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 nonstationary 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 [14-16]. Two types of EEG signals were used as input patterns and illustrated as case1 and case2. Within this practice the applied signal has been compared in a chronological order with divergent cases in existence in the database [17]. The signal under consideration was evaluated and distinguished the holder 100% truthfully

EEG refers to the recording of the brain’s spontaneous electrical activity over a short period of time, usually 20–40 minutes, as recorded from multiple electrodes placed on the scalp. In advance EEG signals used to be a first-line method for the diagnosis of tumors, stroke and other focal brain disorders. The structure generating the signal is not simply linear, but also involves nonlinear contributions [7, 8, 9].These non-stationary signals are may contain indicators of current disease, or even warnings about impending diseases. This work aims at providing new insights on the Electroencephalography (EEG) fragmentation problem using wavelets [2, 5]. The present work describes a computer model to provide a more accurate picture of the EEG signal processing via Wavelet Transform [16, 17, 18, 19]. The Matlab techniques have been uses which provide a system oriented scientific decision making modal [16, 17]. Within this practice the applied signal has been compared in a sequential order with dissimilar cases in attendance in the database. Special EEG signals have been considered from Physio bank [1] and Vijaya Medical Centre, Visakhapatnam, India. Analyze the signal under consideration and renowned the holder 100% truthfully.