Monitoring pilot trainees’ cognitive control under a simulator-based training process with EEG microstate analysis

Sibi, S., Baiters, S., Mok, B., Steiner, M. & Ju, W. Assessing driver cortical activity under varying levels of automation with functional near infrared spectroscopy. In 2017 IEEE Intelligent Vehicles Symposium (IV), 1509–1516 (IEEE, 2017).Causse, M., Chua, Z. K. & Rémy, F. Influences of age, mental workload, and flight experience on cognitive performance and prefrontal activity in private pilots: a fNIRS study. Scientific reports 9, 1–12 (2019).Article 

Google Scholar 
Borghini, G. et al. EEG-based cognitive control behaviour assessment: an ecological study with professional air traffic controllers. Scientific Reports 7, 1–16 (2017).Article 

Google Scholar 
Jaquess, K. J. et al. Changes in mental workload and motor performance throughout multiple practice sessions under various levels of task difficulty. Neuroscience 393, 305–318 (2018).Article 
PubMed 

Google Scholar 
Balters, S., Gowda, N., Ordonez, F. & Paredes, P. E. Individualized stress detection using an unmodified car steering wheel. Scientific reports 11, 20646 (2021).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Klimesch, W. EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain research reviews 29, 169–195 (1999).Article 
PubMed 

Google Scholar 
Başar, E., Başar-Eroglu, C., Karakaş, S. & Schürmann, M. Gamma, alpha, delta, and theta oscillations govern cognitive processes. International journal of psychophysiology 39, 241–248 (2001).Article 
PubMed 

Google Scholar 
Stipacek, A., Grabner, R., Neuper, C., Fink, A. & Neubauer, A. Sensitivity of human EEG alpha band desynchronization to different working memory components and increasing levels of memory load. Neuroscience letters 353, 193–196 (2003).Article 
PubMed 

Google Scholar 
Gevins, A. & Smith, M. E. Neurophysiological measures of cognitive workload during human-computer interaction. Theoretical issues in ergonomics science 4, 113–131 (2003).Article 

Google Scholar 
Kamzanova, A. T., Kustubayeva, A. M. & Matthews, G. Use of EEG workload indices for diagnostic monitoring of vigilance decrement. Human factors 56, 1136–1149 (2014).Article 
PubMed 

Google Scholar 
Slobounov, S., Fukada, K., Simon, R., Rearick, M. & Ray, W. Neurophysiological and behavioral indices of time pressure effects on visuomotor task performance. Cognitive Brain Research 9, 287–298 (2000).Article 
PubMed 

Google Scholar 
Fairclough, S. H., Venables, L. & Tattersall, A. The influence of task demand and learning on the psychophysiological response. International Journal of Psychophysiology 56, 171–184 (2005).Article 
PubMed 

Google Scholar 
Roux, F. & Uhlhaas, P. J. Working memory and neural oscillations: alpha-gamma versus theta-gamma codes for distinct WM information?. Trends in cognitive sciences 18, 16–25 (2014).Article 
PubMed 

Google Scholar 
Raghavachari, S. et al. Gating of human theta oscillations by a working memory task. Journal of Neuroscience 21, 3175–3183 (2001).Article 
PubMed 

Google Scholar 
Tesche, C. & Karhu, J. Theta oscillations index human hippocampal activation during a working memory task. Proceedings of the National Academy of Sciences 97, 919–924 (2000).Article 
ADS 

Google Scholar 
Jensen, O. & Lisman, J. E. An oscillatory short-term memory buffer model can account for data on the sternberg task. Journal of Neuroscience 18, 10688–10699 (1998).Article 
PubMed 

Google Scholar 
Cavanagh, J. F. & Frank, M. J. Frontal theta as a mechanism for cognitive control. Trends in cognitive sciences 18, 414–421 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Fiebelkorn, I. C. & Kastner, S. A rhythmic theory of attention. Trends in cognitive sciences 23, 87–101 (2019).Article 
PubMed 

Google Scholar 
Herweg, N. A., Solomon, E. A. & Kahana, M. J. Theta oscillations in human memory. Trends in cognitive sciences 24, 208–227 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Staudigl, T. & Hanslmayr, S. Theta oscillations at encoding mediate the context-dependent nature of human episodic memory. Current biology 23, 1101–1106 (2013).Article 
PubMed 

Google Scholar 
Guderian, S. & Düzel, E. Induced theta oscillations mediate large-scale synchrony with mediotemporal areas during recollection in humans. Hippocampus 15, 901–912 (2005).Article 
PubMed 

Google Scholar 
Addante, R. J., Watrous, A. J., Yonelinas, A. P., Ekstrom, A. D. & Ranganath, C. Prestimulus theta activity predicts correct source memory retrieval. Proceedings of the National Academy of Sciences 108, 10702–10707 (2011).Article 
ADS 

Google Scholar 
Nyhus, E. & Curran, T. Functional role of gamma and theta oscillations in episodic memory. Neuroscience & Biobehavioral Reviews 34, 1023–1035 (2010).Article 

Google Scholar 
Bosseler, A. et al. Theta brain rhythms index perceptual narrowing in infant speech perception. Frontiers in Psychology 4, 690 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
Veen, V. v. & Carter, C. S. Conflict and cognitive control in the brain. Current Directions in Psychological Science 15, 237–240 (2006).Eisma, J., Rawls, E., Long, S., Mach, R. & Lamm, C. Frontal midline theta differentiates separate cognitive control strategies while still generalizing the need for cognitive control. Scientific Reports 11, 1–14 (2021).Article 

Google Scholar 
Cavanagh, J. F., Cohen, M. X. & Allen, J. J. Prelude to and resolution of an error: EEG phase synchrony reveals cognitive control dynamics during action monitoring. Journal of Neuroscience 29, 98–105 (2009).Article 
PubMed 

Google Scholar 
Cohen, M. X., Ridderinkhof, K. R., Haupt, S., Elger, C. E. & Fell, J. Medial frontal cortex and response conflict: evidence from human intracranial EEG and medial frontal cortex lesion. Brain research 1238, 127–142 (2008).Article 
PubMed 

Google Scholar 
Koechlin, E., Ody, C. & Kouneiher, F. The architecture of cognitive control in the human prefrontal cortex. Science 302, 1181–1185 (2003).Article 
ADS 
PubMed 

Google Scholar 
Taylor, J. L., O’Hara, R., Mumenthaler, M. S., Rosen, A. C. & Yesavage, J. A. Cognitive ability, expertise, and age differences in following air-traffic control instructions. Psychology and aging 20, 117 (2005).Article 
PubMed 

Google Scholar 
Krall, J., Menzies, T. & Davies, M. Gale: Geometric active learning for search-based software engineering. IEEE Transactions on Software Engineering 41, 1001–1018 (2015).Article 

Google Scholar 
Taheri Gorji, H. et al. Using machine learning methods and EEG to discriminate aircraft pilot cognitive workload during flight. Scientific Reports 13, 2507 (2023).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Roberts, R. E., Anderson, E. J. & Husain, M. Expert cognitive control and individual differences associated with frontal and parietal white matter microstructure. Journal of Neuroscience 30, 17063–17067 (2010).Article 
PubMed 

Google Scholar 
Eriksen, B. A. & Eriksen, C. W. Effects of noise letters upon the identification of a target letter in a nonsearch task. Perception & psychophysics 16, 143–149 (1974).Article 

Google Scholar 
Nachev, P., Rees, G., Parton, A., Kennard, C. & Husain, M. Volition and conflict in human medial frontal cortex. Current Biology 15, 122–128 (2005).Article 
PubMed 

Google Scholar 
Rasmussen, J. Skills, rules, and knowledge; signals, signs, and symbols, and other distinctions in human performance models. IEEE transactions on systems, man, and cybernetics 257–266 (1983).Lopez, N., Previc, F. H., Fischer, J., Heitz, R. P. & Engle, R. W. Effects of sleep deprivation on cognitive performance by united states air force pilots. Journal of Applied Research in Memory and Cognition 1, 27–33 (2012).Article 

Google Scholar 
Krall, J., Menzies, T. & Davies, M. Learning mitigations for pilot issues when landing aircraft (via multiobjective optimization and multiagent simulations). IEEE Transactions on Human-Machine Systems 46, 221–230 (2016).Article 

Google Scholar 
Lehmann, D., Ozaki, H. & Pál, I. EEG alpha map series: brain micro-states by space-oriented adaptive segmentation. Electroencephalography and clinical neurophysiology 67, 271–288 (1987).Article 
PubMed 

Google Scholar 
Michel, C. M. & Koenig, T. EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: a review. Neuroimage 180, 577–593 (2018).Article 
PubMed 

Google Scholar 
Britz, J., Van De Ville, D. & Michel, C. M. Bold correlates of EEG topography reveal rapid resting-state network dynamics. Neuroimage 52, 1162–1170 (2010).Article 
PubMed 

Google Scholar 
Seitzman, B. A. et al. Cognitive manipulation of brain electric microstates. Neuroimage 146, 533–543 (2017).Article 
PubMed 

Google Scholar 
Xu, X., Yuan, H. & Lei, X. Activation and connectivity within the default mode network contribute independently to future-oriented thought. Scientific reports 6, 1–10 (2016).
Google Scholar 
Bréchet, L. et al. Capturing the spatiotemporal dynamics of self-generated, task-initiated thoughts with EEG and fMRI. Neuroimage 194, 82–92 (2019).Article 
PubMed 

Google Scholar 
Cohen, J. D. Cognitive control: Core constructs and current considerations. The Wiley handbook of cognitive control 1–28 (2017).Musslick, S. & Cohen, J. D. Rationalizing constraints on the capacity for cognitive control. Trends in Cognitive Sciences 25, 757–775 (2021).Article 
PubMed 

Google Scholar 
Petersen, S. E., Van Mier, H., Fiez, J. A. & Raichle, M. E. The effects of practice on the functional anatomy of task performance. Proceedings of the National Academy of Sciences 95, 853–860 (1998).Article 
ADS 

Google Scholar 
Borghini, G. et al. A new perspective for the training assessment: machine learning-based neurometric for augmented user’s evaluation. Frontiers in Neuroscience 11, 251123 (2017).Article 

Google Scholar 
Law, A. et al. An integrated physiological monitoring system for airborne and laboratory research. NRC Aerospace. Flight Research Laboratory; LTR-FRL-2017-0095 (2017).Delorme, A. & Makeig, S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of neuroscience methods 134, 9–21 (2004).Article 
PubMed 

Google Scholar 
Winkler, I., Haufe, S. & Tangermann, M. Automatic classification of artifactual ICA-components for artifact removal in EEG signals. Behavioral and brain functions 7, 1–15 (2011).Article 

Google Scholar 
Makeig, S., Bell, A., Jung, T.-P. & Sejnowski, T. J. Independent component analysis of electroencephalographic data. Advances in neural information processing systems 8 (1995).Gabard-Durnam, L. J., Mendez Leal, A. S., Wilkinson, C. L. & Levin, A. R. The harvard automated processing pipeline for electroencephalography (HAPPE): standardized processing software for developmental and high-artifact data. Frontiers in neuroscience 12, 97 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Nolan, H., Whelan, R. & Reilly, R. B. Faster: fully automated statistical thresholding for EEG artifact rejection. Journal of neuroscience methods 192, 152–162 (2010).Article 
PubMed 

Google Scholar 
García-Martínez, B., Martinez-Rodrigo, A., Alcaraz, R. & Fernández-Caballero, A. A review on nonlinear methods using electroencephalographic recordings for emotion recognition. IEEE Transactions on Affective Computing 12, 801–820 (2019).Article 

Google Scholar 
Agnoli, S., Zanon, M., Mastria, S., Avenanti, A. & Corazza, G. E. Predicting response originality through brain activity: An analysis of changes in EEG alpha power during the generation of alternative ideas. NeuroImage 207, 116385 (2020).Article 
PubMed 

Google Scholar 
Jia, W. & Zeng, Y. Eeg signals respond differently to idea generation, idea evolution and evaluation in a loosely controlled creativity experiment. Scientific Reports 11, 1–20 (2021).Article 

Google Scholar 
Pascual-Marqui, R. D., Michel, C. M. & Lehmann, D. Segmentation of brain electrical activity into microstates: model estimation and validation. IEEE Transactions on Biomedical Engineering 42, 658–665 (1995).Article 
PubMed 

Google Scholar 
Von Wegner, F. Partial autoinformation to characterize symbolic sequences. Frontiers in physiology 1382 (2018).Peng, C.-K., Havlin, S., Stanley, H. E. & Goldberger, A. L. Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos: an interdisciplinary journal of nonlinear science 5, 82–87 (1995).Custo, A. et al. Electroencephalographic resting-state networks: source localization of microstates. Brain connectivity 7, 671–682 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Grupe, D. W. & Nitschke, J. B. Uncertainty and anticipation in anxiety: an integrated neurobiological and psychological perspective. Nature Reviews Neuroscience 14, 488–501 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
Morriss, J., Gell, M. & van Reekum, C. M. The uncertain brain: A co-ordinate based meta-analysis of the neural signatures supporting uncertainty during different contexts. Neuroscience & Biobehavioral Reviews 96, 241–249 (2019).Article 

Google Scholar 
Cavanagh, J. F., Zambrano-Vazquez, L. & Allen, J. J. Theta lingua franca: A common mid-frontal substrate for action monitoring processes. Psychophysiology 49, 220–238 (2012).Article 
PubMed 

Google Scholar 
Riddle, J., Vogelsang, D. A., Hwang, K., Cellier, D. & D’Esposito, M. Distinct oscillatory dynamics underlie different components of hierarchical cognitive control. Journal of Neuroscience 40, 4945–4953 (2020).Article 
PubMed 

Google Scholar 
Darvishi-Bayazi, M.-J. et al. Beyond performance: the role of task demand, effort, and individual differences in ab initio pilots. Scientific Reports 13, 14035 (2023).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Ruiz-Segura, A. et al. Flight emotions unleashed: Navigating training phases and difficulty levels in simulated flying. Journal of Computer Assisted Learning (2024).Cooper, P. S. et al. Frontal theta predicts specific cognitive control-induced behavioural changes beyond general reaction time slowing. Neuroimage 189, 130–140 (2019).Article 
PubMed 

Google Scholar 
Sauseng, P., Griesmayr, B., Freunberger, R. & Klimesch, W. Control mechanisms in working memory: a possible function of EEG theta oscillations. Neuroscience & Biobehavioral Reviews 34, 1015–1022 (2010).Article 

Google Scholar 
Karakaş, S. A review of theta oscillation and its functional correlates. International Journal of Psychophysiology 157, 82–99 (2020).Article 
PubMed 

Google Scholar 
Botvinick, M. M., Braver, T. S., Barch, D. M., Carter, C. S. & Cohen, J. D. Conflict monitoring and cognitive control. Psychological review 108, 624 (2001).Article 
PubMed 

Google Scholar 
Verguts, T. & Notebaert, W. Adaptation by binding: A learning account of cognitive control. Trends in cognitive sciences 13, 252–257 (2009).Article 
PubMed 

Google Scholar 
Unsworth, N., Fukuda, K., Awh, E. & Vogel, E. K. Working memory delay activity predicts individual differences in cognitive abilities. Journal of Cognitive Neuroscience 27, 853–865 (2015).Article 
PubMed 

Google Scholar 
Amer, T., Campbell, K. L. & Hasher, L. Cognitive control as a double-edged sword. Trends in cognitive sciences 20, 905–915 (2016).Article 
PubMed 

Google Scholar