FEATURE EXTRACTION AND CLASSIFICATION OF EEG SIGNALS

Abstract

The EEG biometric system will be superior in performance and much reliable. In the case of EEG based security system, the mere presence of a person is not enough to bypass the system. EEG of same person will differ with the state of the person and conditions under which it is recorded. The biometric security system will have a sample of EEG recorded under some standard condition. So if a person is forcefully asked to bypass the system, he will not be able to bypass it. This is not the case with most conventional security systems like fingerprinting. Here we may be able to bypass the system, by forceful placement of finger printing or using some fake finger print. The challenging part in this will be to identify and calculate the features of EEG which will give best results. This is a case dependent problem, and hence we cannot say for certain that a particular set of features will give best result. Further the classification efficiency will also depend up on the type of classifier used. in my thesis work I tried to classify EEG belonging to different persons and thereby there by trying to classify different person. If this classification works well for a large number of persons of whom some may be intruders i.e., their EEG is not in the list of EEG patterns of persons whom we have to identify, then we will be able to make a biometric security system.