Resting-State EEG-Based Biometrics with Signals Features Extracted by Multivariate Empirical Mode Decomposition
Matthew King-Hang Ma, Tan Lee, Manson Cheuk-Man Fong, William Shi-Yuan Wang
Abstract
EEG-based biometrics has gained great attention in recent years due to its superiority over traditional biometrics in terms of its resistance to circumvention. While there are numerous choices of data acquisition protocol, the present study is carried out with the least demanding resting-state condition. Motivated by neurophysiological knowledge, a type of novel feature, namely the intrinsic mode correlation (IMCOR), is proposed. It is designed by combining the nonstationary multivariate empirical mode decomposition (NA-MEMD) and the concept of brain connectivity. With machine learning classifiers, our system yields promising performance in a 81-class classification (F1 score: 0.99) within a single session. For 32-class cross-session classification, an F1 score of 0.55 is attained. The results suggest that the proposed method might be vulnerable to temporal effects and between-session variability. This study highlights the uniqueness of the proposed non-stationary and connectivity-based feature and demonstrated its success as a biometrics. Further investigation is needed to make the method practically useful.
BibTeX
@inproceedings{icassp2020_restingstateeegb,
title = {Resting-State EEG-Based Biometrics with Signals Features Extracted by Multivariate Empirical Mode Decomposition},
author = {Matthew King-Hang Ma and Tan Lee and Manson Cheuk-Man Fong and William Shi-Yuan Wang},
booktitle = {ICASSP 2020},
year = {2020}
}