Analysis and evaluation of new identification methods considering brain waves in the same conditions but different mental states

Today, brain signals can be confidently used as a strong biometric identifier to identify individuals due to the difficulty of forgery and theft. This study examines, compares and analyzes the results of the latest methods in the field of identification using brain waves. This article also answers the question of whether the development of new hybrid methods using components of existing methods can lead to better results for identifying individuals. In summary, the use of the same database, as well as the study of different states of mental activity when recording signals, as well as the creation of different combinations of units of existing methods, creates a comprehensive and principled comparison between existing algorithms and identifies strengths and They have become weak. The results show that none of the combined methods led to acceptable results and had a large classification error. It was also found that using the raw signal at CNN neural network input would have better results than feature input. On the other hand, changing the input mode when recording a signal from the user affects the performance of the SVM classifier and CNN neural network, while changing the input mode will not have a significant effect on the RF classifier.