Using machine learning methods and EEG to discriminate aircraft pilot cognitive workload during flight

Gorji, Hamed Taheri; Wilson, Nicholas; VanBree, Jessica; Hoffmann, Bradley; Petros, Thomas V.; Tavakolian, Kouhyar

doi:10.1038/s41598-023-29647-0

Cited by 38 publications

(14 citation statements)

References 68 publications

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“…The only study that demonstrates the impact of COVID-19 on this professional group relates to the risk of SARS-CoV-2 virus infection in small aircraft, which can occur during flights between pilots [ 37 ]. Therefore, in our opinion, the use of quantitative electroencephalography as a method to diagnose the impact of the SARS-CoV-2 virus on the central nervous system of professional pilots represents an innovative approach, as it is the first study conducted on this professional group in which cognitive function disorders have appeared after recovering from the coronavirus [ 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 ].…”

Section: Discussionmentioning

confidence: 99%

The Use of Quantitative Electroencephalography (QEEG) to Assess Post-COVID-19 Concentration Disorders in Professional Pilots: An Initial Concept

Kopańska,

Rydzik,

Błajda

et al. 2023

Brain Sciences

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Announced by WHO in 2020, the global COVID-19 pandemic caused by SARS-CoV-2 has affected many people, leading to serious health consequences. These consequences are observed in the daily lives of infected patients as various dysfunctions and limitations. More and more people are suffering post-COVID-19 complications that interfere with or completely prevent them from working or even functioning independently on a daily basis. The aim of our study was to demonstrate that innovative quantitative electroencephalography (QEEG) can be used to assess cognitive function disorders reported after the COVID-19 pandemic. It is worth noting that no similar study has been conducted to date in a group of pilots. The QEEG method we used is currently one of the basic neurological examinations, enabling easy observation of post-COVID-19 changes in the nervous system. With the innovativeness of this technique, our study shows that the use of quantitative electroencephalography can be a precursor in identifying complications associated with cognitive function disorders after COVID-19. Our study was conducted on twelve 26-year-old pilots. All participants had attended the same flight academy and had contracted SARS-CoV-2 infection. The pilots began to suspect COVID-19 infection when they developed typical symptoms such as loss of smell and taste, respiratory problems, and rapid fatigue. Quantitative electroencephalography (QEEG), which is one of the most innovative forms of diagnostics, was used to diagnose the patients. Comparison of the results between the study and control groups showed significantly higher values of all measurements of alpha, theta, and beta2 waves in the study group. In the case of the sensorimotor rhythm (SMR), the measurement results were significantly higher in the control group compared to the study group. Our study, conducted on pilots who had recovered from COVID-19, showed changes in the amplitudes of brain waves associated with relaxation and concentration. The results confirmed the issues reported by pilots as evidenced by the increased amplitudes of alfa, theta, and beta2 waves. It should be emphasized that the modern diagnostic method (QEEG) presented here has significant importance in the medical diagnosis of various symptoms and observation of treatment effects in individuals who have contracted the SARS-CoV-2 virus. The present study demonstrated an innovative approach to the diagnosis of neurological complications after COVID-19.

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Section: Discussionmentioning

confidence: 99%

The Use of Quantitative Electroencephalography (QEEG) to Assess Post-COVID-19 Concentration Disorders in Professional Pilots: An Initial Concept

Kopańska,

Rydzik,

Błajda

et al. 2023

Brain Sciences

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“…Pupil diameter change has previously been associated with Duty Cycle and Aggressiveness derived from stick deflections in VR flight tasks [23]. Recently, EEG data have been implemented in an offline classification of workload of pilots during real flight [36]. Additionally, Kakkos et al [37] have shown that EEG data can be used to classify workload of novice users in low-fidelity VR simulators using machine learning methods.…”

Section: Discussionmentioning

confidence: 99%

Comparing Presence, Workload, and Performance in Desktop and Virtual Reality Flight Simulations

Weelden

Wiltshire

Alimardani

et al. 2022

Proceedings of the Human Factors and Ergonomics Society Annual

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Virtual reality (VR) offers a training environment that promotes increased learning and performance. However, to what extent VR flight simulations offer increased performance compared to less-immersive simulators is not clear, and neither are their underlying cognitive aspects. In a within-subject experiment, we compared fight performance and subjective measures of workload, presence, and engagement in two flight training environments (Desktop and VR) on two flight tasks of equal difficulty (changing speed and performing turns). No differences were observed in the flight performance between these conditions. However, participants reported higher presence and engagement in the VR training environment. Additionally, we found a correlation between subjective workload and flight performance in the Desktop condition, but not in VR. We conclude that VR is promising for basic flight training tasks and encourage future work to explore this further by investigating neurophysiological indices of workload and engagement and establishing relationships between presence, workload, and flight performance.

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“…Experiments demonstrated that the proposed method could achieve significant performance. In the field of aviation, in [ 146 ], they proposed using EEG to discriminate aircraft pilot cognitive workload during flight, which achieved an accuracy of 91.67% in classification tasks. Furthermore, in the maritime field, an approach was proposed for assessing mental fatigue based on EEG frequency bands [ 147 ].…”

Section: The Pipeline Of Eeg Signal Analysismentioning

confidence: 99%

Electroencephalography Signal Processing: A Comprehensive Review and Analysis of Methods and Techniques

Chaddad

Kateb

et al. 2023

Sensors

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The electroencephalography (EEG) signal is a noninvasive and complex signal that has numerous applications in biomedical fields, including sleep and the brain–computer interface. Given its complexity, researchers have proposed several advanced preprocessing and feature extraction methods to analyze EEG signals. In this study, we analyze a comprehensive review of numerous articles related to EEG signal processing. We searched the major scientific and engineering databases and summarized the results of our findings. Our survey encompassed the entire process of EEG signal processing, from acquisition and pretreatment (denoising) to feature extraction, classification, and application. We present a detailed discussion and comparison of various methods and techniques used for EEG signal processing. Additionally, we identify the current limitations of these techniques and analyze their future development trends. We conclude by offering some suggestions for future research in the field of EEG signal processing.

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Using machine learning methods and EEG to discriminate aircraft pilot cognitive workload during flight

Cited by 38 publications

References 68 publications

The Use of Quantitative Electroencephalography (QEEG) to Assess Post-COVID-19 Concentration Disorders in Professional Pilots: An Initial Concept

The Use of Quantitative Electroencephalography (QEEG) to Assess Post-COVID-19 Concentration Disorders in Professional Pilots: An Initial Concept

Comparing Presence, Workload, and Performance in Desktop and Virtual Reality Flight Simulations

Electroencephalography Signal Processing: A Comprehensive Review and Analysis of Methods and Techniques

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