標題: EEG-based Driving Fatigue Prediction System Using Functional-link-based Fuzzy Neural Network
作者: Liu, Yu-Ting
Lin, Yang-Yin
Wu, Shang-Lin
Chuang, Chun-Hsiang
Prasad, Mukesh
Lin, Chin-Teng
資訊工程學系
電控工程研究所
腦科學研究中心
Department of Computer Science
Institute of Electrical and Control Engineering
Brain Research Center
關鍵字: Electroencephalography (EEG);Driving fatigue;functional link neural networks (FLNNs);fuzzy neural networks (FNNs)
公開日期: 2014
摘要: This study presents a fuzzy prediction system for the forecasting and estimation of driving fatigue, which utilizes a functional-link-based fuzzy neural network (FLFNN) to predict the drowsiness (DS) level in car driving task. The cognitive state in car driving task is one of key issue in cognitive neuroscience because fatigue driving usually causes enormous losses nowadays. The damage can be extremely decreased by the assistant of various artificial systems. Many Electroencephalography (EEG)based interfaces have been widely developed recently due to its convenient measurement and real-time response. However, the improvement of recognition accuracy is still confined to some specific problems (e.g., individual difference). In order to solve this issue, the proposed methodology in this paper utilizes a non-linear fuzzy neural network structure to increase the adaptability in the real-world environment. Therefore, this study is further to analysis the brain activities in car driving, which is constructed in a simulated three-dimensional virtual-reality (VR) environment. Finally, through the development of brain cognitive model in car driving task, this system can predict the cognitive state effectively before drivers\' action and then provide correct feedback to users. This study also compared the result with the-state-of-art systems, including Linear Regression (LR), Multi-Layer Perceptron Neural Network (MLPNN) and Support Vector Regression (SVR). Results of this study demonstrate the effectiveness of the proposed FLFNN model.
URI: http://hdl.handle.net/11536/135079
ISBN: 978-1-4799-1484-5
ISSN: 2161-4393
期刊: PROCEEDINGS OF THE 2014 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)
起始頁: 4109
結束頁: 4113
Appears in Collections:Conferences Paper