Resting-state EEG-based biometrics with signals features extracted by multivariate empirical mode decomposition

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 94-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 nonstationary
and connectivity-based feature and demonstrated
its success as a biometrics. Further investigation is needed to
make the method practically useful.