EEG-based Classification of Microsleep by Means of Feature Selection: An Application in Aviation

Guragain, Bijay; Rad, Ali Bahrami; Wang, Chunwu; Verma, Ajay; Archer, Lewis; Wilson, Nicholas; Tavakolian, Kouhyar

doi:10.1109/embc.2019.8856429

Cited by 7 publications

(6 citation statements)

References 18 publications

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“…The results of this study also support technical feasibility of continued development of advanced headset technology designed to improve pilot situational awareness and monitoring physiological measures underway by Wilson and Tavakolian (2019). Earlier EEG research within a flight simulator also showed promise of EEG and other external measures such as eye tracking to detect periods of drowsiness and fatigue with pilots in a collegiate aviation environment (Guragain et al, 2019;Wang et al, 2019).…”

Section: Limitations and Future Directionsmentioning

confidence: 99%

“…A growing body of research focusing pilot, driver or operator physiological and cognitive state monitoring during operations of air or ground vehicles facilitates our understandings of the role of human and machine operations (Dussault et al, 2004;Wilson et al, 2019;Guragain et al, 2019;Wang et al, 2019). Within this domain of research, we can apply observed changes in physiological and cognitive state to invoke augmented cognition, or system adaptation based on the condition of the operator.…”

Section: Loss Of Situational Awareness During Commercialmentioning

confidence: 99%

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21st International Symposium on Aviation Psychology

Kibler¹,

Pyatt²,

Motamedi³

et al. 2021

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Katharine and Orville Wright sitting in Wright Model H Flyer, created 1914. Used courtesy of Special Collections and Archives, Wright Brothers Collection, Wright State University Libraries. https://corescholar.libraries.wright.edu/special_ms1_photographs/ 1088/ Description. Katharine Wright, wearing flying goggles, and Orville Wright sitting in a Wright Model H Flyer. Note on back, "Note vertical radiator made out of speaking tubes. Also note brake against wheel." i

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Section: Limitations and Future Directionsmentioning

confidence: 99%

Section: Loss Of Situational Awareness During Commercialmentioning

confidence: 99%

21st International Symposium on Aviation Psychology

Kibler¹,

Pyatt²,

Motamedi³

et al. 2021

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“…Focusing on aviation, interesting studies have been carried out using EEG techniques in various fields. For example, the study of microsleeps in commercial pilots was the focus of [3]. In this study, a method for classifying microsleeps was proposed using different EEG-derived features.…”

Section: Introductionmentioning

confidence: 99%

A step-by-step guide to include key electroencephalography (EEG) parameters in the study of human performance applied to air traffic control

Suárez,

Martínez,

Moreno

et al. 2024

J. Phys.: Conf. Ser.

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The study of human performance of air traffic controllers (ATCOs) is an interesting line of research to improve operational safety. In recent years, there has been an increase in the number of techniques available to develop this research based on massive data analysis. This study presents the use of certain electroencephalography (EEG) parameters to study the human performance of ATCOs. Although software and applications are now available to calculate these parameters, there are often problems in understanding the detailed process used to calculate them. The parameters presented in this study are intended to overcome this limitation and are applicable in real air traffic control (ATC) situations. Six parameters are analysed: excitement, stress, relaxation, boredom, engagement, and attention. As an application case for the parameters, a total of 50 data samples obtained during the development of real-time simulations on a highly realistic ATC platform are analysed. From these data, the above-mentioned EEG parameters and their trends are calculated. In addition, the evolution of these parameters is studied in relation to two other variables that characterise the operational situation of the sector during the simulations: the taskload based on ATC events and the number of simultaneous aircraft in the sector per minute.

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“…It can record neural activity in sleep through different frequency bands. This technology has been widely used as a standard to quantify potential neural activity in sleep research [13], [14]. Research has found that most patients with bruxism have signi cant signs of increased electrical activity in the cerebral cortex during tooth grinding [1].…”

Section: Introductionmentioning

confidence: 99%

Classification of Bruxism Based on Time- frequency and Nonlinear Features of single channel EEG

wang,

Verma,

Guragain

et al. 2023

Preprint

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Background In the classification of Bruxism patients based on electroencephalogram (EEG), feature extraction is essential. The method of using multi-channel EEG and fusing electrocardiogram (ECG) and Electromyography (EMG) signal features has been proved to have good performance in Bruxism classification, but the classification performance based on single channel EEG signal is still unsatisfactory. Methods Extract time-domain, frequency-domain and nonlinear features based on single channel EEG signal to improve the classification performance of Bruxism. Five common bipolar EEG recordings from 2 bruxism patients and 4 healthy controls during REM sleep were analyzed. The time domain (mean, standard deviation, root mean squared value), frequency domain (absolute, relative and ratios power spectral density (PSD)), and non-linear features include (sample entropy) of different EEG frequency bands were extracted from five channels of participant. Fine tree algorithm was trained and tested for classifying sleep bruxism and healthy controls using five-fold cross-validation. Results Our results suggest that the C4P4 EEG channel was found to be most useful for classification of sleep bruxism and yielded 95.59% sensitivity, 98.44% specificity, 97.84% accuracy, and 94.20% positive predictive value (PPV). Conclusions Our research has proved that the proposed features are effective, further demonstrating the feasibility of using only one EEG channel to classify sleep Bruxism, and providing experimental basis for developing a portable automatic sleep Bruxism detection system.

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EEG-based Classification of Microsleep by Means of Feature Selection: An Application in Aviation

Cited by 7 publications

References 18 publications

21st International Symposium on Aviation Psychology

21st International Symposium on Aviation Psychology

A step-by-step guide to include key electroencephalography (EEG) parameters in the study of human performance applied to air traffic control

Classification of Bruxism Based on Time- frequency and Nonlinear Features of single channel EEG

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