Signing EEG-based biometric authentication system using multivariate Fourier-Bessel series expansion-based entropies

Abstract

Data privacy and security are severe concerns in our world today. A broad range of biometric identification systems relies on physiological traits, including fingerprints, iris scans, and facial recognition for authentication. Conventional methods like signatures, passwords can easily be spoofed by an unauthorized person. We have proposed a framework for biometric identification using electroencephalogram (EEG) signals recorded during signing, as an individual can not replicate another individual’s signals. Multivariate variational mode decomposition (MVMD) is applied on multi-channel EEG signals to extract properly aligned oscillatory modes. Features are extracted using Fourier-Bessel series expansion (FBSE)-based entropies. We have extended the univariate entropy for multi-channel signals, namely, multivariate FBSE-based entropy (M-FBSE-E). The M-FBSE-E features are classified using machine learning classifiers for biometric identification. We have collected a database from 35 participants to validate our proposed model. These features are evaluated using machine learning classifiers to distinguish genuine from forged signatures. The proposed method achieves 93.4 ± 7.0 % accuracy in subject-wise and 89.4 ± 1.9 % in subject-independent settings. Experimental results show the effectiveness of the proposed framework for EEG-based biometric identification. © 2025 Elsevier Inc.