This paper provides a comprehensive review of the state-of-the-art in brain signal processing, classification, and security research conducted from 2013 to 2023. Summarize the key advances in signal processing techniques, feature extraction methods, and machine learning algorithms used for brain signal classification. Also discuss the various security challenges and solutions for protecting brain signals from unauthorized access and attacks. The review highlights the importance of developing a robust and reliable EEG-based authentication system that can handle the variability and complexity of brain signals. Also emphasize the need to develop secure and privacy-preserving brain-computer interfaces (BCIs) that protect users' sensitive brain data from potential threats. Furthermore, it critically analyses the limitations and future directions of the current research in EEG-based authentication, identifying several promising research directions, including developing explainable and interpretable machine learning models, integrating multimodal brain signals, and exploring new applications in affective computing and social signal processing. Also, this review discusses the challenges and opportunities of the future of authentication systems.