Classification of ECoG Signals for Motor Imagery Tasks

摘要：

The electrocorticogram(ECoG) is proved to have high signal-to-noise ratio(SNR), which makes it better fitting for BCIs. And this paper represents a kind of classification method of ECoG signals for motor imagery tasks(left finger and tongue). Band power(BP) with the frequency band of |8 30) was extracted as the feature, and the linear discriminant analysis (LDA), k-nearest neighbor(kNN) rules and linear support vector machine(SVM) were used as the classifiers. From the results of these three classifiers, kNN with k=7 performed better than all the other classifiers, and the classification accuracy was 87％. But the combination of these three classifiers could improve the final results a little better, which could be up to 89％.

关键词： ECoG BP LDA kNN SVM classifier combination

作者: Liu Chong Zhao Hai-bin Li Chun-sheng Wang Hong

作者单位: School of Mechanical Engineering & Automation Northeastern University Shenyang 110004 China

会议类型: 国际会议

会议名称: 2010 2nd International Conference on Signal Processing System(2010年信号处理系统国际会议 ICSPS 2010)

会议地点: 大连

会议语种:英文

页码: 1866-1869

在线出版日期: 2010-07-05（万方平台首次上网日期，不代表论文的发表时间）