Physiological-signal-based mental workload estimation via transfer dynamical autoencoders in a deep learning framework-Reference-Cited by-同舟云学术

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Physiological-signal-based mental workload estimation via transfer dynamical autoencoders in a deep learning framework

Published:2019-06 Issue: Volume:347 Page:212-229
ISSN:0925-2312
Container-title:Neurocomputing
language:en
Short-container-title:Neurocomputing

Author:

Yin Zhong,Zhao Mengyuan,Zhang Wei^ORCID,Wang Yongxiong,Wang Yagang,Zhang Jianhua^ORCID

Funder

National Natural Science Foundation of China

Shanghai Sailing Program

Shanghai Philosophy and Social Sciences Fund

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference43 articles.

1. Operator Functional State as a Framework for the Assessment of Performance Degradation;Hockey,2003

2. Integrating cognitive load theory and concepts of human-computer interaction;Hollender;Comput. Hum. Behav.,2010

3. Measuring neurophysiological signals in aircraft pilots and car drivers for the assessment of mental workload, fatigue and drowsiness;Borghini;Neurosci. Biobehav. Rev.,2014

4. Measurement of attentional reserve and mental effort for cognitive workload assessment under various task demands during dual-task walking;Shaw;Biol. Psychol.,2018

5. Towards an effective cross-task mental workload recognition model using electroencephalography based on feature selection and support vector machine regression;Ke;Int. J. Psychophysiol.,2015

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4. Influence of Automation Level of Human-machine System on Operators’ Mental Load;Journal of Safety and Sustainability;2023-12

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