When Does Your Brain Know You? Segment Length and Its Impact on EEG-based Biometric Authentication Accuracy

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.