Biometric Authentication from Photic Stimulated EEG Records

• Published in: Applied artificial intelligence Vol. 35; no. 15; pp. 1407 - 1419
• Main Authors: Kasim, Ömer, Tosun, Mustafa
• Format: Journal Article
• Language: English
• Published: Philadelphia Taylor & Francis 15.12.2021; Taylor & Francis Ltd; Taylor & Francis Group
• Subjects: 1D-CNN algorithm; Algorithms; Artificial neural networks; Authentication; Biometrics; Brain; EEG-based authentication; Electroencephalography; Fraud; Machine learning; periodogram estimation method using Kaiser Window; photic stimulation; Power spectral density; Stimuli; subjects with ADHD; Visual signals
• DOI: 10.1080/08839514.2021.1981660
• ISSN: 0883-9514

Studies on brain biometrics have shown that electroencephalogram (EEG) signals encourage more secure authentication. The uniqueness, persistence, universality and robustness of EEG signals against fraud attacks offer potential for highly secure biometric systems. However, more studies are needed to improve collectibility, stability, performance, and acceptability of brain biometrics. EEG signals of healthy subjects were generally used in previous studies. However, adequate studies have not been conducted on subjects who are not mentally healthy. Moreover, EEG signals were usually recorded from healthy subjects at resting, thinking, visual stimulation, and imagery states. In this study, unlike other studies, photic stimuli with EEG data were used for the first time in order to identify both subjects with attention deficit hyperactivity disorder (ADHD) and healthy subjects. In the proposed method, power densities of 1-49 Hz frequencies of EEG segments were obtained by applying periodogram power spectral density estimation method using Kaiser Window to raw EEG data. These values were used for training of 1D Convolutional Neural Network deep learning algorithms. Classification success of the proposed method was measured as 97.17%. These results proved that EEG data obtained from subjects by applying photic stimuli can be used in EEG-based identification systems.