ArticleCognitive neurodynamics2020
Instantaneous mental workload assessment using time-frequency analysis and semi-supervised learning.
Article in Cognitive neurodynamics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Human-Centric Cognitive State Recognition Using Physiological Signals: A Systematic Review of Machine Learning Strategies Across Application Domains.Sensors (Basel, Switzerland) · 2025Pooled it
- Multivariate synchrosqueezing transform and time-frequency attention for mental workload classification from EEG signals.Scientific reports · 2026Article
- A survey of machine learning for estimating workload: considering unknown tasks.Frontiers in robotics and AI · 2026Review
- Continuous Assessment of Mental Workload During Complex Human-Machine Interaction: Inferring Cognitive State from Signals External to the Operator.Sensors (Basel, Switzerland) · 2025Article
- Dynamic mode decomposition of resting-state fMRI revealing abnormal brain region features in schizophrenia.Frontiers in computational neuroscience · 2025Article
- Mental workload level assessment based on compounded hysteresis effect.Cognitive neurodynamics · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.