Tensor Based Simultaneous Feature Extraction and Sample Weighting for EEG Classification
In this paper we propose a Multi-linear Principal Component Analysis (MPCA) which is a new feature extraction and sample weighting method for classification of EEG signals using tensor decomposition. The method has been successfully applied to Motor-Imagery Brain Computer Interface (MI-BCI) paradigm. The performance of the proposed approach has been compared with standard Common Spatial Pattern (CSP) as well with a combination of PCA and CSP methods. We have achieved an average accuracy improvement of two classes classification in a range from 4 to 14 percents.
KeywordsFeature extraction classification tensor decomposition multi-linear PCA
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- 4.Phan, A.H., Cichocki, A.: Tensor Decompositions for Feature Extraction and Classification of High Dimensional Datasets. IEICE NOLTA E93-N(10) (October 2010)Google Scholar