Exploring Spatial Information for EEG-Based User Authentication: A ShallowNet Approach

As biometric authentication has become common-place in recent years, electroencephalography (EEG)-based user authentication research has gained significant attention from researchers. The practicality of enhancing authentication requires users to be classified effectively with fewer channels, which necessitates research on spatial information across brain regions to identify optimal channels for authentication protocols. While previous research has examined different acquisition protocols, comprehensive studies that have investigated the influence of spatial information from various brain regions are lacking. Therefore, in this study, a convolutional neural network (CNN)-based deep learning model was trained using EEG signals obtained from one of the most promising protocols, the steady-state visual evoked potential (SSVEP) experiment. By using a pre-trained model, the effect of spatial information specific to each brain region was examined by classifying data masked for each respective region. Through this study, we confirmed that the amount of information used to classify users is distributed more in the occipital and parietal regions than in the frontal and temporal regions. These insights suggest that focusing on channels in the occipital and parietal regions may be important in SSVEP-based user authentication research.