Method for extracting time-varying rhythms of electroencephalography via wavelet packet analysis

A method is proposed for detecting time-varying rhythms of nonstationary electroencephalograms (EEG). Multi-resolution decomposition is used to investigate the transition of clinical EEG signals. Wavelet packet transformation is applied to design the filters with different frequency characteristics in order to extract different kinds of dynamic EEG rhythms. Several actual EEG signals with different brain function states are tested and analysed. The parameters of the wavelet packet transform corresponding to the rhythms are developed to reconstruct the time-varying electrical brain activity mapping. From the experimental results, the dynamic characteristics of clinical brain electrical activities can be demonstrated by using wavelet packet decomposition. The presented method can be used for the analysis of other biomedical signals