search
Include:
1 Research products, page 1 of 1

Relevance
arrow_drop_down
  • Open Access
    Authors: 
    Yu-Ting Liu; Yang-Yin Lin; Shang-Lin Wu; Tsung-Yu Hsieh; Chin-Teng Lin;
    Country: Australia

    © 2015 IEEE. This study proposes an EEG-based forecasting system based on a functional-link recurrent self-evolving fuzzy neural network (FL-RSEFNN) for assessing mental fatigue during a highway driving task. Drivers' cognitive states significantly affect driving safety, especially for fatigue or drowsy driving which is one of common factors to endanger individuals and the public safety. In this study, a FL-RSEFNN employs an on-line gradient descent (GD) learning rule to address the EEG regression problem in brain dynamics for estimation of driving fatigue. We analyze brain dynamics in a car driving task, which is constructed in a simulated virtual reality (VR) environment. The EEG-based forecasting system is evaluated using the generalized cross-subject approach, and the results indicate that the FLRSEFNN is superior to state-of-The-Art models regardless of the use of recurrent or non-recurrent structures.

Include:
1 Research products, page 1 of 1
  • Open Access
    Authors: 
    Yu-Ting Liu; Yang-Yin Lin; Shang-Lin Wu; Tsung-Yu Hsieh; Chin-Teng Lin;
    Country: Australia

    © 2015 IEEE. This study proposes an EEG-based forecasting system based on a functional-link recurrent self-evolving fuzzy neural network (FL-RSEFNN) for assessing mental fatigue during a highway driving task. Drivers' cognitive states significantly affect driving safety, especially for fatigue or drowsy driving which is one of common factors to endanger individuals and the public safety. In this study, a FL-RSEFNN employs an on-line gradient descent (GD) learning rule to address the EEG regression problem in brain dynamics for estimation of driving fatigue. We analyze brain dynamics in a car driving task, which is constructed in a simulated virtual reality (VR) environment. The EEG-based forecasting system is evaluated using the generalized cross-subject approach, and the results indicate that the FLRSEFNN is superior to state-of-The-Art models regardless of the use of recurrent or non-recurrent structures.

Send a message
How can we help?
We usually respond in a few hours.