A PCA aided cross-covariance scheme for discriminative feature extraction from EEG signals. (July 2017)

• Record Type: Journal Article
• Authors: Zarei, Roozbeh; He, Jing; Siuly, Siuly; Zhang, Yanchun
• Is Part Of: Computer methods and programs in biomedicine. Volume 146(2017)
• Page Start: 47
• Page End: 57
• Publication Date: 2017-07
• Subjects: Electroencephalogram (EEG) -- Brain-computer interface (BCI) -- Feature extraction -- Principal component analysis (PCA) -- Cross-covariance method (CCOV); 00-01 -- 99-00; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2017.05.009
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A low-complexity feature extraction method is proposed for EEG signal classification. Introducing PCA with cross-covariance method for extracting features from EEG signals. Efficacy of the methods are confirmed by statistical and graphical analyses. The method yields the utmost accuracy improving up to 0.25% than others. Abstract: Background and objectives: Feature extraction of EEG signals plays a significant role in Brain-computer interface (BCI) as it can significantly affect the performance and the computational time of the system. The main aim of the current work is to introduce an innovative algorithm for acquiring reliable discriminating features from EEG signals to improve classification performances and to reduce the time complexity. Methods: This study develops a robust feature extraction method combining the principal component analysis (PCA) and the cross-covariance technique (CCOV) for the extraction of discriminatory information from the mental states based on EEG signals in BCI applications. We apply the correlation based variable selection method with the best first search on the extracted features to identify the best feature set for characterizing the distribution of mental state signals. To verify the robustness of the proposed feature extraction method, three machine learning techniques: multilayer perceptron neural networks (MLP), least square support vector machine (LS-SVM), and logistic regression (LR) are employed on the obtained features. The proposed methods are evaluated on two publicly available datasets. Furthermore, we evaluate the performance of the proposed methods by comparing it with some recently reported algorithms. Results: The experimental results show that all three classifiers achieve high performance (above 99% overall classification accuracy) for the proposed feature set. Among these classifiers, the MLP and LS-SVM methods yield the best performance for the obtained feature. The average sensitivity, specificity and classification accuracy for these two classifiers are same, which are 99.32%, 100%, and 99.66%, respectively for the BCI competition dataset IVa and 100%, 100%, and 100%, for the BCI competition dataset IVb. The results also indicate the proposed methods outperform the most recently reported methods by at least 0.25% average accuracy improvement in dataset IVa. The execution time results show that the proposed method has less time complexity after feature selection. Conclusions: The proposed feature extraction method is very effective for getting representatives information from mental states EEG signals in BCI applications and reducing the computational complexity of classifiers by reducing the number of extracted features.