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A Non-parametric Approach to the Overall Estimate of Cognitive Load Using NIRS Time Series
We present a non-parametric approach to prediction of the n-back n ∈ {1, 2} task as a proxy measure of mental workload using Near Infrared Spectroscopy (NIRS) data. In particular, we focus on measuring the mental workload through hemodynamic responses in the brain induced by these tasks, thereby rea...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5290219/ https://www.ncbi.nlm.nih.gov/pubmed/28217088 http://dx.doi.org/10.3389/fnhum.2017.00015 |
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author | Keshmiri, Soheil Sumioka, Hidenobu Yamazaki, Ryuji Ishiguro, Hiroshi |
author_facet | Keshmiri, Soheil Sumioka, Hidenobu Yamazaki, Ryuji Ishiguro, Hiroshi |
author_sort | Keshmiri, Soheil |
collection | PubMed |
description | We present a non-parametric approach to prediction of the n-back n ∈ {1, 2} task as a proxy measure of mental workload using Near Infrared Spectroscopy (NIRS) data. In particular, we focus on measuring the mental workload through hemodynamic responses in the brain induced by these tasks, thereby realizing the potential that they can offer for their detection in real world scenarios (e.g., difficulty of a conversation). Our approach takes advantage of intrinsic linearity that is inherent in the components of the NIRS time series to adopt a one-step regression strategy. We demonstrate the correctness of our approach through its mathematical analysis. Furthermore, we study the performance of our model in an inter-subject setting in contrast with state-of-the-art techniques in the literature to show a significant improvement on prediction of these tasks (82.50 and 86.40% for female and male participants, respectively). Moreover, our empirical analysis suggest a gender difference effect on the performance of the classifiers (with male data exhibiting a higher non-linearity) along with the left-lateralized activation in both genders with higher specificity in females. |
format | Online Article Text |
id | pubmed-5290219 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-52902192017-02-17 A Non-parametric Approach to the Overall Estimate of Cognitive Load Using NIRS Time Series Keshmiri, Soheil Sumioka, Hidenobu Yamazaki, Ryuji Ishiguro, Hiroshi Front Hum Neurosci Neuroscience We present a non-parametric approach to prediction of the n-back n ∈ {1, 2} task as a proxy measure of mental workload using Near Infrared Spectroscopy (NIRS) data. In particular, we focus on measuring the mental workload through hemodynamic responses in the brain induced by these tasks, thereby realizing the potential that they can offer for their detection in real world scenarios (e.g., difficulty of a conversation). Our approach takes advantage of intrinsic linearity that is inherent in the components of the NIRS time series to adopt a one-step regression strategy. We demonstrate the correctness of our approach through its mathematical analysis. Furthermore, we study the performance of our model in an inter-subject setting in contrast with state-of-the-art techniques in the literature to show a significant improvement on prediction of these tasks (82.50 and 86.40% for female and male participants, respectively). Moreover, our empirical analysis suggest a gender difference effect on the performance of the classifiers (with male data exhibiting a higher non-linearity) along with the left-lateralized activation in both genders with higher specificity in females. Frontiers Media S.A. 2017-02-03 /pmc/articles/PMC5290219/ /pubmed/28217088 http://dx.doi.org/10.3389/fnhum.2017.00015 Text en Copyright © 2017 Keshmiri, Sumioka, Yamazaki and Ishiguro. http://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) or licensor 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 | Neuroscience Keshmiri, Soheil Sumioka, Hidenobu Yamazaki, Ryuji Ishiguro, Hiroshi A Non-parametric Approach to the Overall Estimate of Cognitive Load Using NIRS Time Series |
title | A Non-parametric Approach to the Overall Estimate of Cognitive Load Using NIRS Time Series |
title_full | A Non-parametric Approach to the Overall Estimate of Cognitive Load Using NIRS Time Series |
title_fullStr | A Non-parametric Approach to the Overall Estimate of Cognitive Load Using NIRS Time Series |
title_full_unstemmed | A Non-parametric Approach to the Overall Estimate of Cognitive Load Using NIRS Time Series |
title_short | A Non-parametric Approach to the Overall Estimate of Cognitive Load Using NIRS Time Series |
title_sort | non-parametric approach to the overall estimate of cognitive load using nirs time series |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5290219/ https://www.ncbi.nlm.nih.gov/pubmed/28217088 http://dx.doi.org/10.3389/fnhum.2017.00015 |
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