Cross-task cognitive workload recognition using a dynamic residual network with attention mechanism based on neurophysiological signals.

Journal: Computer methods and programs in biomedicine
Published Date: Jan 13, 2023

Abstract

BACKGROUND AND OBJECTIVE: Evaluation of human cognitive workload (CW) helps improve the user experience of human-centered systems. To provide a continuous estimation of the CW, we built a CW recognizer that maps human electroencephalograms (EEGs) to discrete CW levels with deep learning tools. However, the EEG distribution varies when humans perform different cognitive tasks. There is thus a question on the capacity for generalizing the CW recognizer across tasks. In this study, we examined the CW's performance when it was trained and tested on two EEG databases corresponding to different human-machine tasks.

Authors

  • Zhangyifan Ji
    School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, PR China.
  • Jiehao Tang
    School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, PR China.
  • Qi Wang
    Biotherapeutics Discovery Research Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China.
  • Xin Xie
    School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, PR China.
  • Jiali Liu
    Department of Clinical Oncology, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China.
  • Zhong Yin

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