Personal identification systems based on electroencephalographic (EEG) signals have their
own strengths and limitations. The stability of EEG signals strongly affects such systems. The human
emotional state is one of the important factors that affects EEG signals’ stability. Stress is a major
emotional state that affects individuals’ capability to perform day-to-day tasks. The main objective of
this work is to study the effect of mental and emotional stress on such systems. Two experiments
have been performed. In the first, we used hand-crafted features (time domain, frequency domain,
and non-linear features), followed by a machine learning classifier. In the second, raw EEG signals
were used as an input for the deep learning approaches. Different types of mental and emotional
stress have been examined using two datasets, SAM 40 and DEAP. The proposed experiments proved
that performing enrollment in a relaxed or calm state and identification in a stressed state have a
negative effect on the identification system’s performance. The best achieved accuracy for the DEAP
dataset was 99.67% in the calm state and 96.67% in the stressed state. For the SAM 40 dataset, the best
accuracy was 99.67%, 93.33%, 92.5%, and 91.67% for the relaxed state and stress caused by identifying
mirror images, the Stroop color-word test, and solving arithmetic operations, respectively