signals, particularly electroencephalograms (EEG), to enhance information security. Using EEG as a biometric offers advantages that cannot be forgotten or forged. One approach to utilizing EEG signals for biometric purposes involves recording auditory evoked potentials (AEP). AEPs are electrical potentials that arise in response to auditory stimulation in the cerebral cortex. These signals are stimulus-dependent and can vary with the auditory stimulus, allowing these signals to be employed even if the registered signal was compromised. In this paper, discriminative features are extracted and classified using convolutional neural networks. A dataset recorded from 20 users using auditory stimulation is analyzed. The reported results demonstrate a classification accuracy of 98.99% in identification mode and an equal error rate of 1.18% in verification mode. These outcomes showcase the proposed method’s high accuracy, marking an improvement over existing methods. Furthermore, the system’s practicality is enhanced by utilizing fewer channels, and its performance is assessed by reducing the number of channels.
