Correlation between Situational Awareness and EEG signals

Kästle, Jan Luca; Anvari, Bani; Krol, Jakub; Würdemann, Helge A.

doi:10.1016/j.neucom.2020.12.026

Cited by 48 publications

(19 citation statements)

References 40 publications

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“…Generally, 93.33% of the precision was obtained [10]. Kastle et al [11] proposed an empirical approach to classify EEG signatures in situational awareness (SA) in various brain regions. The new data collection of 32 participants who have completed the psychology experiment building language (PEBL) SA test is obtained with the 32-channel dry-EEG headset.…”

Section: Related Workmentioning

confidence: 99%

Driving sleepiness detection using electrooculogram analysis and grey wolf optimizer

Jasim

Hassan

2022

IJECE

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<span lang="EN-US">In modern society, providing safe and collision-free travel is essential. Therefore, detecting the drowsiness state of the driver before its ability to drive is compromised. For this purpose, an automated hybrid sleepiness classification system that combines the artificial neural network and gray wolf optimizer is proposed to distinguish human Sleepiness and fatigue. The proposed system is tested on data collected from 15 drivers (male and female) in alert and sleep-deprived conditions where physiological signals are used as sleep markers. To evaluate the performance of the proposed algorithm, k-nearest neighbors (k-NN), support vector machines (SVM), and artificial neural networks (ANN) classifiers have been used. The results show that the proposed hybrid method provides 99.6% accuracy, while the SVM classifier provides 93.0% accuracy when the kernel is (RBF) and outlier (0.1). Furthermore, the k-NN classifier provides 96.7% accuracy, whereas the standalone ANN algorithm provides 97.7% accuracy.</span>

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Section: Related Workmentioning

confidence: 99%

Driving sleepiness detection using electrooculogram analysis and grey wolf optimizer

Jasim

Hassan

2022

IJECE

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“…By analyzing the EEG data obtained from different brain areas, it is concluded that there was a co-activity in visual and highorder perception regions during loss of situation awareness. Luca Kästle et al (2021) proposed a novel analytical methodology to correlate EEG signals to situation awareness. Participants completed the situation awareness test in Psychology Experiment Building Language (PEBL).…”

Section: Physiological Measurementsmentioning

confidence: 99%

Measurements of Mental Stress and Safety Awareness during Human Robot Collaboration -Review

Xie

Wang

et al. 2022

Proceedings of the Human Factors and Ergonomics Society Annual

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Human-robot collaboration is an emerging research area that has gained tremendous attention in both academia and industry. Yet, the feature that human and robot sharing the workplace has led to safety concerns. In particular, the psychological states of human teammates during human-robot collaboration remains unclear but is also of great importance to workplace safety. This manuscript briefly reviewed possible direct and indirect measures that can be used to evaluate workers’ mental stress and safety awareness during human robot collaboration. It was concluded that each measure reviewed in this paper has its validity and rationality, and a combination of different methods may provide a more comprehensive and accurate assessment.

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“…Regarding the relationship between time-frequency features and SA, there have been attempts to validate the association (Klaproth et al, 2020;Iqbal et al, 2021). Kästle et al (2021) identified the correlations between β and γ in parietal and temporal regions and SA levels during a control task. Notably, only a few studies (Saini et al, 2020) have adopted multiple EEG spatialfrequency features, but there is no consensus on which features are associated with SA due to different task conditions and application purposes, let alone the identification of pilots' SA levels based on EEG association metrics.…”

Section: Introductionmentioning

confidence: 99%

EEG-based analysis for pilots’ at-risk cognitive competency identification using RF-CNN algorithm

et al. 2023

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Cognitive competency is an essential complement to the existing ship pilot screening system that should be focused on. Situation awareness (SA), as the cognitive foundation of unsafe behaviors, is susceptible to influencing piloting performance. To address this issue, this paper develops an identification model based on random forest- convolutional neural network (RF-CNN) method for detecting at-risk cognitive competency (i.e., low SA level) using wearable EEG signal acquisition technology. In the poor visibility scene, the pilots’ SA levels were correlated with EEG frequency metrics in frontal (F) and central (C) regions, including α/β (p = 0.071 < 0.1 in F and p = 0.042 < 0.05 in C), θ/(α + θ) (p = 0.048 < 0.05 in F and p = 0.026 < 0.05 in C) and (α + θ)/β (p = 0.046 < 0.05 in F and p = 0.012 < 0.05 in C), and then a total of 12 correlation features were obtained based on a 5 s sliding time window. Using the RF algorithm developed by principal component analysis (PCA) for further feature combination, these salient combinations are used as input sets to obtain the CNN algorithm with optimal parameters for identification. The comparative results of the proposed RF-CNN (accuracy is 84.8%) against individual RF (accuracy is 78.1%) and CNN (accuracy is 81.6%) methods demonstrate that the RF-CNN with feature optimization provides the best identification of at-risk cognitive competency (accuracy increases 6.7%). Overall, the results of this paper provide key technical support for the development of an adaptive evaluation system of pilots’ cognitive competency based on intelligent technology, and lay the foundation and framework for monitoring the cognitive process and competency of ship piloting operation in China.

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Correlation between Situational Awareness and EEG signals

Cited by 48 publications

References 40 publications

Driving sleepiness detection using electrooculogram analysis and grey wolf optimizer

Driving sleepiness detection using electrooculogram analysis and grey wolf optimizer

Measurements of Mental Stress and Safety Awareness during Human Robot Collaboration -Review

EEG-based analysis for pilots’ at-risk cognitive competency identification using RF-CNN algorithm

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