Driver Fatigue Classification with Independent Component by Entropy Rate Bound Minimization Analysis in an EEG-Based System

Rifai Chai, Ganesh R. Naik, Tuan Nghia Nguyen, Sai Ho Ling, Yvonne Tran, Ashley Craig, Hung T. Nguyen

Research output: Contribution to journalArticle

94 Citations (Scopus)

Abstract

This paper presents a two-class electroencephal-ography-based classification for classifying of driver fatigue (fatigue state versus alert state) from 43 healthy participants. The system uses independent component by entropy rate bound minimization analysis (ERBM-ICA) for the source separation, autoregressive (AR) modeling for the features extraction, and Bayesian neural network for the classification algorithm. The classification results demonstrate a sensitivity of 89.7%, a specificity of 86.8%, and an accuracy of 88.2%. The combination of ERBM-ICA (source separator), AR (feature extractor), and Bayesian neural network (classifier) provides the best outcome with a p-value < 0.05 with the highest value of area under the receiver operating curve (AUC-ROC = 0.93) against other methods such as power spectral density as feature extractor (AUC-ROC = 0.81). The results of this study suggest the method could be utilized effectively for a countermeasure device for driver fatigue identification and other adverse event applications.

Original languageEnglish
Pages (from-to)715-724
Number of pages10
JournalIEEE Journal of Biomedical and Health Informatics
Volume21
Issue number3
DOIs
Publication statusPublished - May 2017
Externally publishedYes

Keywords

  • Autoregressive model
  • Bayesian neural network
  • driver fatigue
  • electroencephalography (EEG)
  • entropy rate bound minimization
  • independent-component analysis (ICA)

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