EEG Based Biometric Authentication System Using Multivariate FBSE Entropy

Data privacy and security are severe concerns in our world today. A broad range of biometrics relies on physiological traits, including fingerprints, iris scans, and facial recognition for authentication. Conventional methods like signatures, passwords can easily be spoofed. 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 EEG signals to extract properly aligned oscillatory modes from multi-channel EEG data. Features are extracted using Fourier-Bessel series expansion-based (FBSE) entropies. We have extended the univariate entropy for multi-channel signals, namely, multivariate FBSE-based entropy (M-FBSE-E). The M-FBSE-E is applied for feature extraction from the EEG signals. Machine learning-based classifiers are utilized to identify the EEG signals corresponding to original and forged signatures. We have collected a database from a reasonable number of participants to validate our proposed model. The proposed method achieved an average accuracy of 93.4$\pm$7.0\% for subject-wise EEG-based biometric identification for the fine K-nearest neighbor (KNN) classifier. The subject-independent EEG-based biometric identifier provides  89.4$\pm$1.9\% average accuracy when cosine KNN classifier is used. Experimental results show the effectiveness of the proposed framework for EEG-based biometric identification.