SVM and Ensemble-SVM in EEG-based Person Identification

Abstract

Biometric person identification is getting more effective and popular because of Electroencephalography (EEG). EEG signals can be captured from human scalp invasively or non-invasively with the help of electrodes. EEG-based biometric system is more secure and unique for person identification .In this paper, we have used two different state to explore the adaptive and uniqueness of the EEG-based biometric sys-tem.We have used eyes open (EO) state as well as eyes closed (EC) state of a EEG motor imagery publicly available dataset of 109 users.The model is trained and test with EO and EC state alternatively to provethe reliability and robustness of the model. The biometric person identification model have been designed using Support Vector machine (SVM)for classification .We achieved a notable person identification rate of 96%(EO) and 91.78% (EC) using SVM with Radial Basis Function (RBF) kernel.We have also used Ensemble Support Vector Machine (ESVM)to enhance the performance of person identification and observed the average performance accuracy of 96.16% with n number of classifier.