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Operator functional state estimation based on EEG-data-driven fuzzy model

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Abstract

This paper proposed a max–min-entropy-based fuzzy partition method for fuzzy model based estimation of human operator functional state (OFS). The optimal number of fuzzy partitions for each I/O variable of fuzzy model is determined by using the entropy criterion. The fuzzy models were constructed by using Wang–Mendel method. The OFS estimation results showed the practical usefulness of the proposed fuzzy modeling approach.

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Acknowledgments

The work was supported by the National Natural Science Foundation of China under Grant 61075070 and Key Grant 11232005. The authors wish to thank the developers of the AutoCAMS software used in our data acquisition experiments.

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Authors and Affiliations

  1. Department of Automation, East China University of Science and Technology, Shanghai, 200237, People’s Republic of China

    Jianhua Zhang & Shaozeng Yang

  2. Engineering Research Center of Optical Instrument and System, Ministry of Education, University of Shanghai for Science and Technology, Shanghai, 200093, People’s Republic of China

    Zhong Yin

  3. Institute of Cognitive Neurodynamics, East China University of Science and Technology, Shanghai, 200237, People’s Republic of China

    Rubin Wang

Authors
  1. Jianhua Zhang
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  2. Zhong Yin
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  3. Shaozeng Yang
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  4. Rubin Wang
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Correspondence to Jianhua Zhang.

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Zhang, J., Yin, Z., Yang, S. et al. Operator functional state estimation based on EEG-data-driven fuzzy model. Cogn Neurodyn 10, 375–383 (2016). https://doi.org/10.1007/s11571-016-9389-x

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  • DOI: https://doi.org/10.1007/s11571-016-9389-x

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