EEG-Driven Biometric Authentication for Investigation of Fourier Synchrosqueezed Transform-ICA Robust Framework
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Dosyalar
Tarih
2023
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer Heidelberg
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Biometric 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.
Açıklama
Anahtar Kelimeler
Fourier Synchrosqueezed Transform; Independent Component Analysis; Biometric; Eeg; Deep Learning
