Using near infrared spectroscopy and heart rate variability to detect mental overload

Durantin, G., Gagnon, J.-F., Tremblay, S. and Dehais, F. (2014) Using near infrared spectroscopy and heart rate variability to detect mental overload. Behavioural Brain Research, 259 16-23. doi:10.1016/j.bbr.2013.10.042


Author Durantin, G.
Gagnon, J.-F.
Tremblay, S.
Dehais, F.
Title Using near infrared spectroscopy and heart rate variability to detect mental overload
Journal name Behavioural Brain Research   Check publisher's open access policy
ISSN 0166-4328
1872-7549
Publication date 2014-02-01
Sub-type Article (original research)
DOI 10.1016/j.bbr.2013.10.042
Open Access Status Not Open Access
Volume 259
Start page 16
End page 23
Total pages 8
Place of publication Amsterdam, Netherlands
Publisher Elsevier
Language eng
Abstract Mental workload is a key factor influencing the occurrence of human error, especially during piloting and remotely operated vehicle (ROV) operations, where safety depends on the ability of pilots to act appropriately. In particular, excessively high or low mental workload can lead operators to neglect critical information. The objective of the present study is to investigate the potential of functional near infrared spectroscopy (fNIRS) - a non-invasive method of measuring prefrontal cortex activity - in combination with measurements of heart rate variability (HRV), to predict mental workload during a simulated piloting task, with particular regard to task engagement and disengagement. Twelve volunteers performed a computer-based piloting task in which they were asked to follow a dynamic target with their aircraft, a task designed to replicate key cognitive demands associated with real life ROV operating tasks. In order to cover a wide range of mental workload levels, task difficulty was manipulated in terms of processing load and difficulty of control - two critical sources of workload associated with piloting and remotely operating a vehicle. Results show that both fNIRS and HRV are sensitive to different levels of mental workload; notably, lower prefrontal activation as well as a lower LF/HF ratio at the highest level of difficulty, suggest that these measures are suitable for mental overload detection. Moreover, these latter measurements point toward the existence of a quadratic model of mental workload.
Keyword NIRS
HRV
Mental overload
Human factors
Remotely operated vehicle
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Unknown

Document type: Journal Article
Sub-type: Article (original research)
Collection: School of Information Technology and Electrical Engineering Publications
 
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Created: Thu, 07 Apr 2016, 15:54:57 EST by Anthony Yeates on behalf of School of Information Technol and Elec Engineering