標題: Driving Fatigue Prediction with Pre-Event Electroencephalography (EEG) via a Recurrent Fuzzy Neural Network
作者: Liu, Yu-Ting
Wu, Shang-Lin
Chou, Kuang-Pen
Lin, Yang-Yin
Lu, Jie
Zhang, Guangquan
Lin, Wen-Chieh
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);recurrent fuzzy neural network (RFNN);brain-computer interface (BCI);driving safety;fatigue prediction
公開日期: 2016
摘要: We propose an electroencephalography (EEG) prediction system based on a recurrent fuzzy neural network (RFNN) architecture to assess drivers\' fatigue degrees during a virtual-reality (VR) dynamic driving environment. Prediction of fatigue degrees is a crucial and arduous biomedical issue for driving safety, which has attracted growing attention of the research community in the recent past. Meanwhile, combined with the benefits of measuring EEG signals facilitates, many EEG-based brain-computer interfaces (BCIs) have been developed for use in real-time mental assessment. In the literature, EEG signals are severely blended with stochastic noise; therefore, the performance of BCIs is constrained by low resolution in recognition tasks. For this rationale, independent component analysis (ICA) is usually used to find a source mapping from original data that has been blended with unrelated artificial noise. However, the mechanism of ICA cannot be used in real-time BCI design. To overcome this bottleneck, the proposed system in this paper utilizes a recurrent self-evolving fuzzy neural work (RSEFNN) to increase memory capability for adaptive noise cancellation when assessing drivers\' mental states during a car driving task. The experimental results without the use of ICA procedure indicate that the proposed RSEFNN model remains superior performance compared with the state-of-thearts models.
URI: http://hdl.handle.net/11536/134566
ISBN: 978-1-5090-0625-0
ISSN: 1544-5615
期刊: 2016 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE)
起始頁: 2488
結束頁: 2494
顯示於類別:會議論文