ISSN (Print) : 2320 – 3765 ISSN (Online): 2278 – 8875 International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering (An ISO 3297: 2007 Certified Organization) Vol. 2, Issue 12, December 2013 Copyright to IJAREEIE www.ijareeie.com 5959 Optimal Mother Wavelet for EEG Signal Processing Arun Chavan 1 , Dr. Mahesh Kolte 2 Assistant professor, Dept. of Biomed Engg., VIT, Wadala, Mumbai, India 1 Prof. & Head , Dept. of EXTC, MIT, Pune, India 2 ABSTRACT: In the paper, wavelet analysis is applied to EEG. The wavelet transform is used to represent essential characteristics of EEG spikes and spike wave(SSW) complex with few coefficients. Since spikes contain high frequency energy, they will be represented in particular scale localized in a small time window. The wave portion of the spike wave complex will be represented in a lower scale of WT, covering wider span of time. Thus with the proper selection of WT scales and time spans a fewer number of WT coefficients may be used to represent the SSW complexes[6].In order to reduce the false detection, we can extend the contextual information by referring to other signals such as EOG for information about eye movements, EMG (Electro Myo Gram) for information about muscle electrical activity. With this it is possible to reduce greatly the false detection of sharp transients due to artifacts.[3] Keywords: Wavelet Analysis, EEG, STFT, WT. I.INTRODUCTION Wavelets are an efficient tool for analysis of short-time changes in signal morphology. As pointed out by Unser and Aldroubi in [8], the preferred type of wavelet transform for signal analysis is the redundant one that is continuous wavelet transform in opposition to the non-redundant type corresponding to the expansion on orthogonal bases ( multi resolution analysis). The reason is that the CWT allows decomposition on an arbitrary scale. Thus, frequency bands of interest can be studied properly at chosen resolution. Wavelet theory provides a unified framework for a number of techniques developed for various signal-processing applications. Particularly, it is of immerse interest for the analysis of non-stationary signals like EEG, because it provides an alternative to the classical Short-Time Fourier Transform (STFT) or Gabor transform. The basic difference is, in contrast to the STFT, which uses a single analysis window, the Wavelet Transform (WT) uses short windows at high frequencies and long windows at lower frequencies. This is similar to “Constant Q” or Constant relative bandwidth frequency analysis.[6]For some applications WT can be seen as signal decomposition onto a set of basis functions. Basis functions called Wavelets are obtained by a single prototype wavelet by dilation and contraction (Scaling) as well as shifts. The prototype wavelet can be thought of as a band pass filter, and the constant Q property of the other band pass filter (Wavelets) follows because they are scaled version of prototype.[6]Therefore, in a WT, the notion of the scale is introduced as an alternative to frequency leading to time scale representation. It means that a signal is mapped into a time-scale plane as compared to the time frequency plane used in the STFT.[6] II. LITERATURE SURVEY Despite advances in the development of drugs for the control of seizures, there are still many individuals with pharmacoresistant epilepsy . Recent conferences suggested the use of animal models of chronic epilepsy to facilitate the development and testing of more efficacious drugs [13]. Because of the volume, human review of the data is impractical. An automated system is required to increase accuracy and speed of analysis. Artificial neural networks have been used for EEG analysis for disease diagnosis, sleep-stage classification, mental-state classification, artifact recognition, and the detection of epileptiform discharges. The radial basis function (RBF) neural networks are used to identify seizure or preseizure states. As input to the RBF networks we can use raw EEG data, coefficients from wavelet decomposition of the raw data. An RBF network consists of an input layer, a single hidden layer, and an output node [12]. In addition to demonstrating a reliable seizure identification method, the possibility of predicting an impending seizure before clinical onset can also be investigated. The period during a seizure is known as the ictal state, while the periods of normal brain activity between seizures are called inter-ictal. A third state, referred to as pre-ictal, has been defined as the period just