Cognitive state recognition using wavelet singular entropy and ARMA entropy with AFPA optimized GP classification

Zhengxiang Cai, Qi Wu, Dan Huang, Lu Ding, Biting Yu, Chun Hung Roberts Law, Jiayang Huang, Shan Fu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

6 Citations (Scopus)


Cognitive state, which is the inner mental state of a person while interacting with an artificial system through man-machine interface, can be affected by various factors, such as fatigue, stress, mental workload, attention deficit, and executive function, among others, which can lead to errors, accidents, or even disasters. One practical solution to this problem is to monitor and recognize the cognitive state of subjects via physiological signals. In this study, a hybrid adaptive flower pollination algorithm-Gaussian process model is proposed to recognize the cognitive state of in-flight pilots. Instead of using the traditional conjugate gradient technique to find optimal hyperparameters, an improved flower pollination algorithm is proposed. The adaptive Lévy strategy is then used to increase the robustness of this algorithm, as well as to enhance the global optimization and generalization capability of the Gaussian process model. In addition to conventional features in the time-frequency domain, a novel set of features involving wavelet singular entropy and autoregressive-moving average entropy is proposed to improve classification accuracy. Experiments are performed through flight simulations in a full flight simulator with six degrees of freedom. Comparable experimental results validate the feasibility of the proposed method for recognizing cognitive state and provide a wide range of conclusions on the feature selection and feature patterns of cognitive state.
Original languageEnglish
Pages (from-to)29-44
Number of pages16
Publication statusPublished - 12 Jul 2016


  • Autoregressive-moving average
  • Cognitive state
  • Flight simulation
  • Gaussian process
  • Wavelet singular entropy

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence


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