Biometry Based on EEG Signals

摘要

The use of EEG as a unique character to identify people in recent years has been considered. Biometric systems are generally operated into Identification and Verification. In this paper the feasibility of the personal recognition in verification mode were investigated, by using EEG signals based on P300, and also, the people's identifying quality, in identification mode and especially in single trial, was improved with Neural Network and Support Vector Machine as classifier. Results in the single trial were increased from 56.2% in the previous study, to 81.4%. Meanwhile in a maximum state, 100% correctly classified was performed by only 5 times averaging of EEG. It was observed that using support vector machine has more sustainable results as a classifier. Nine different pictures have been shown to five participants randomly; before the test was examined, each subject had already chosen one or some pictures in order to P300 occurrence took place in examination.