Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume 27, Issue 2, Pages 347-360Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2015.2496330
Keywords
Brain-computer interface (BCI); driving fatigue; electroencephalography (EEG); recurrent fuzzy neural network (RFNN)
Categories
Funding
- Aiming for the Top University Plan within National Chiao Tung University through Ministry of Education, Taiwan [104W963]
- University System of Taiwan-UC San Diego International Center of Excellence in Advanced Bio-Engineering within Ministry of Science and Technology through I-RiCE Program [MOST 103-2911-I-009-101, MOST 104-2627-E-009-001]
- Cognition and Neuroergonomics Collaborative Technology Alliance Annual Program Plan through Army Research Laboratory [W911NF-10-2-0022]
- Tung Thih Electronic Fellowship
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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.
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