Deep learning has demonstrated a remarkable ability to extract high-level features and uncover complex latent dependencies, making it highly effective for various tasks. However, the success of deep learning models typically hinges on the availability of large datasets for training, which poses a significant challenge in real-world applications. In the domain of brain fingerprint identification, where data from multiple individuals is often sparse and each class contains only a few samples, deep learning models face difficulties in achieving reliable performance. This chapter presents a Convolutional Tensor-Train Neural Network (CTNN) designed to tackle these challenges in the context of multi-task brain fingerprint identification with limited training samples. The method integrates a convolutional neural network (CNN) with a depthwise separable convolution mechanism to extract local temporal and spatial features from the brainprint, focusing on subtle neural patterns that distinguish individuals. To further enhance the model’s ability to capture complex interdependencies, the TensorNet (TN) component is introduced, employing low-rank tensor decomposition to model multilinear interactions between different features. This representation enables the model to efficiently integrate local information into a global feature space with minimal parameters, making it particularly effective in scenarios with small-sample sizes. The CTNN approach not only addresses the challenge of limited data but also excels in multi-task learning, enabling it to extract shared features across different recognition tasks. This capability is crucial for real-world applications where brainprint identification must function across diverse individuals and conditions. Furthermore, the model provides interpretability by identifying key brain regions, with seven specific channels being dominant in the recognition tasks, thus offering valuable insights into the neural biomarkers underlying brain fingerprint identification.