Electroencephalography (EEG) signals provide a unique opportunity for personal identification due to their inherent permanence and uniqueness. However, EEG data are complex, multidimensional, non-stationary, and time-dependent, making feature extraction and classification particularly challenging. This study proposes a framework by integrating autoencoder-based feature extraction with self-attention mechanism for EEG-based personal identification utilizing the proposed EEG data cube spatio-temporal stream representation. Three types of stimuli were applied: auditory, cognitive, and resting state. EEG data were collected in-house from college students in two sessions separated by 10 days. A longitudinal design was employed, with Session-1 data used for training and Session-2 data for testing, ensuring robustness against inter-session variability. Extracted features were subsequently classified using Support Vector Machines (SVM), Artificial Neural Networks (ANN), k-Nearest Neighbors (KNN), and Random Forests (RF). SVM achieved accuracy scores 97.62% when identifying two subject, 82.54% when identifying five subjects and 62.90% when identifying seven subjects using the resting state neural patterns. This research highlights the feasibility of using deep learning networks strengthened with a self-attention mechanism for advancing biometric identification through EEG signals.