March 20, 2024, 4:10 a.m. | Nibras Abo Alzahab, Lorenzo Scalise, Marco Baldi

cs.CR updates on arXiv.org arxiv.org

arXiv:2403.12644v1 Announce Type: new
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 …

accuracy arxiv authentication balance biometric brain complexity computational cs.cr eess.sp identification impact length machine performance q-bio.nc quest segment study

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