Brain Dynamics in Predicting Driving Fatigue Using a Recurrent Self-Evolving Fuzzy Neural Network

Yu-Ting Liu, Yang-Yin Lin, Shang-Lin Wu, Chun Hsiang Chuang, Chin-Teng Lin

Research output: Contribution to journalArticlepeer-review

54 Scopus citations

Abstract

This paper proposes a generalized prediction system called a recurrent self-evolving fuzzy neural network (RSEFNN) that employs an on-line gradient descent learning rule to address the electroencephalography (EEG) regression problem in brain dynamics for driving fatigue. The cognitive states of drivers significantly affect driving safety; in particular, fatigue driving, or drowsy driving, endangers both the individual and the public. For this reason, the development of brain-computer interfaces (BCIs) that can identify drowsy driving states is a crucial and urgent topic of study. Many EEG-based BCIs have been developed as artificial auxiliary systems for use in various practical applications because of the benefits of measuring EEG signals. In the literature, the efficacy of EEG-based BCIs in recognition tasks has been limited by low resolutions. The system proposed in this paper represents the first attempt to use the recurrent fuzzy neural network (RFNN) architecture to increase adaptability in realistic EEG applications to overcome this bottleneck. This paper further analyzes brain dynamics in a simulated car driving task in a virtual-reality environment. The proposed RSEFNN model is evaluated using the generalized cross-subject approach, and the results indicate that the RSEFNN is superior to competing models regardless of the use of recurrent or nonrecurrent structures.
Original languageEnglish
Pages (from-to)347-360
Number of pages14
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume27
Issue number2
DOIs
StatePublished - Feb 2016

Keywords

  • Brain-computer interface (BCI)
  • driving fatigue
  • electroencephalography (EEG)
  • Recurrent fuzzy neural network (rfnn)

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