The study by Al-Nafjan et al,. evaluates EEG (electroencephalography) brainwaves as an authentication method for biometrics and the cybersecurity industry. It combines event-related potentials (ERPs) and power spectral densities (PSDs) within a convolutional neural network (CNN) to capture temporal and spectral EEG signal dynamics. The authors used the public Brainwave Authentication dataset with 36 participants and 5 ERP tasks according to P300 (attention) and N400 (semantic processing) to segment epochs. For instance, the authors would show pictures or words to the participants with the EEG recording brainwaves, to pull ERP and PSD to feed into the CNN. The results showed effectiveness overall, with the best on N400-Faces task with 99% accuracy. The P300 task was 92-93% effective. The N400-sentences was least effective with 76% accuracy. However, results also suggested that brain signals are state-dependent, fatigue and stress being factors that can alter aspects of brainwaves. EEG biometrics seem promising when combined with certain stimuli, and could also be strong if combined with current biometrics.