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

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...