Abstract
To investigate the time-varying characteristics of the multi-channels electroencephalogram (EEG) signals with 4 rhythms, a useful approach is developed to obtain the EEG’s rhythms based on the multi-resolution decomposition of wavelet transformation. Four specified rhythms can be decomposed from EEG signal in terms of wavelet packet analysis. A novel method for time-varying brain electrical activity mapping (BEAM) is also proposed using the time-varying rhythm for visualizing the dynamic EEG topography to help studying the changes of brain activities for one rhythm. Further more, in order to detect the changes of the nonlinear features of the EEG signal, wavelet packet entropy is proposed for this purpose. Both relative wavelet packet energy and wavelet packet entropy are regarded as the quantitative parameter for computing the complexity of the EEG rhythm. Some simulations and experiments using real EEG signals are carried out to show the effectiveness of the presented procedure for clinical use.
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Shen, M., Chen, J., Beadle, P.J. (2009). Analysis of Time-Varying EEG Based on Wavelet Packet Entropy. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5551. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01507-6_3
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DOI: https://doi.org/10.1007/978-3-642-01507-6_3
Publisher Name: Springer, Berlin, Heidelberg
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