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 competitionⅡdataset 4 and BCI competitionⅣdataset 3 in 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.
Citation：
WANGJinjia, YANGLiang. Tensor Feature Extraction Using Multi-linear Principal Component Analysis for Brain Computer Interface. Journal of Biomedical Engineering, 2015, 32(3): 526-530. doi: 10.7507/1001-5515.20150096
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- 1. WOLPAW J R, BIRBAUMER N, MCFARLAND D J, et al. Brain-computer interfaces for communication and control[J]. Clin Neurophysiol, 2002, 113(6):767-791.
- 2. BAGHDADI G, NASRABADI A M. Comparison of different EEG features in estimation of hypnosis susceptibility level[J]. Comput Biol Med, 2012, 42(5):590-597.
- 3. 王金甲, 周丽娜.基于PCA和LDA数据降维的脑磁图脑机接口研究[J].生物医学工程学杂志, 2011, 28(6):1069-1074.
- 4. YANG J, ZHANG D, FRANGI A F, et al. Two-dimensional PCA:a new approach to appearance-based face representation and recognition[J]. IEEE Trans Pattern Anal Mach Intell, 2004, 26(1):131-137.
- 5. LU H, PLATANIOTIS K N, VENETSANOPOULOS A N. MPCA:multilinear principal component analysis of tensor objects[J]. IEEE Trans Neural Netw, 2008, 19(1):18-39.
- 6. LI J, ZHANG L, TAO D, et al. A prior neurophysiologic knowledge free tensor-based scheme for single trial EEG classification[J]. IEEE Trans Neural Syst Rehabil Eng, 2009, 17(2):107-115.
- 7. LATHAUWER L D, MOOR B D, VANDEWALLE J. A multilinear singular value decomposition[J]. SIAM J Matrix Anal Appl, 2000, 21(4):1253-1278.
- 8. LU H, ENG H L, GUAN C, et al. Regularized common spatial pattern with aggregation for EEG classification in small-sample setting[J]. IEEE Trans Biomed Eng, 2010, 57(12):2936-2946.
