Journal
COGNITIVE NEURODYNAMICS
Volume 14, Issue 5, Pages 619-642Publisher
SPRINGER
DOI: 10.1007/s11571-020-09589-3
Keywords
Mental workload; Operator functional state; Physiological signals; Time-frequency analysis; Semi-supervised learning
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Funding
- [201369-100]
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The real-time assessment of mental workload (MWL) is critical for development of intelligent human-machine cooperative systems in various safety-critical applications. Although data-driven machine learning (ML) approach has shown promise in MWL recognition, there is still difficulty in acquiring a sufficient number of labeled data to train the ML models. This paper proposes a semi-supervised extreme learning machine (SS-ELM) algorithm for MWL pattern classification requiring only a small number of labeled data. The measured data analysis results show that the proposed SS-ELM paradigm can effectively improve the accuracy and efficiency of MWL classification and thus provide a competitive ML approach to utilizing a large number of unlabeled data which are available in many real-world applications.
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