A Review on Automated Detection, Classification and Clustering of Epileptic EEG Using Wavelet Transform & Soft Computing Techniques

EEG is an important tool for diagnosis, monitoring and managing various nervous disorder .It is a neurophysiologic measurement of the electric activity of bioelectric potential of brain. The electrical activity of brain changes in accordance with various parameters inside & outside environment. To study human physiology with respect to EEG, bioelelectric potential of brains is recorded with help of electrodes. These raw signals are firstly processed with help of mathematical tools in order to make them more and more informative. The informative signal thus calculated from recording is known as ERP (event related potential). These ERP data are very specific and it changes with every physiological & biological change in human body. The analysis of ERP has got a wide range of clinical importance. It serves as a base for diagnosis and detection of various diseases. ERP are also helpful in designing various emotion sensor interfaces. EEG has got diversified applications in field of biomedical engineering. This is a review paper elaborating various elementary ideas about EEG Signal pre-processing and analysis. In this paper we have collaborated various soft computing tools available for EEG signal processing. Generation of ERP from raw EEG should be very precise to be effective. With help of mathematical and computational tools we can classify specific EEG signals which are further useful for prediction & diagnosis of diseases and other emotion based applications.