Mobile EEG-based situation awareness recognition for air traffic controllers

Yeo, Lee Guan; Sun, Haoqi; Liu, Yisi; Trapsilawati, Fitri; Sourina, Olga; Chen, Chun‐Hsien; Müeller-Wittig, Wolfgang; Ang, Wei Tech

doi:10.1109/smc.2017.8123090

Cited by 27 publications

(22 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other physiological measures used in the identified papers included EEG, EDA, and RE measures ( Table 5). Three studies used EEG (Catherwood et al, 2014;Kaur et al, 2020;Yeo et al, 2017). One studied the relationship between RE and SA (Sun et al, 2017), and the remaining used EDA (Wang et al, 2012).…”

Section: Other Physiological Measuresmentioning

confidence: 99%

“…They also found a positive correlation between SA and PRAFA EEG metrics (correlation = .56, p = .01) in the limbic region that has direct access to perceptual information. Yeo et al (2017) used EEG metrics, such as alpha band, theta band, beta band, entropy, and band power ratio to predict the SA of air traffic controllers using three machine learning methods. Among them, the support vector regression, using EEG signals, had the lowest prediction error of 1.5, which outperformed the results of the linear regression and extreme learning machine for regression algorithms they used in predicting SA.…”

Section: Other Physiological Measuresmentioning

confidence: 99%

See 1 more Smart Citation

Physiological Measurements of Situation Awareness: A Systematic Review

et al. 2020

View full text Add to dashboard Cite

Objective The goal of this review is to investigate the relationship between indirect physiological measurements and direct measures of situation awareness (SA). Background Assessments of SA are often performed using techniques designed specifically to directly measure SA, such as SA global assessment technique (SAGAT), situation present assessment method (SPAM), and/or SA rating technique (SART). However, research suggests that physiological sensing methods may also be capable of inferring SA. Method Seven databases were searched. Eligibility criteria included human–subject experiments that used at least one direct SA assessment technique as well as at least one physiological measurement. Information extracted from each article were the physiological metric(s), direct SA measurement(s), correlation between these two metrics, and experimental task(s). Results Twenty-five articles were included in this review. Eye tracking techniques were the most commonly used physiological measures, and correlations between conscious aspects of eye movement measures and direct SA scores were observed. Evidence for cardiovascular predictors of SA was mixed. Only three electroencephalography (EEG) studies were identified, and their results suggest that EEG was sensitive to changes in SA. Overall, medium correlations were observed among the studies that reported a correlation coefficient between physiological and direct SA measures. Conclusion Reviewed studies observed relationships between a wide range of physiological measurements and direct assessments of SA. However, further investigations are needed to methodically collect more evidence. Application This review provides researchers and practitioners a summary of observed methods to indirectly assess SA with sensors and highlights research gaps to be addressed in future work.

show abstract

Section: Other Physiological Measuresmentioning

confidence: 99%

Section: Other Physiological Measuresmentioning

confidence: 99%

Physiological Measurements of Situation Awareness: A Systematic Review

et al. 2020

View full text Add to dashboard Cite

show abstract

“…It was found that there is co-activity in visual regions and prefrontal, anterior cingulate and parietal regions linked to cognition under uncertainty in the 100–150 ms following the loss of situation awareness. As illustrated in Yeo et al (2017), situation awareness can also be monitored in air-traffic controllers in real-time and accurately with portable EEG equipment.…”

Section: Applications Of Neuroscience Technologies For Human Augmementioning

confidence: 99%

Neurotechnologies for Human Cognitive Augmentation: Current State of the Art and Future Prospects

Cinel

Valeriani

Poli

2019

Front. Hum. Neurosci.

124

View full text Add to dashboard Cite

Recent advances in neuroscience have paved the way to innovative applications that cognitively augment and enhance humans in a variety of contexts. This paper aims at providing a snapshot of the current state of the art and a motivated forecast of the most likely developments in the next two decades. Firstly, we survey the main neuroscience technologies for both observing and influencing brain activity, which are necessary ingredients for human cognitive augmentation. We also compare and contrast such technologies, as their individual characteristics (e.g., spatio-temporal resolution, invasiveness, portability, energy requirements, and cost) influence their current and future role in human cognitive augmentation. Secondly, we chart the state of the art on neurotechnologies for human cognitive augmentation, keeping an eye both on the applications that already exist and those that are emerging or are likely to emerge in the next two decades. Particularly, we consider applications in the areas of communication, cognitive enhancement, memory, attention monitoring/enhancement, situation awareness and complex problem solving, and we look at what fraction of the population might benefit from such technologies and at the demands they impose in terms of user training. Thirdly, we briefly review the ethical issues associated with current neuroscience technologies. These are important because they may differentially influence both present and future research on (and adoption of) neurotechnologies for human cognitive augmentation: an inferior technology with no significant ethical issues may thrive while a superior technology causing widespread ethical concerns may end up being outlawed. Finally, based on the lessons learned in our analysis, using past trends and considering other related forecasts, we attempt to forecast the most likely future developments of neuroscience technology for human cognitive augmentation and provide informed recommendations for promising future research and exploitation avenues.

show abstract

“…Catherwood et al [14], used electroencephalography (EEG) to map brain activity during loss of situation awareness to identify target patterns and threats in urban scenes; their results showed that loss of situation awareness activated cortical areas associated with cognition, such as prefrontal, anterior cingulate, parietal, and visual regions. Yeo et al [15] used EEG to monitor SA in an air traffic controller (ATC) task. Their model predicted the response latency of the ATC operators with a 10% error.…”

Section: B Situation Awareness Measurement From Eeg Signalmentioning

confidence: 99%

EEG-based Recognition of Driver State Related to Situation Awareness Using Graph Convolutional Networks

Lan

Cui

et al. 2020

2020 International Conference on Cyberworlds (CW)

Self Cite

View full text Add to dashboard Cite

Extracting intra-and inter-subject parameters from Electroencephalogram (EEG) representing different Situation Awareness (SA) status is a critical challenge for objective SA recognition. Most of the existing work focuses on the subject-dependent classification that applies power spectrum density (PSD) features. In this paper, we propose a novel spectral-spatial (S-S) model for cross-subject fatigue-related SA recognition. The S-S model not only considers the biological topology across different brain regions to capture both local and global relations among different EEG channels, but also extracts spectral features for each EEG channel. Specifically, we firstly model the topological structure of EEG channels via an adjacency matrix which is built based on the Euclidean distance between EEG channels. Then, the graph convolution operation is employed to perform the neighbourhood aggregation for extracting spatial features. We test our model on a public dataset collected during driver's task performance. The subject-independent performance of the model is explored. Results demonstrate (1) the superior performance of our model compared with the state-of-theart models on SA recognition from EEG signals. Specifically, our S-S model achieves 70.6% accuracy which is higher than traditional machine learning methods by 2.7%-6.8% and deep learning methods by 10.3%-11.6%; (2) EEG signal at the occipital region can better reflect the change of SA.

show abstract

Mobile EEG-based situation awareness recognition for air traffic controllers

Cited by 27 publications

References 20 publications

Physiological Measurements of Situation Awareness: A Systematic Review

Physiological Measurements of Situation Awareness: A Systematic Review

Neurotechnologies for Human Cognitive Augmentation: Current State of the Art and Future Prospects

EEG-based Recognition of Driver State Related to Situation Awareness Using Graph Convolutional Networks

Contact Info

Product

Resources

About