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.
Highlights
A brain computer interface (BCI) system is an emerging technology that is regarded as a new way for the human brain to control external devices without the neural pathway [1]
To solve the computational complexity and data storage problem caused by the high dimension of signals, many dimensionality reduction methods have been used in traditional BCI technology, such as principal component analysis (PCA), independent components analysis (ICA) and multidimensional scaling (MDS)
In consideration of the results from the wavelet packet decomposition (WPD) + locally linear embedding (LLE) and WPD + isomap methods being relatively close, a two-sample t-test, whose assumption is similar to the one in Section 4.3.1, was implemented here to verify the statistical difference between the two feature extraction methods, and the p-value is 0.0497, which implies that the performance of WPD + isomap is significantly better than that of WPD + LLE
Summary
A brain computer interface (BCI) system is an emerging technology that is regarded as a new way for the human brain to control external devices without the neural pathway [1]. Yang et al [19] applied WPD to extracting the time-frequency features of MI-EEG, and the wavelet packet coefficients were input to the CSP algorithm to obtain a set of six-dimensional eigenvectors. Features; The optimal different subjects by using above WPD‐based methods, as the time‐frequency wavelet packets (OWPs), the are isautonomously the WPD coefficients arereflecting covered by thesubject-based same frequencyfeatures, ranges. This not good for theselected, extractionand of the subject‐specific features and results in the poor adaptability of WPD‐based feature extraction coefficients of OWPs are utilized to statistically calculate time-frequency features.
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