When Does Your Brain Know You? Segment Length and Its Impact on EEG-based Biometric Authentication Accuracy
Nibras Abo Alzahab, Lorenzo Scalise, Marco Baldi

TL;DR
This study explores how segment length affects EEG-based biometric authentication accuracy, aiming to optimize data segmentation for better security and efficiency in real-world applications.
Contribution
It identifies optimal EEG segment durations for biometric authentication and evaluates their impact using advanced machine learning models.
Findings
Optimal segment lengths improve authentication accuracy.
Trade-offs between performance and computational complexity are analyzed.
Practical guidelines for deploying EEG biometrics in real-world settings.
Abstract
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.
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Taxonomy
TopicsEEG and Brain-Computer Interfaces
