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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Bibliographic Details
Published in:	Computer methods in biomechanics and biomedical engineering pp. 1 - 14
Main Authors:	Liu, Qi, Jiang, Xu, Wang, Huanjie, Chen, Jingjing
Format:	Journal Article
Language:	English
Published:	England 04.07.2025
Subjects:	autoencoder mental workload recognition semi-supervised learning EEG
ISSN:	1025-5842, 1476-8259, 1476-8259
Online Access:	Get full text
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Summary:	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.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1025-5842 1476-8259 1476-8259
DOI:	10.1080/10255842.2025.2523310