Real-time state estimation in a flight simulator using fNIRS

Gateau, Thibault, Durantin, Gautier, Lancelot, Francois, Scannella, Sebastien and Dehais, Frederic (2015) Real-time state estimation in a flight simulator using fNIRS. PLoS ONE, 10 3: 1-19. doi:10.1371/journal.pone.0121279

Author Gateau, Thibault
Durantin, Gautier
Lancelot, Francois
Scannella, Sebastien
Dehais, Frederic
Title Real-time state estimation in a flight simulator using fNIRS
Journal name PLoS ONE   Check publisher's open access policy
ISSN 1932-6203
Publication date 2015-03-27
Year available 2015
Sub-type Article (original research)
DOI 10.1371/journal.pone.0121279
Open Access Status DOI
Volume 10
Issue 3
Start page 1
End page 19
Total pages 19
Place of publication San Francisco, CA United States
Publisher Public Library of Science
Collection year 2015
Language eng
Formatted abstract
Working memory is a key executive function for flying an aircraft. This function is particularly critical when pilots have to recall series of air traffic control instructions. However, working memory limitations may jeopardize flight safety. Since the functional near-infrared spectroscopy (fNIRS) method seems promising for assessing working memory load, our objective is to implement an on-line fNIRS-based inference system that integrates two complementary estimators. The first estimator is a real-time state estimation MACD-based algorithm dedicated to identifying the pilot’s instantaneous mental state (not-on-task vs. on-task). It does not require a calibration process to perform its estimation. The second estimator is an on-line SVM-based classifier that is able to discriminate task difficulty (low working memory load vs. high working memory load). These two estimators were tested with 19 pilots who were placed in a realistic flight simulator and were asked to recall air traffic control instructions. We found that the estimated pilot’s mental state matched significantly better than chance with the pilot’s real state (62% global accuracy, 58% specificity, and 72% sensitivity). The second estimator, dedicated to assessing single trial working memory loads, led to 80% classification accuracy, 72% specificity, and 89% sensitivity. These two estimators establish reusable blocks for further fNIRS-based passive brain computer interface development.
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Non HERDC
School of Information Technology and Electrical Engineering Publications
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 8 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 12 times in Scopus Article | Citations
Google Scholar Search Google Scholar
Created: Thu, 07 Apr 2016, 15:54:00 EST by Anthony Yeates on behalf of School of Information Technol and Elec Engineering