脑机接口中多线性主成分分析的张量特征提取

Tensor Feature Extraction Using Multi-linear Principal Component Analysis for Brain Computer Interface

脑机接口(BCI)可以直接通过脑电(EEG)信号控制外部设备。本文针对传统主成分分析(PCA)和二维主成分分析(2DPCA)处理多通道EEG信号的局限性,提出了多线性主成分分析(MPCA)的张量特征提取和分类框架。首先生成张量EEG数据,然后进行张量降维并提取特征,最后用Fisher线性判别分析分类器进行分类。实验中将新方法应用到BCI competitionⅡ数据集4和BCI competitionⅣ数据集3,分别使用了EEG数据的时空二阶张量表示形式和时空频三阶张量表示形式,通过对可调参数多次调试,取得了高于其它同类降维方法的最佳结果。二阶输入最高正确率分别达到81.0%和40.1%,三阶输入分别达到76.0%和43.5%。

The brain computer interface（BCI）can be used to control external devices directly through electroencephalogram（EEG）information.A multi-linear principal component analysis（MPCA）framework was used for the limitations of tensor form of multichannel EEG signals processing based on traditional principal component analysis（PCA）and two-dimensional principal component analysis（2DPCA）.Based on MPCA,we used the projection of tensor-matrix to achieve the goal of dimensionality reduction and features exaction.Then we used the Fisher linear classifier to classify the features.Furthermore,we used this novel method on the BCI competition Ⅱ dataset 4and BCI competition Ⅳ dataset 3in the experiment.The second-order tensor representation of time-space EEG data and the third-order tensor representation of time-space-frequency EEG data were used.The best results that were superior to those from other dimensionality reduction methods were obtained by much debugging on parameter P and testQ.For two-order tensor,the highest accuracy rates could be achieved as 81.0%and 40.1%,and for three-order tensor,the highest accuracy rates were 76.0% and 43.5%,respectively.