A Novel Method of Multi-channel Feature Extraction Combining Multivariate Autoregression and Multiple-linear Principal Component Analysis_Journal of Biomedical Engineering

Authors：

 WANGJinjia , ZHANGYanna

College of Information Science and Engineer, Yanshan University, Qinhuangdao 066004, China;

Corresponding author：

WANGJinjia, Email: wjj@ysu.edu.cn

Keywords：

brain-computer interface; magnetoencephalography; electroencephalograph; multivariate autoregressive model; multi-linear principal component analysis; feature extraction

DOI：

10.7507/1001-5515.20150004

Video：

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Abstract Full text Figures/Tables Video References Cited by

Brain-computer interface (BCI) systems identify brain signals through extracting features from them. In view of the limitations of the autoregressive model feature extraction method and the traditional principal component analysis to deal with the multichannel signals, this paper presents a multichannel feature extraction method that multivariate autoregressive (MVAR) model combined with the multiple-linear principal component analysis (MPCA), and used for magnetoencephalography (MEG) signals and electroencephalograph (EEG) signals recognition. Firstly, we calculated the MVAR model coefficient matrix of the MEG/EEG signals using this method, and then reduced the dimensions to a lower one, using MPCA. Finally, we recognized brain signals by Bayes Classifier. The key innovation we introduced in our investigation showed that we extended the traditional single-channel feature extraction method to the case of multi-channel one. We then carried out the experiments using the data groups ofⅣ_ⅢandⅣ_Ⅰ. The experimental results proved that the method proposed in this paper was feasible.

Citation： WANGJinjia, ZHANGYanna. A Novel Method of Multi-channel Feature Extraction Combining Multivariate Autoregression and Multiple-linear Principal Component Analysis. Journal of Biomedical Engineering, 2015, 32(1): 19-24. doi: 10.7507/1001-5515.20150004 Copy

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12.	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.
13.	PARK C, LOONEY D, UR REHMAN N, et al. Classification of motor imagery BCI using multivariate empirical mode decomposition[J]. IEEE Trans Neural Syst Rehabil Eng, 2013, 21(1):10-22.
14.	王金甲, 张玲智, 胡备.多类核共空间模式特征提取方法研究[J].生物医学工程学杂志, 2012, 29(2):217-222.

1. MCFARLAND D J, WOLPAW J R. Brain-computer interfaces for communication and control[J]. Commun ACM, 2011, 54(5):60-66.
2. MOUSAVI E A, MALLER J J, FITZGERALD P B, et al. Wavelet common spatial pattern in asynchronous offline brain computer interfaces[J]. Biomed Signal Process Contr, 2011, 6(2):121-128.
3. LAWHERN V, HAIRSTON W D, MCDOWELL K, et al. Detection and classification of subject-generated artifacts in EEG signals using autoregressive models[J]. J Neurosci Methods, 2012, 208(2):181-189.
4. KEIRN Z A, AUNON J I. A new mode of communication between man and his surroundings[J]. IEEE Trans Biomed Eng, 1990, 37(12):1209-1214.
5. HU X, NENOV V. Multivariate AR modeling of electromyography for the classification of upper arm movements[J]. Clin Neurophysiol, 2004, 115(6):1276-1287.
6. 薛建中, 郑崇勋, 闫相国.快速多变量自回归模型的意识任务的特征提取与分类[J].西安交通大学学报, 2003, 37(8):861-864.
7. 王江, 徐桂芝, 王磊, 等.基于多通道自适应自回归模型脑-机接口系统特征的提取[J].中国组织工程研究与临床康复, 2011, 15(48):9007-9010.
8. ZHAO C L, ZHENG C X, ZHAO M, et al. Multivariate autoregressive models and kernel learning algorithms for classifying driving mental fatigue based on electroencephalographic[J]. Expert Syst Appl, 2011, 38(3):1859-1865.
9. 王宁, 魏玲, 李颖洁.基于格兰杰因果分析情绪认知过程中alpha脑电特性[J].生物医学工程学杂志, 2012, 29(6):1021-1026.
10. ANDERSON C W, STOLZ E A, SHAMSUNDER S. Multivariate autoregressive models for classification of spontaneous electroencephalographic signals during mental tasks[J]. IEEE Trans Biomed Eng, 1998, 45(3):277-286.
11. MORF M, VIEIRA A, LEE D T L, et al. Recursive multichannel maximum entropy spectral estimation[J]. IEEE Trans Geosci Electr, 1978, 16(2):85-94.
12. 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.
13. PARK C, LOONEY D, UR REHMAN N, et al. Classification of motor imagery BCI using multivariate empirical mode decomposition[J]. IEEE Trans Neural Syst Rehabil Eng, 2013, 21(1):10-22.
14. 王金甲, 张玲智, 胡备.多类核共空间模式特征提取方法研究[J].生物医学工程学杂志, 2012, 29(2):217-222.

Journal of Biomedical Engineering

A Novel Method of Multi-channel Feature Extraction Combining Multivariate Autoregression and Multiple-linear Principal Component Analysis

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