A novel method of multi-channel feature extraction combining multivariate autoregression and multiple-linear principal component analysis.
- Author:
Jinjia WANG
;
Yanna ZHANG
- Publication Type:Journal Article
- MeSH:
Bayes Theorem;
Brain;
physiology;
Brain-Computer Interfaces;
Electroencephalography;
Humans;
Magnetoencephalography;
Multivariate Analysis;
Principal Component Analysis
- From:
Journal of Biomedical Engineering
2015;32(1):19-24
- CountryChina
- Language:Chinese
-
Abstract:
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 of IV-III and IV - I. The experimental results proved that the method proposed in this paper was feasible.
