| Summary: | The neuronal activity has a unique genetic signature that can be used for personal identification and authentication. Continuous authentication of individuals is required in the field involving high security, such as military services, intelligence organizations and secret agencies. Electroencephalogram (EEG) based authentication method is favorable due to its uniqueness and the fact that it can be used even when the person is unconscious. In this study, we investigated the number of samples per subject required to reliably develop a biometric trait authentication system. Initially, we extracted the background EEG signals from the publicly available Temple University Hospital (TUH) database. A total of 46 statistical and frequency domain features were extracted from each EEG signal per subject. The classification was performed using the extreme Gradient Boosting (XGBoost) classifier. We varied the number of EEG signal segments per subject from 5 to 127 with an increment of 5 segments per trial. Finally, the performance parameters such as accuracy, sensitivity, specificity, precision and F-measure were obtained in each case for the biometric authentication system using the test data. Our model achieved the highest accuracy of 100% when more than 75 EEG signals per subject were considered for the analysis. We also identified alpha band power as the most efficient feature for authentication. Our results show that EEG signals can be effectively used for personal identification and authentication.
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