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KMID : 1144120120020010024
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Biomedical Engineering Letters
2012 Volume.2 No. 1 p.24 ~ p.37
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Wrapper based wavelet feature optimization for EEG signals
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Garg Girisha
Singh Vijander
Gupta J. R. P.
Mittal A. P.
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Abstract
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Purpose: In this paper a computationally efficient wrapper based Wavelet Feature Optimization (WFO) is developed. The algorithm is developed for the classification of high dimensional EEG signals which may suffer from the curse of dimensionality and sub optimal feature selection.
Methods: The key design phases of the algorithm involve: 1) Feature Transformation of the original EEG signals using Discrete Wavelet Transform; 2) Feature Extraction using the concept of Relative Wavelet Energy (RWE) 3) Selecting the optimal subset of the RWE features using wrapper approach. In contrast to the methods guided by the filter technique of feature selection, this approach uses the wrapper based method to select the optimal and a very low dimensional feature space from the wavelet features.
Results: The highlight of the algorithm is that in addition to increase the computational efficiency, it also enhances the predictive power of the system without any loss of relevant information. This paper includes the experimentation performed on EEG datasets using WFO algorithm.
Conclusions: The algorithm gives consistent and excellent performance for the EEG datasets. The feature sets obtained with the help of WFO are also tested using mutual information methods to confirm the optimality of the wavelet feature subset.
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KEYWORD
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Wrapper, Wavelet, EEG, Feature selection, Dimension reduction
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