and outlier contaminations are alleviated.Experiment results demonstrate that the proposed CSPE classifier is robust to various artifacts and can achieve an average accuracy of 83.02%.展开更多
为了提高运动想象脑机接口系统的性能,基于脑-机接口竞赛数据比较了不同空间滤波器下获得的CSP特征,在支持向量(线性核和高斯核)(linear kernel support vector machine,LSVM and(gaussian kernel support vector machine,GSVM)),线性...为了提高运动想象脑机接口系统的性能,基于脑-机接口竞赛数据比较了不同空间滤波器下获得的CSP特征,在支持向量(线性核和高斯核)(linear kernel support vector machine,LSVM and(gaussian kernel support vector machine,GSVM)),线性判别分析(linear discrimination analysis,LDA),梯度提升决策树(gradient boosting descrision tree,GBDT)下的分类效果.比较结果表明,GBDT获得了比其它分类器更优的分类效果.进一步把最小绝对收缩和选择算法(the Least Absolute Shrinkage and Selectionator operator,LASSO)与以上四种分类器进行结合使用,发现其与GBDT结合使用后得到的平均分类准确率最高,比结合LSVM,GSVM和LDA分别提高了5.57%,4.57%,3.16%.展开更多
基金supported by the National Natural Science Foundation of China under Grant No. 30525030, 60701015, and 60736029.
文摘Common spatial pattern(CSP) algorithm is a successful tool in feature estimate of brain-computer interface(BCI).However,CSP is sensitive to outlier and may result in poor outcomes since it is based on pooling the covariance matrices of trials.In this paper,we propose a simple yet effective approach,named common spatial pattern ensemble(CSPE) classifier,to improve CSP performance.Through division of recording channels,multiple CSP filters are constructed.By projection,log-operation,and subtraction on the original signal,an ensemble classifier,majority voting,is achieved and outlier contaminations are alleviated.Experiment results demonstrate that the proposed CSPE classifier is robust to various artifacts and can achieve an average accuracy of 83.02%.
