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

Edmond Q. Wu, Meng Chu Zhou, Dewen Hu, Longjun Zhu, Zhiri Tang, Xu Yi Qiu, Ping Yu Deng, Li Min Zhu, He Ren

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
JournalIEEE Transactions on Cybernetics
DOIs
StateAccepted/In press - 2020

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

Keywords

  • Brain fatigue
  • dynamic mixture model
  • electroencephalogram (EEG)
  • machine learning
  • self-paced learning

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