The Classification of Transient Time-Varying EEG Signals Via Wavelet Packets Decomposition
نویسندگان
چکیده
The classification of transient timevarying electroencephalography (EEG) is quite important for further understanding the brain function. In order to classify different kinds of nonstationary EEG rhythms, wavelet packet analysis is used for designing sub-band filters with specified band-passed characteristics. Four kinds of wavelet packet decomposition using Daubechies wavelet are employed to investigate the nonstationarity of clinical EEG signals. Several real EEG signals with different brain function states are analyzed and compared via the dynamic rhythms. It is indicated from the experimental results that the nonstationary characteristics of clinical brain electrical activities can be classified by using wavelet packet decomposition. The method in this paper also proposes an effective way to form the Dynamic Topographic Brain Mapping (DTBM) to present the dynamic EEG topography.
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