Mental workload recognition from EEG signals via semi-supervised autoencoders

Mental workload-the cognitive effort to complete tasks-is vital in fields like system design, healthcare, and human-machine interaction. Supervised learning is often used for EEG-based workload recognition but is limited by scarce labeled data. To address this, we propose semi-supervised autoencoder...

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Vydáno v:Computer methods in biomechanics and biomedical engineering s. 1 - 14
Hlavní autoři: Liu, Qi, Jiang, Xu, Wang, Huanjie, Chen, Jingjing
Médium: Journal Article
Jazyk:angličtina
Vydáno: England 04.07.2025
Témata:
autoencoder
mental workload recognition
semi-supervised learning
EEG
ISSN:1025-5842, 1476-8259, 1476-8259
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Shrnutí:Mental workload-the cognitive effort to complete tasks-is vital in fields like system design, healthcare, and human-machine interaction. Supervised learning is often used for EEG-based workload recognition but is limited by scarce labeled data. To address this, we propose semi-supervised autoencoders that combine labeled and abundant unlabeled data. Our model integrates a supervised objective into an unsupervised autoencoder, forming a joint function that minimizes both reconstruction and prediction errors. This enhances discriminative power. To overcome vanishing/exploding gradients, we add skip connections between layers. Tested on two EEG datasets, our framework achieved high accuracy in binary mental workload classification.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:1025-5842
1476-8259
1476-8259
DOI:10.1080/10255842.2025.2523310