This chapter presents an advanced brain fingerprint identification method that leverages electroencephalography (EEG) signals, referred to as the Residual and Multi-scale Spatio-Temporal Convolution Neural Network (RAMST-CNN). Brain fingerprinting, a unique approach to biometric identification, relies on the inherent characteristics of EEG signals to identify individuals based on their cognitive and neurological patterns. The RAMST-CNN model integrates several cutting-edge deep learning techniques to enhance its performance in feature extraction, including Residual Learning (RL), Multi-scale Grouping Convolution (MGC), Global Average Pooling (GAP), and Batch Normalization (BN). These components work synergistically to enable robust extraction of spatio-temporal features from EEG data, making the model highly effective in capturing both spatial and temporal dynamics that are crucial for brain fingerprinting. The task-independent design of the RAMST-CNN significantly alleviates the complexities traditionally associated with manual feature selection and extraction, which have often been a bottleneck in previous brain fingerprinting systems. By automating feature learning, the method minimizes human intervention and ensures consistent feature representation across different cognitive tasks. The model’s efficiency is further enhanced by its lightweight architecture, which facilitates high-performance recognition without the need for large computational resources. Extensive comparative evaluations across a range of datasets and against various state-of-the-art methods demonstrate the superior performance of RAMST-CNN in terms of accuracy, robustness, and generalization. The results underscore the model’s potential for real-world brain fingerprinting applications, highlighting its ability to deliver high recognition accuracy in both single-session and multi-task scenarios.