Instantaneous mental workload assessment using time–frequency analysis and semi-supervised learning
Peer reviewed, Journal article
Published version
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https://hdl.handle.net/11250/2756435Utgivelsesdato
2020-05-12Metadata
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Originalversjon
Cognitive Neurodynamics. 2020, 14 (5), 619-642). https://doi.org/10.1007/s11571-020-09589-3Sammendrag
The real-time assessment of mental workload (MWL) is critical for development of intelligent human–machine cooper- ative 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 classi- fication 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.