Wavelet packet transform for feature extraction of EEG during mental tasks

Wavelet packet transform (WPT) based feature extraction of the electroencephalogram (EEG) is introduced. Six-channel EEG data of four subjects were recorded while they performed three different mental tasks. Approximate one-second data segments were divided and transformed to multi-scale representat...

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Bibliographic Details
Published inProceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693) Vol. 1; pp. 360 - 363 Vol.1
Main Authors Jian-Zhong Xue, Hui Zhang, Chong-Xun Zheng, Xiang-Guo Yan
Format Conference Proceeding
LanguageEnglish
Published IEEE 2003
Subjects
Bayesian methods
Biomedical engineering
Brain modeling
Data mining
Electrodes
Electroencephalography
Feature extraction
Radial basis function networks
Wavelet packets
Wavelet transforms
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Summary:Wavelet packet transform (WPT) based feature extraction of the electroencephalogram (EEG) is introduced. Six-channel EEG data of four subjects were recorded while they performed three different mental tasks. Approximate one-second data segments were divided and transformed to multi-scale representations by dyadic wavelet packet decomposition channel by channel. Power values of different sub-spaces of six-channel EEG signals formed the feature vectors. A radial basis function (RBF) network was applied to classify the three task pairs. The average classification accuracy of four subjects over three task pairs is 85.3%. Compared with the two autoregressive (AR) model methods, wavelet packet transform would be a promising method to extract features from EEG signals.
ISBN:0780378652
9780780378650
9780780381315
0780381319
DOI:10.1109/ICMLC.2003.1264502