Estimating distribution shifts for predicting cross-subject generalization in electroencephalography-based mental workload assessment

Estimating distribution shifts for predicting cross-subject generalization in electroencephalography-based mental workload assessment

Assessment of mental workload in real-world conditions is key to ensuring the performance of workers executing tasks that demand sustained attention. Previous literature has employed electroencephalography (EEG) to this end despite having observed that EEG correlates of mental workload vary across s...

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Bibliographic Details
Main Authors: Isabela Albuquerque, João Monteiro, Olivier Rosanne, Tiago H. Falk
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-10-01
Series:Frontiers in Artificial Intelligence
Subjects:
mental workload assessment
distribution shifts
electroencephalography
cross-subject generalization
domain adaptation
Online Access:https://www.frontiersin.org/articles/10.3389/frai.2022.992732/full

Internet

https://www.frontiersin.org/articles/10.3389/frai.2022.992732/full

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