Relevance of wavelet transform for taxonomy of EEG signals

Electroencephalograms (EEGs) are becoming increasingly important measurements of brain activity and they have great potential for the diagnosis and treatment of mental and brain diseases and abnormalities (7). The frequency range of EEG signal is 0 to 64 Hz (8). These non-stationary signals are may contain indicators of current disease, or even warnings about impending diseases. An original investigative move toward for data mining of EEG signal based on continuous wavelet transformation (CWT) investigation is introduced and applied. This paper describes the relevance of wavelet transform (WT) model for categorization of electroencephalogram (EEG) signals which provides a system oriented scientific conclusion. Decision making was performed in two steps: development of a data bank for dissimilar EEG signals using the wavelet transform (WT) and identification of different EEG signals there in the data bank to wrap up a judgment making. Two types of EEG signals were used as input patterns and illustrated as easel and case2. Within this practice the applied signal has been compared in a chronological order with divergent cases in existence in the database. The signal under consideration was evaluated and distinguished the holder 100% truthfully.