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

• Published in: IEEE transaction on neural networks and learning systems Vol. 27; no. 2; pp. 347 - 360
• Main Authors: Liu, Yu-Ting, Lin, Yang-Yin, Wu, Shang-Lin, Chuang, Chun-Hsiang, Lin, Chin-Teng
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.02.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adult; Artificial neural networks; Automobile Driving - psychology; Biological neural networks; Brain; Brain - physiology; Brain modeling; Brain-computer interface (BCI); driving fatigue; Dynamical systems; Dynamics; Electroencephalography; electroencephalography (EEG); Electroencephalography - methods; Fatigue; Fatigue (materials); Fatigue - diagnosis; Fatigue - psychology; Female; Forecasting; Fuzzy Logic; Fuzzy neural networks; Humans; Male; Networks; Neural networks; Neural Networks (Computer); Reaction Time - physiology; Real-time systems; recurrent fuzzy neural network (RFNN); Sleep deprivation; Vehicles; Young Adult; electroencephalography (EEG); recurrent fuzzy neural network (RFNN); driving fatigue; Brain–computer interface (BCI)
• ISSN: 2162-237X; 2162-2388
• DOI: 10.1109/TNNLS.2015.2496330

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.