Monitoring attentional state with fNIRS
The ability to distinguish between high and low levels of task engagement in the real world is important for detecting and preventing performance decrements during safety-critical operational tasks. We therefore investigated whether functional Near Infrared Spectroscopy (fNIRS), a portable brain neu...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2013-12-01
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| Series: | Frontiers in Human Neuroscience |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fnhum.2013.00861/full |
| _version_ | 1819225198312816640 |
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| author | Angela R. Harrivel Angela R. Harrivel Daniel H. Weissman Douglas C. Noll Scott J. Peltier |
| author_facet | Angela R. Harrivel Angela R. Harrivel Daniel H. Weissman Douglas C. Noll Scott J. Peltier |
| author_sort | Angela R. Harrivel |
| collection | DOAJ |
| description | The ability to distinguish between high and low levels of task engagement in the real world is important for detecting and preventing performance decrements during safety-critical operational tasks. We therefore investigated whether functional Near Infrared Spectroscopy (fNIRS), a portable brain neuroimaging technique, can be used to distinguish between high and low levels of task engagement during the performance of a selective attention task. A group of participants performed the multi-source interference task (MSIT) while we recorded brain activity with fNIRS from two brain regions. One was a key region of the task-positive network, which is associated with relatively high levels of task engagement. The second was a key region of the task-negative network, which is associated with relatively low levels of task engagement (e.g., resting and not performing a task). Using activity in these regions as inputs to a multivariate pattern classifier, we were able to predict above chance levels whether participants were engaged in performing the MSIT or resting. We were also able to replicate prior findings from functional magnetic resonance imaging (fMRI) indicating that activity in task-positive and task-negative regions is negatively correlated during task performance. Finally, data from a companion fMRI study verified our assumptions about the sources of brain activity in the fNIRS experiment and established an upper bound on classification accuracy in our task. Together, our findings suggest that fNIRS could prove quite useful for monitoring cognitive state in real-world settings. |
| first_indexed | 2024-12-23T10:05:47Z |
| format | Article |
| id | doaj.art-e4b3040dacc04b8794040e229afd5f63 |
| institution | Directory Open Access Journal |
| issn | 1662-5161 |
| language | English |
| last_indexed | 2024-12-23T10:05:47Z |
| publishDate | 2013-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Human Neuroscience |
| spelling | doaj.art-e4b3040dacc04b8794040e229afd5f632022-12-21T17:51:05ZengFrontiers Media S.A.Frontiers in Human Neuroscience1662-51612013-12-01710.3389/fnhum.2013.0086170628Monitoring attentional state with fNIRSAngela R. Harrivel0Angela R. Harrivel1Daniel H. Weissman2Douglas C. Noll3Scott J. Peltier4NASA Glenn Research CenterUniversity of MichiganUniversity of MichiganUniversity of MichiganUniversity of MichiganThe ability to distinguish between high and low levels of task engagement in the real world is important for detecting and preventing performance decrements during safety-critical operational tasks. We therefore investigated whether functional Near Infrared Spectroscopy (fNIRS), a portable brain neuroimaging technique, can be used to distinguish between high and low levels of task engagement during the performance of a selective attention task. A group of participants performed the multi-source interference task (MSIT) while we recorded brain activity with fNIRS from two brain regions. One was a key region of the task-positive network, which is associated with relatively high levels of task engagement. The second was a key region of the task-negative network, which is associated with relatively low levels of task engagement (e.g., resting and not performing a task). Using activity in these regions as inputs to a multivariate pattern classifier, we were able to predict above chance levels whether participants were engaged in performing the MSIT or resting. We were also able to replicate prior findings from functional magnetic resonance imaging (fMRI) indicating that activity in task-positive and task-negative regions is negatively correlated during task performance. Finally, data from a companion fMRI study verified our assumptions about the sources of brain activity in the fNIRS experiment and established an upper bound on classification accuracy in our task. Together, our findings suggest that fNIRS could prove quite useful for monitoring cognitive state in real-world settings.http://journal.frontiersin.org/Journal/10.3389/fnhum.2013.00861/fullAttentionClassificationDefault Mode Networkhuman performanceNear infra-red spectroscopy |
| spellingShingle | Angela R. Harrivel Angela R. Harrivel Daniel H. Weissman Douglas C. Noll Scott J. Peltier Monitoring attentional state with fNIRS Frontiers in Human Neuroscience Attention Classification Default Mode Network human performance Near infra-red spectroscopy |
| title | Monitoring attentional state with fNIRS |
| title_full | Monitoring attentional state with fNIRS |
| title_fullStr | Monitoring attentional state with fNIRS |
| title_full_unstemmed | Monitoring attentional state with fNIRS |
| title_short | Monitoring attentional state with fNIRS |
| title_sort | monitoring attentional state with fnirs |
| topic | Attention Classification Default Mode Network human performance Near infra-red spectroscopy |
| url | http://journal.frontiersin.org/Journal/10.3389/fnhum.2013.00861/full |
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