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Appl. Sci. 2017, 7(4), 390; doi:10.3390/app7040390

Adaptive Feature Extraction of Motor Imagery EEG with Optimal Wavelet Packets and SE-Isomap

1
Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
2
Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing 100124, China
*
Author to whom correspondence should be addressed.
Academic Editor: U Rajendra Acharya
Received: 16 February 2017 / Revised: 7 April 2017 / Accepted: 10 April 2017 / Published: 14 April 2017
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Abstract

Motor imagery EEG (MI-EEG), which reflects one’s active movement intention, has attracted increasing attention in rehabilitation therapy, and accurate and fast feature extraction is the key problem to successful applications. Based on wavelet packet decomposition (WPD) and SE-isomap, an adaptive feature extraction method is proposed in this paper. The MI-EEG is preprocessed to determine a more effective time interval through average power spectrum analysis. WPD is then applied to the selected segment of MI-EEG, and the subject-based optimal wavelet packets (OWPs) with top mean variance difference are obtained autonomously. The OWP coefficients are further used to calculate the time-frequency features statistically and acquire the nonlinear manifold structure features, as well as the explicit nonlinear mapping, through SE-isomap. The hybrid features are obtained in a serial fusion way and evaluated by a k-nearest neighbor (KNN) classifier. The extensive experiments are conducted on a publicly available dataset, and the experiment results of 10-fold cross-validation show that the proposed method yields relatively higher classification accuracy and computation efficiency simultaneously compared with the commonly-used linear and nonlinear approaches. View Full-Text
Keywords: feature extraction; supervised explicit isomap (SE-isomap); motor imagery EEG (MI-EEG); wavelet packet decomposition (WPD); k-nearest neighbor (KNN) feature extraction; supervised explicit isomap (SE-isomap); motor imagery EEG (MI-EEG); wavelet packet decomposition (WPD); k-nearest neighbor (KNN)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Li, M.-A.; Zhu, W.; Liu, H.-N.; Yang, J.-F. Adaptive Feature Extraction of Motor Imagery EEG with Optimal Wavelet Packets and SE-Isomap. Appl. Sci. 2017, 7, 390.

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