Biometric identification system using EEG signals

This study focuses on using EEG signal-based behavioral biometric data to classify and identify persons. A person identification system based on a nonlinear model with excellent estimation accuracy was proposed. Six tasks were performed on all subjects utilizing EEG data collected from 64 channels and 96 subjects. The stages of this model included feature creation, feature selection, and classification. To generate features from EEG signals, statistical methods were used. Using neighborhood component analysis, the best 15 features were chosen. The selected feature vector was given as input to deep feature neural network (DNN) model and traditional ML classifier decision tree (DT), k-nearest neighbor (KNN), support vector machine (SVM), ensemble-random forest (RF) classifiers. The classification performance of the proposed methods for six tasks was separately tested and compared. The average accuracy of DT, RF, KNN, SVM and DNN classifiers for all tasks was 98.63%, 100%, 99.96%, 99.91%, and 100%, respectively. The results show that RF and DNN classifier-based models are highly effective in the classification and person identification using EEG signals.