Grand Field Challenges for Cognitive Neuroergonomics in the Coming Decade

Gramann, Klaus; McKendrick, Ryan; Baldwin, Carryl L.; Roy, Raphaëlle N.; Jeunet, Camille; Mehta, Ranjana K.; Vecchiato, Giovanni

doi:10.3389/fnrgo.2021.643969

Cited by 33 publications

(29 citation statements)

References 43 publications

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“…We were successful in employing machine learning to capitalize on these differences in distinguishing between the two stages and also to provide some explainability around how those differences changed with environmental conditions. Neural activity has shown strong potential for detecting cognitive and physiological states in VR scenarios [ 14 ]; however, there are very few studies that investigate their use in developing adaptive VR training solutions for emergency responders. Our findings signal positively toward the development of VR-based learning platforms that can adapt to the learning state of individual trainees in emergence response (ER) skill development.…”

Section: Discussionmentioning

confidence: 99%

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Brain Activity-Based Metrics for Assessing Learning States in VR under Stress among Firefighters: An Explorative Machine Learning Approach in Neuroergonomics

et al. 2021

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The nature of firefighters` duties requires them to work for long periods under unfavorable conditions. To perform their jobs effectively, they are required to endure long hours of extensive, stressful training. Creating such training environments is very expensive and it is difficult to guarantee trainees’ safety. In this study, firefighters are trained in a virtual environment that includes virtual perturbations such as fires, alarms, and smoke. The objective of this paper is to use machine learning methods to discern encoding and retrieval states in firefighters during a visuospatial episodic memory task and explore which regions of the brain provide suitable signals to solve this classification problem. Our results show that the Random Forest algorithm could be used to distinguish between information encoding and retrieval using features extracted from fNIRS data. Our algorithm achieved an F-1 score of 0.844 and an accuracy of 79.10% if the training and testing data are obtained at similar environmental conditions. However, the algorithm’s performance dropped to an F-1 score of 0.723 and accuracy of 60.61% when evaluated on data collected under different environmental conditions than the training data. We also found that if the training and evaluation data were recorded under the same environmental conditions, the RPM, LDLPFC, RDLPFC were the most relevant brain regions under non-stressful, stressful, and a mix of stressful and non-stressful conditions, respectively.

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Section: Discussionmentioning

confidence: 99%

“…Brain activity and connectivity hold strong potential for monitoring cognitive and physiological states in VR scenarios [ 14 ]. Brain activity-based tutoring systems that monitor behavior to assess the cognitive states and workload of the trainee have shown to be very effective for adaptive training [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Brain Activity-Based Metrics for Assessing Learning States in VR under Stress among Firefighters: An Explorative Machine Learning Approach in Neuroergonomics

et al. 2021

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“…Recent technological advances in the field of highly portable neurophysiological sensors offer interesting perspectives to study brain functioning in the real world (Fairclough and Lotte, 2020;Gramann et al, 2021). The main motivation of this study was to benchmark two non-invasive and comfortable ultra-mobile EEG systems to study inattentional deafness-related brain activities in a motion flight simulator.…”

Section: Discussionmentioning

confidence: 99%

“…Neuroergonomics is a recent field of research that promotes the use of portable brain imaging to investigate complex cognitive processes that are difficult to observe and measure under laboratory settings (Parasuraman, 2003;Dehais et al, 2020a;Gramann et al, 2021). In this regard, recording systems have to fulfill certain requirements in terms of bulkiness, portability, sensitivity and specificity (Hettinger et al, 2003;Somon et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Benchmarking cEEGrid and solid gel-based electrodes to classify inattentional deafness in a flight simulator

Somon¹,

Giebeler²,

Darmet³

et al. 2021

Preprint

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Transfer from experiments in laboratory to real-life tasks is challenging due notably to the inability to reproduce the complexity of multitasking dynamic everyday life situations in a standardized lab condition, and to the bulkiness and invasiveness of recording systems preventing participants from moving freely and disturbing the environment. Here we used a motion flight simulator to induce inattentional deafness to auditory alarms, a cognitive difficulty arising in complex environments. In addition, we assessed the possibility of two low-density EEG systems (a solid gel-based electrode Enobio (Neuroelectrics, Barcelona, Spain) and a gel-based cEEGrid (TMSi, Oldenzaal, Netherlands)) to record and classify brain activity associated with inattentional deafness (misses vs. hits to odd sounds) with a small pool of expert participants. In addition to inducing inattentional deafness (missing auditory alarms) at much higher rates than with usual lab tasks (34.7% compared to the usual 5%), we observed typical inattentional deafness-related activity in the time domain, but also in the frequency and time-frequency domains with both systems. Finally, a classifier based on Riemannian Geometry principles allowed us to obtain more than 70% of single-trial classification accuracy for both mobile EEG, and up to 72.9% for the cEEGrid (TMSi, Oldenzaal, Netherlands). These results open promising avenue towards detecting cognitive failures in real-life situations, such as real flight.

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“…Due to the complexity of the underlying brain processes and the technological perspectives possibly impacting the market, driving is one of the most studied scenarios targeted by the field of neuroergonomics [14,15]. Several systems have been developed to decode brain activity anticipating salient actions during driving, such as braking [16,17] and steering [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Validation of a Novel Wearable Multistream Data Acquisition and Analysis System for Ergonomic Studies

Ascari¹,

Marchenkova

Bellotti³

et al. 2021

Sensors

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Nowadays, the growing interest in gathering physiological data and human behavior in everyday life scenarios is paralleled by an increase in wireless devices recording brain and body signals. However, the technical issues that characterize these solutions often limit the full brain-related assessments in real-life scenarios. Here we introduce the Biohub platform, a hardware/software (HW/SW) integrated wearable system for multistream synchronized acquisitions. This system consists of off-the-shelf hardware and state-of-art open-source software components, which are highly integrated into a high-tech low-cost solution, complete, yet easy to use outside conventional labs. It flexibly cooperates with several devices, regardless of the manufacturer, and overcomes the possibly limited resources of recording devices. The Biohub was validated through the characterization of the quality of (i) multistream synchronization, (ii) in-lab electroencephalographic (EEG) recordings compared with a medical-grade high-density device, and (iii) a Brain-Computer-Interface (BCI) in a real driving condition. Results show that this system can reliably acquire multiple data streams with high time accuracy and record standard quality EEG signals, becoming a valid device to be used for advanced ergonomics studies such as driving, telerehabilitation, and occupational safety.

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Grand Field Challenges for Cognitive Neuroergonomics in the Coming Decade

Cited by 33 publications

References 43 publications

Brain Activity-Based Metrics for Assessing Learning States in VR under Stress among Firefighters: An Explorative Machine Learning Approach in Neuroergonomics

Brain Activity-Based Metrics for Assessing Learning States in VR under Stress among Firefighters: An Explorative Machine Learning Approach in Neuroergonomics

Benchmarking cEEGrid and solid gel-based electrodes to classify inattentional deafness in a flight simulator

Validation of a Novel Wearable Multistream Data Acquisition and Analysis System for Ergonomic Studies

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