Electronic Science and Technology ›› 2021, Vol. 34 ›› Issue (3): 48-53.doi: 10.16180/j.cnki.issn1007-7820.2021.03.009

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Inter-Subject Recognition of Cognitive Workload Based on Ensemble SDAE and EEG

ZHENG Zhanpeng,YIN Zhong   

  1. School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China
  • Received:2019-12-09 Online:2021-03-15 Published:2021-03-10
  • Supported by:
    Youth Fund of National Natural Science Foundation of China(61703277);Shanghai Sailing Program(17YF1427000)


Assessing the operator's cognitive workload status in a human-machine system based on EEG signals can effectively prevent the operator's performance from degrading. This study proposes a novel inter-subject CW classifier, E-SDAE, to adapt the variations of the EEG feature distributions across multiple subjects. The E-SDAE includes two cascade-connected modules: high level personalized feature abstractions and decision fusion. The feature filters employ the base learner SDAE to abstract the EEG features from a group of individuals. The supervised CW classifier exploites the random characteristics of ELM to fuse the filtered EEG abstractions under the implementation of Q-statistics. The classification rate achieves 0.635 3 and 0.674 7 for Task 1 and Task 2, respectively, and are significantly better than several conventional CW estimators. The time complexity calculation results show that the computational workload of the E-SDAE is also acceptable for high-dimensional EEG features.

Key words: cognitive workload, stacked denoising autoencoder, extreme learning machine, human-machine system, EEG, ensemble learning

CLC Number: 

  • TP391