脑机接口中脑电信号的特征提取和模式分类

Feature Extraction and Pattern Classification for EEG in Brain-Computer Interface

从智能处理与不确定性的角度,探讨了脑机接口中的核心问题-EEG模式特征的识别和分类.针对EEG模式分类中所存在的不确定性问题,从EEG的特征提取和分类模型构建两个方面进行了分析,并提出了解决问题的方法和对策.以P300成分为例,从导联选择、滤波处理和时间窗处理三方面进行特征提取,采用贝叶斯线性判别分析的方法进行模式分类.最后以第三届脑机接口竞赛P300字符输入的数据为实验,分别采用3种不同的方法进行数据分析,通过分类准确率和不同重复次数下性能的比较,实验结果表明了本文特征提取和模式分类方法的有效性.

The identification and classification of EEG pattern features in brain-computer interface（BCI） were proposed from the angle of the intelligent processing and the uncertainty. For the uncertainty problem of the existence of EEG, two aspects of EEG processing, feature extraction and classification, were analyzed. Furthermore, we put forward the methods to solve the problem. With P300 component as an example, the channel selection, filtering and time window selection were used for feature extraction. Then the Bayes linear discriminant analysis method was used for pattern classification. Finally, the P300 data sets of the BCI competition III were used for data analysis. By comparing the classification accuracy rate of three different methods, the results demonstrated the effectiveness of our method.