EEG-based Biometric Authentication Using Machine and Deep Learning Approachs : A Review

The utilization of electroencephalography (EEG) in scientific investigations related to biometric authentication has spurred intricate experiments aiming to recognize and confirm individuals based on brain activity in specific contexts. Recently, EEG has proven to be a viable signal for biometric authentication, offering significant advantages such as resilience against fraudulent attempts and immunity to coercion or pressure. Leveraging EEG signals derived from brain activity holds promise for addressing contemporary security challenges in conventional knowledge-based user authentication. This paper delves into cutting-edge approaches within EEG-based authentication. The analysis encompasses 14 distinct experiments involving EEG-based user authentication, exploring various facets including pre-processing techniques and classification methods employed in EEG biometric authentication. The study also zooms in on artificial intelligence techniques integrated into the classification process and their corresponding outcomes. The scrutinized papers span from 2019 to 2024, encompassing diverse experimental protocols and algorithms employed for classifying EEG signals. This review engenders a discourse on advanced biometric authentication methods, offering a comprehensive overview of the most recent biometric authentication techniques rooted in machine learning and deep learning, utilizing EEG data. Anticipated outcomes include the provision of a roadmap for forthcoming research endeavors, emphasizing the development of efficient and effective EEG-based user authentication methods while upholding privacy standards.