摘要
脑机接口(BCI)脑电图(EEG)分类能实现人脑直接与外部环境的信息交互。提出了基于辅助训练思想的半监督稀疏表示分类器方法在BCI EEG分类中的应用。首先采用稀疏表示分类器从未标记样本中选择部分相关度较高的样本。其次采用Fisher线性分类器作为判别分类器得到已选样本的边界信息。通过距离大小和方向判别条件进一步选出高置信度样本。本文对三组基准数据集BCIⅠ、BCIⅡ_Ⅳ和USPS分别进行仿真实验,分类正确率分别为97%、82%和84.7%,运算速度最快的仅需约0.2s。在分类正确率和运算效率两个方面,均优于自训练半监督SVM、有导师SVM两种方法。
Electroencephalogram （EEG） classification for brain-computer interface （BCI） is a new way of realizing human-computer interreaction. In this paper the application of semi-supervised sparse representation classifier algorithms based on help training to EEG classification for BCI is reported. Firstly, the correlation information of the unlabeled data is obtained by sparse representation classifier and some data with high correlation selected. Secondly, the boundary information of the selected data is produced by discriminative classifier, which is the Fisher linear classifier. The final unlabeled data with high confidence are selected by a criterion containing the information of distance and direction. We applied this novel method to the three benchmark datasets, which were BCI I , BCI Ⅱ_Ⅳ and USPS. The classification rate were 97% ,82% and 84.7%, respectively. Moreover the fastest arithmetic rate was just about 0.2 s. The classification rate and efficiency results of the novel method are both better than those of S3VM and SVM, proving that the proposed method is effective.
出处
《生物医学工程学杂志》
EI
CAS
CSCD
北大核心
2014年第1期1-6,共6页
Journal of Biomedical Engineering
基金
国家自然科学基金资助项目(61074195)
河北省自然科学基金资助项目(F2010001281
A2010001124)
关键词
半监督学习
稀疏表示分类器
辅助训练
自训练
脑电图
脑机接口
semi-supervised learning
sparse representation classifier
help training
self-training
electroencephalogram
brain-computer interface
