  In the quest for optimal EEG-based biometric authentication, this study
investigates the pivotal balance for accurate identification without
sacrificing performance or adding unnecessary computational complexity. Through
a methodical exploration of segment durations, and employing a variety of
sophisticated machine learning models, the research seeks to pinpoint a
threshold where EEG data provides maximum informational yield for
authentication purposes. The findings are set to advance the field of
non-invasive biometric technologies, proposing a practical approach to secure
and user-friendly identity verification systems while also raising
considerations for the real-world application of EEG-based biometric
authentication beyond controlled environments.
