Recognition of the Mental Workloads of Pilots in the Cockpit Using EEG Signals

Hernàndez-Sabaté, Aura; Vidalón, José Elías Yauri; Folch, Pau; Piera, Miquel Àngel; Gil, Débora

doi:10.3390/app12052298

Cited by 25 publications

(24 citation statements)

References 39 publications

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“…Other works have addressed the shortcomings of EEG-based mental workload estimation from the task demand perspective (Ke et al, 2014 ). A convolutional neural network to classify EEG features across different task load conditions in a continuous performance task test was created in Hernández-Sabaté et al ( 2022 ). The goal was to partly measures working memory and working memory capacity, as an indicator of mental workload.…”

Section: Measuring Mental Workloadmentioning

confidence: 99%

Human Mental Workload: A Survey and a Novel Inclusive Definition

et al. 2022

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Human mental workload is arguably the most invoked multidimensional construct in Human Factors and Ergonomics, getting momentum also in Neuroscience and Neuroergonomics. Uncertainties exist in its characterization, motivating the design and development of computational models, thus recently and actively receiving support from the discipline of Computer Science. However, its role in human performance prediction is assured. This work is aimed at providing a synthesis of the current state of the art in human mental workload assessment through considerations, definitions, measurement techniques as well as applications, Findings suggest that, despite an increasing number of associated research works, a single, reliable and generally applicable framework for mental workload research does not yet appear fully established. One reason for this gap is the existence of a wide swath of operational definitions, built upon different theoretical assumptions which are rarely examined collectively. A second reason is that the three main classes of measures, which are self-report, task performance, and physiological indices, have been used in isolation or in pairs, but more rarely in conjunction all together. Multiple definitions complement each another and we propose a novel inclusive definition of mental workload to support the next generation of empirical-based research. Similarly, by comprehensively employing physiological, task-performance, and self-report measures, more robust assessments of mental workload can be achieved.

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Section: Measuring Mental Workloadmentioning

confidence: 99%

Human Mental Workload: A Survey and a Novel Inclusive Definition

et al. 2022

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“…To train the machine learning model, participant physiological signals were collected for three stressor levels during either a spaceflight emergency fire procedure on a VR International Space Station (VR-ISS) [46,47] or on a wellvalidated and less-complex N-back mental workload task [48]. Several previous studies have detected stress induced by Nback task via machine learning methods, both alone [48,49] and with another job-specific task [50]. Therefore, comparing a job-specific VR-ISS task to the N-back using the same personalized approach is a way to assess the system's reliability can work for multiple stress detection tasks.…”

Section: E Approachmentioning

confidence: 99%

Real-Time Personalized Physiologically Based Stress Detection for Hazardous Operations

et al. 2023

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When training for hazardous operations, real-time stress detection is an asset for optimizing task performance and reducing stress. Stress detection systems train a machine-learning model with physiological signals to classify stress levels of unseen data. Unfortunately, individual differences and the time-series nature of physiological signals limit the effectiveness of generalized models and hinder both posthoc stress detection and real-time monitoring. This study evaluated a personalized stress detection system that selects a personalized subset of features for model training. The system was evaluated post-hoc for realtime deployment. Further, traditional classifiers were assessed for error caused by indirect approximations against a benchmark, optimal probability classifier (Approximate Bayes; ABayes). Healthy participants completed a task with three levels of stressors (low, medium, high), either a complex task in virtual reality (responding to spaceflight emergency fires, n=27) or a simple laboratory-based task (N-back, n=14). Heart rate, blood pressure, electrodermal activity, and respiration were assessed. Personalized features and different interval window sizes were compared. Classification performance was compared for ABayes, support vector machine, decision tree, and random forest. The results demonstrate that a personalized model with time series intervals can classify three stress levels with high accuracy better than a generalized model. However, crossvalidation and holdout performance varied for traditional classifiers vs. ABayes, suggesting error from indirect approximations. The selected features changed with windows size and differed between tasks, but blood pressure contributed to the most prominent features. The capability to account for individual difference is a prominent advantage of personalized models, and will likely have a growing presence in future detection systems.

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“…To overcome the challenges, neural networks, in recent decades, have revolutionized computer vision systems to detect the weather condition using images as an input. Indeed, Convolutional Neural Networks (CNN) have been deployed in various fields such as ship detection [8][9][10][11][12][13], object tracking in endoscopic vision [14,15], nuclear plant inspection [16][17][18], transport systems [19,20], and other complex engineering tasks [21,22]. Yet, there is still a lot of ground to cover.…”

Section: Introductionmentioning

confidence: 99%

Weather Classification by Utilizing Synthetic Data

Minhas

Khanam

Ehsan

et al. 2022

Sensors

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Weather prediction from real-world images can be termed a complex task when targeting classification using neural networks. Moreover, the number of images throughout the available datasets can contain a huge amount of variance when comparing locations with the weather those images are representing. In this article, the capabilities of a custom built driver simulator are explored specifically to simulate a wide range of weather conditions. Moreover, the performance of a new synthetic dataset generated by the above simulator is also assessed. The results indicate that the use of synthetic datasets in conjunction with real-world datasets can increase the training efficiency of the CNNs by as much as 74%. The article paves a way forward to tackle the persistent problem of bias in vision-based datasets.

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Recognition of the Mental Workloads of Pilots in the Cockpit Using EEG Signals

Cited by 25 publications

References 39 publications

Human Mental Workload: A Survey and a Novel Inclusive Definition

Human Mental Workload: A Survey and a Novel Inclusive Definition

Real-Time Personalized Physiologically Based Stress Detection for Hazardous Operations

Weather Classification by Utilizing Synthetic Data

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