Resting-state eegbased biometrics with signals features extracted by multivariate empirical mode decomposition.

Matthew King Hang Ma (Corresponding Author), Tan Lee, Manson Cheuk-Man Fong, William Shi Yuan Wang (Corresponding Author)

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review


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.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
Number of pages5
ISBN (Electronic)9781509066315
Publication statusPublished - May 2020

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149


  • Biometrics , resting-state EEG , feature extraction , multivariate empirical mode decomposition , connectivity
  • Biometrics
  • connectivity
  • feature extraction
  • multivariate empirical mode decomposition
  • resting-state EEG

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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