Deep Transfer Learning Model for EEG Biometric Decoding

In automated systems, biometric systems can be used for efficient and unique identification and authentication of individuals without requiring users to carry or remember any physical tokens or passwords. Biometric systems are a rapidly developing and promising technology domain. in contrasting with conventional methods like password IDs. Biometrics refer to biological measures or physical traits that can be employed to identify and authenticate individuals. The motivation to employ brain activity as a biometric identifier in automatic identification systems has increased substantially in recent years. with a specific focus on data obtained through electroencephalography (EEG). Numerous investigations have revealed the existence of discriminative characteristics in brain signals captured during different types of cognitive tasks. However, because of their high dimensional and nonstationary properties, EEG signals are inherently complex, which means that both feature extraction and classification methods must take this into consideration. In this study, a hybridization method that combined a classical classifier with a pre-trained convolutional neural network (CNN) and the short-tim