ArticlePloS one2015
Real-time state estimation in a flight simulator using fNIRS.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
61 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Detecting and Predicting Pilot Mental Workload Using Heart Rate Variability: A Systematic Review.Sensors (Basel, Switzerland) · 2024Pooled it
- Trial
- Consumer-Grade Wearable Sensors for Classifying Pilot Workload and Stress During Real Flight Training: A Leave-One-Subject-Out Validation Study.Sensors (Basel, Switzerland) · 2026Article
- Sensory processing sensitivity predicts pilot decision time via indecisiveness: evidence from flight simulation and fNIRS.BMC psychology · 2026Article
- Cognitive state monitoring for neuroadaptive information visualization.Frontiers in human neuroscience · 2026Review
- From signal to construct evidence: multimodal consumer-wearable sensing in real general-aviation flight.Frontiers in neuroergonomics · 2026Article
- Exploring Graph Theory Mechanisms of Fluid Intelligence in the DLPFC: Insights From Resting-State fNIRS Across Various Time Windows.Brain and behavior · 2025Article
- Effects of the Interaction Between Time-on-Task and Task Load on Response Lapses.Behavioral sciences (Basel, Switzerland) · 2024Article
- Effects of age and flight experience on prefrontal cortex activity in airline pilots: An fNIRS study.Heliyon · 2024Article
- Working memory load recognition with deep learning time series classification.Biomedical optics express · 2024Article
- Hybrid statistical and machine learning modeling of cognitive neuroscience data.Journal of applied statistics · 2024Article
- A systematic narrative review of the involvement of executive functions in flying performance of pilots.Frontiers in neuroergonomics · 2024Review
- Enhancement of prefrontal functional connectivity under the influence of concurrent physical load during mental tasks.Frontiers in human neuroscience · 2024Article
- An Isolated CNN Architecture for Classification of Finger-Tapping Tasks Using Initial Dip Images: A Functional Near-Infrared Spectroscopy Study.Bioengineering (Basel, Switzerland) · 2023Article
- Metaheuristic Optimization-Based Feature Selection for Imagery and Arithmetic Tasks: An fNIRS Study.Sensors (Basel, Switzerland) · 2023Article
- Stress and Workload Assessment in Aviation-A Narrative Review.Sensors (Basel, Switzerland) · 2023Review
- Friend or foe: classifying collaborative interactions using fNIRS.Frontiers in neuroergonomics · 2023Article
- Interdisciplinary views of fNIRS: Current advancements, equity challenges, and an agenda for future needs of a diverse fNIRS research community.Frontiers in integrative neuroscience · 2023Review
- Identification of autism spectrum disorder based on functional near-infrared spectroscopy using adaptive spatiotemporal graph convolution network.Frontiers in neuroscience · 2023Article
- Evaluating mental workload during multitasking in simulated flight.Brain and behavior · 2022Article
1 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.