Cargando…
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...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9576998/ https://www.ncbi.nlm.nih.gov/pubmed/36267659 http://dx.doi.org/10.3389/frai.2022.992732 |
_version_ | 1784811656903131136 |
---|---|
author | Albuquerque, Isabela Monteiro, João Rosanne, Olivier Falk, Tiago H. |
author_facet | Albuquerque, Isabela Monteiro, João Rosanne, Olivier Falk, Tiago H. |
author_sort | Albuquerque, Isabela |
collection | PubMed |
description | 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 subjects and physical strain, thus making it difficult to devise models capable of simultaneously presenting reliable performance across users. Domain adaptation consists of a set of strategies that aim at allowing for improving machine learning systems performance on unseen data at training time. Such methods, however, might rely on assumptions over the considered data distributions, which typically do not hold for applications of EEG data. Motivated by this observation, in this work we propose a strategy to estimate two types of discrepancies between multiple data distributions, namely marginal and conditional shifts, observed on data collected from different subjects. Besides shedding light on the assumptions that hold for a particular dataset, the estimates of statistical shifts obtained with the proposed approach can be used for investigating other aspects of a machine learning pipeline, such as quantitatively assessing the effectiveness of domain adaptation strategies. In particular, we consider EEG data collected from individuals performing mental tasks while running on a treadmill and pedaling on a stationary bike and explore the effects of different normalization strategies commonly used to mitigate cross-subject variability. We show the effects that different normalization schemes have on statistical shifts and their relationship with the accuracy of mental workload prediction as assessed on unseen participants at training time. |
format | Online Article Text |
id | pubmed-9576998 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-95769982022-10-19 Estimating distribution shifts for predicting cross-subject generalization in electroencephalography-based mental workload assessment Albuquerque, Isabela Monteiro, João Rosanne, Olivier Falk, Tiago H. Front Artif Intell Artificial Intelligence 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 subjects and physical strain, thus making it difficult to devise models capable of simultaneously presenting reliable performance across users. Domain adaptation consists of a set of strategies that aim at allowing for improving machine learning systems performance on unseen data at training time. Such methods, however, might rely on assumptions over the considered data distributions, which typically do not hold for applications of EEG data. Motivated by this observation, in this work we propose a strategy to estimate two types of discrepancies between multiple data distributions, namely marginal and conditional shifts, observed on data collected from different subjects. Besides shedding light on the assumptions that hold for a particular dataset, the estimates of statistical shifts obtained with the proposed approach can be used for investigating other aspects of a machine learning pipeline, such as quantitatively assessing the effectiveness of domain adaptation strategies. In particular, we consider EEG data collected from individuals performing mental tasks while running on a treadmill and pedaling on a stationary bike and explore the effects of different normalization strategies commonly used to mitigate cross-subject variability. We show the effects that different normalization schemes have on statistical shifts and their relationship with the accuracy of mental workload prediction as assessed on unseen participants at training time. Frontiers Media S.A. 2022-10-04 /pmc/articles/PMC9576998/ /pubmed/36267659 http://dx.doi.org/10.3389/frai.2022.992732 Text en Copyright © 2022 Albuquerque, Monteiro, Rosanne and Falk. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Artificial Intelligence Albuquerque, Isabela Monteiro, João Rosanne, Olivier Falk, Tiago H. Estimating distribution shifts for predicting cross-subject generalization in electroencephalography-based mental workload assessment |
title | Estimating distribution shifts for predicting cross-subject generalization in electroencephalography-based mental workload assessment |
title_full | Estimating distribution shifts for predicting cross-subject generalization in electroencephalography-based mental workload assessment |
title_fullStr | Estimating distribution shifts for predicting cross-subject generalization in electroencephalography-based mental workload assessment |
title_full_unstemmed | Estimating distribution shifts for predicting cross-subject generalization in electroencephalography-based mental workload assessment |
title_short | Estimating distribution shifts for predicting cross-subject generalization in electroencephalography-based mental workload assessment |
title_sort | estimating distribution shifts for predicting cross-subject generalization in electroencephalography-based mental workload assessment |
topic | Artificial Intelligence |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9576998/ https://www.ncbi.nlm.nih.gov/pubmed/36267659 http://dx.doi.org/10.3389/frai.2022.992732 |
work_keys_str_mv | AT albuquerqueisabela estimatingdistributionshiftsforpredictingcrosssubjectgeneralizationinelectroencephalographybasedmentalworkloadassessment AT monteirojoao estimatingdistributionshiftsforpredictingcrosssubjectgeneralizationinelectroencephalographybasedmentalworkloadassessment AT rosanneolivier estimatingdistributionshiftsforpredictingcrosssubjectgeneralizationinelectroencephalographybasedmentalworkloadassessment AT falktiagoh estimatingdistributionshiftsforpredictingcrosssubjectgeneralizationinelectroencephalographybasedmentalworkloadassessment |