摘要
针对两种不同意识任务的脑-机接口设计,提出了以方差作为特征的方法和以分类速率作为评价标准之一的新方法.首先深入研究了小波理论,分析了小波包分解中存在的频带交错现象,然后以小波系数和小波包系数的方差作为特征,对C3,C4导联脑电信号分别进行两种特征的提取,最后采用线性支持向量机作为分类器进行分类.结果表明,两种特征对应的最大分类正确率均达到了86.43%,对应时间分别为4.32和4.31 s.因此,以小波方差和小波包方差作为特征是完全可取的;分类速率的提出能同时反映分类正确率和分类时间,为大脑意识任务分类提供了新思路.
A method using variance as feature and using classification rate as one of evaluation criteria was proposed for the brain-computer interface（BCI） design of two kinds of imagery tasks. The wavelet theory was firstly discussed, and cross-banding of wavelet packet decomposition was analyzed. Variances of wavelet and wavelet packet coefficients were taken as features, then the two EEG features were extracted from the electrodes C3 and C4, and they were finally classified by using a linear support vector machine. The results showed that the maximum classification accuracies of both features were 86.43 % and the corresponding times were 4.32 and 4.31 s. So, it was suitable to use wavelet variance and wavelet packet variance as features. The presented classification rate could reflect the classification accuracies and classification time at the same time, and also give a new idea for classification of imagery tasks in BCI.
出处
《东北大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2012年第10期1504-1508,共5页
Journal of Northeastern University(Natural Science)
基金
国家自然科学基金资助项目(61071057)
关键词
脑-机接口
小波分析
方差
支持向量机
分类时间
brain-computer interface
wavelet analysis
variance
support vector machine
classification time
