EEG-Driven Biometric Authentication for Investigation of Fourier Synchrosqueezed Transform-ICA Robust Framework

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dc.authoridözer, zeynep/0000-0001-8654-0902
dc.authoridolmez, emre/0000-0003-1686-0251
dc.authoridCETIN, ONURSAL/0000-0001-5220-3959
dc.authorwosidözer, zeynep/AFZ-8205-2022
dc.contributor.authorGorur, Kutlucan
dc.contributor.authorOlmez, Emre
dc.contributor.authorOzer, Zeynep
dc.contributor.authorCetin, Onursal
dc.date.accessioned2024-03-09T18:48:17Z
dc.date.available2024-03-09T18:48:17Z
dc.date.issued2023
dc.departmentİzmir Bakırçay Üniversitesien_US
dc.description.abstractBiometric authentication systems have recently been gaining increased attention as an integral part of modern civic life. Security surveillance systems encompass broad categories ranging from unlocking mobile phones to personal identification authentication. Most state-of-the-art biometric systems employ fingerprint, face recognition, iris scanner systems, etc. These biometric systems are popular because of their easy-to-use and highly accurate in-person authentication. However, they do not guarantee liveness detection and can be easily deceived. Electroencephalography (EEG)-based biometric systems have dynamic and nonstationary characteristics during liveness, which offer unique, universal, and robust approaches against fraud attacks and thus present a high potential for secure biometric authentication systems. This study aimed to investigate the performance of a new framework for an FSST-ICA-based EEG-biometric authentication approach over motor imagery (MI) signals using an ensemble of LSTM deep models. The Fourier synchrosqueezed transform (FSST) was performed to implement feature extraction by analyzing the time-frequency (TF) matrix properties of the EEG signals. Synchrosqueezing transform was adopted as a feasible way to provide compact component localization capabilities for dynamic and nonstationary EEG signals with detailed spectral properties in the TF domain. Independent component analysis (ICA) was also carried out to decompose EEG multichannel sources in order to improve the true acceptance rate (TAR) and false acceptance rate (FAR) performance, as well as the correct recognition rate. The biometric authentication outcomes indicated that a high average accuracy (99.54%), sensitivity (99.81%), and specificity (99.41%) had been obtained regarding the one-versus-others discrimination among seven individuals via MI-EEG raw and subband (< 30 Hz) signals. Furthermore, high average TAR (97.8%) and low FAR (0%) values demonstrated robustness against multiple trials. To the best of our knowledge, an FSST-ICA framework for the EEG-based biometric approach using an ensemble of LSTM deep models has not been explored to date in the current literature. The study presents a highly secure and low-cost biometric system having broad fields of application. The effectiveness of the proposed framework over the spatiotemporal dynamics of the MI-EEGs was also evaluated by examining the broad statistical methods. This appears to be the first attempt to validate the discrimination of individuals in both raw and time-frequency features using extensive statistical analysis.en_US
dc.identifier.doi10.1007/s13369-023-07798-6
dc.identifier.endpage10923en_US
dc.identifier.issn2193-567X
dc.identifier.issn2191-4281
dc.identifier.issue8en_US
dc.identifier.scopus2-s2.0-85151724644en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage10901en_US
dc.identifier.urihttps://doi.org/10.1007/s13369-023-07798-6
dc.identifier.urihttps://hdl.handle.net/20.500.14034/1276
dc.identifier.volume48en_US
dc.identifier.wosWOS:000963040500002en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Heidelbergen_US
dc.relation.ispartofArabian Journal For Science and Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectFourier Synchrosqueezed Transform; Independent Component Analysis; Biometric; Eeg; Deep Learningen_US
dc.titleEEG-Driven Biometric Authentication for Investigation of Fourier Synchrosqueezed Transform-ICA Robust Frameworken_US
dc.typeArticleen_US

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