TY - GEN
T1 - Resting-state EEG-based biometrics with signals features extracted by multivariate empirical mode decomposition
AU - Ma, Matthew King Hang
AU - Lee, Tan
AU - Fong, Manson Cheuk-Man
AU - Wang, William Shi Yuan
PY - 2020/4/9
Y1 - 2020/4/9
N2 - 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.
AB - 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.
KW - Biometrics , resting-state EEG , feature extraction , multivariate empirical mode decomposition , connectivity
KW - Biometrics
KW - connectivity
KW - feature extraction
KW - multivariate empirical mode decomposition
KW - resting-state EEG
UR - http://www.scopus.com/inward/record.url?scp=85089230584&partnerID=8YFLogxK
U2 - 10.1109/ICASSP40776.2020.9054351
DO - 10.1109/ICASSP40776.2020.9054351
M3 - Conference article published in proceeding or book
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 991
EP - 995
BT - 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
PB - IEEE
T2 - 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
Y2 - 4 May 2020 through 8 May 2020
ER -