TY - GEN
T1 - Exploiting Mallows Distance to Quantify EEG Distribution for Personal Identification
AU - Chen, Baicheng
AU - Cho, Kun Woo
AU - Xu, Chenhan
AU - Lin, Feng
AU - Jin, Zhanpeng
AU - Xu, Wenyao
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - Electroencephalogram (EEG) activity from the brain is a promising biological marker that can serve as personal identification. Despite substantial efforts, it still remains unsolved problems to quantify EEG feature distribution for brain biometrics due to the complexity of the brain. In this study, we attempt to tackle EEG-based identification challenges by exploiting a novel distribution model. The distribution dissimilarity is measured by Mallows distance, a cluster similarity sensitive distance that is robust to signal noises. Specifically, EEG signals are decomposed through several statistical feature extraction methods, autoregressive (AR) model, discrete wavelet transform (DWT), and fast Fourier transform (FFT). With the dataset obtained from the real-world application, our proposed system achieves the f-score accuracy of 96.18% and half total error rate of 2.223%, which demonstrates the feasibility and effectiveness of utilizing EEG biometrics for personal identification applications.
AB - Electroencephalogram (EEG) activity from the brain is a promising biological marker that can serve as personal identification. Despite substantial efforts, it still remains unsolved problems to quantify EEG feature distribution for brain biometrics due to the complexity of the brain. In this study, we attempt to tackle EEG-based identification challenges by exploiting a novel distribution model. The distribution dissimilarity is measured by Mallows distance, a cluster similarity sensitive distance that is robust to signal noises. Specifically, EEG signals are decomposed through several statistical feature extraction methods, autoregressive (AR) model, discrete wavelet transform (DWT), and fast Fourier transform (FFT). With the dataset obtained from the real-world application, our proposed system achieves the f-score accuracy of 96.18% and half total error rate of 2.223%, which demonstrates the feasibility and effectiveness of utilizing EEG biometrics for personal identification applications.
KW - Biometrics
KW - Secure Authentication
KW - Wearable Computing
UR - https://www.scopus.com/pages/publications/85078037338
U2 - 10.1109/DSC47296.2019.8937586
DO - 10.1109/DSC47296.2019.8937586
M3 - Conference contribution
AN - SCOPUS:85078037338
T3 - 2019 IEEE Conference on Dependable and Secure Computing, DSC 2019 - Proceedings
BT - 2019 IEEE Conference on Dependable and Secure Computing, DSC 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd IEEE Conference on Dependable and Secure Computing, DSC 2019
Y2 - 18 November 2019 through 20 November 2019
ER -