TY - GEN
T1 - Driving fatigue prediction with pre-event electroencephalography (EEG) via a recurrent fuzzy neural network
AU - Liu, Yu Ting
AU - Wu, Shang Lin
AU - Chou, Kuang Pen
AU - Lin, Yang Yin
AU - Lu, Jie
AU - Zhang, Guangquan
AU - Lin, Wen-Chieh
AU - Lin, Chin Teng
PY - 2016/11/7
Y1 - 2016/11/7
N2 - 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.
AB - 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.
KW - Brain-computer interface (BCI)
KW - Driving safety
KW - Electroencephalography (EEG)
KW - Fatigue prediction
KW - Recurrent fuzzy neural network (rfnn)
UR - http://www.scopus.com/inward/record.url?scp=85006814698&partnerID=8YFLogxK
U2 - 10.1109/FUZZ-IEEE.2016.7738006
DO - 10.1109/FUZZ-IEEE.2016.7738006
M3 - Conference contribution
AN - SCOPUS:85006814698
T3 - 2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016
SP - 2488
EP - 2494
BT - 2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016
Y2 - 24 July 2016 through 29 July 2016
ER -