The growing reliance on biometric authentication in security-critical applications has exposed limitations in conventional modalities such as fingerprints, facial recognition, and iris scans—particularly their vulnerability to spoofing and limited capacity for verifying user liveness. This has prompted the search for more secure and intrinsic biometric traits. EEG signals, generated by the brain’s electrical activity, offer a novel and promising solution due to their uniqueness, difficulty to replicate, and dynamic nature under cognitive stimulation. The ability to use mental tasks to evoke person-specific electrophysiological patterns presents an opportunity to develop authentication systems that are both highly secure and resilient to impersonation attacks. This research is motivated by the potential of integrating EEG biometrics with advanced machine learning techniques to create a robust, spoof-resistant identity verification framework suitable for real-world, high-security environments.