Self-Paced Dynamic Infinite Mixture Model for Fatigue Evaluation of Pilots' Brains

Authors

Edmond Q. Wu, Shanghai Jiao Tong University
Mengchu Zhou, Newark College of Engineering
Dewen Hu, National University of Defense Technology China
Longjun Zhu, Shanghai Normal University
Zhiri Tang, Wuhan University
Xu Yi Qiu, China National Aeronautical Radio Electronics Research Institute
Ping Yu Deng, China National Aeronautical Radio Electronics Research Institute
Li Min Zhu, Shanghai Jiao Tong University
He Ren, Commercial Aircraft Corporation of China, Ltd.

Document Type

Article

Publication Date

7-1-2022

Abstract

Current brain cognitive models are insufficient in handling outliers and dynamics of electroencephalogram (EEG) signals. This article presents a novel self-paced dynamic infinite mixture model to infer the dynamics of EEG fatigue signals. The instantaneous spectrum features provided by ensemble wavelet transform and Hilbert transform are extracted to form four fatigue indicators. The covariance of log likelihood of the complete data is proposed to accurately identify similar components and dynamics of the developed mixture model. Compared with its seven peers, the proposed model shows better performance in automatically identifying a pilot's brain workload.

Identifier

85097947772 (Scopus)

Publication Title

IEEE Transactions on Cybernetics

External Full Text Location

https://doi.org/10.1109/TCYB.2020.3033005

e-ISSN

21682275

ISSN

21682267

PubMed ID

33284758

First Page

5623

Last Page

5638

Issue

7

Volume

52

Recommended Citation

Wu, Edmond Q.; Zhou, Mengchu; Hu, Dewen; Zhu, Longjun; Tang, Zhiri; Qiu, Xu Yi; Deng, Ping Yu; Zhu, Li Min; and Ren, He, "Self-Paced Dynamic Infinite Mixture Model for Fatigue Evaluation of Pilots' Brains" (2022). Faculty Publications. 2866.
https://digitalcommons.njit.edu/fac_pubs/2866