Multimodal approach for pilot mental state detection based on EEG

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dc.contributor.authorAlreshidi, Ibrahim
dc.contributor.authorMoulitsas, Irene
dc.contributor.authorJenkins, Karl W.
dc.date.accessioned2023-08-24T10:33:32Z
dc.date.available2023-08-24T10:33:32Z
dc.date.issued2023-08-23
dc.description.abstractThe safety of flight operations depends on the cognitive abilities of pilots. In recent years, there has been growing concern about potential accidents caused by the declining mental states of pilots. We have developed a novel multimodal approach for mental state detection in pilots using electroencephalography (EEG) signals. Our approach includes an advanced automated preprocessing pipeline to remove artefacts from the EEG data, a feature extraction method based on Riemannian geometry analysis of the cleaned EEG data, and a hybrid ensemble learning technique that combines the results of several machine learning classifiers. The proposed approach provides improved accuracy compared to existing methods, achieving an accuracy of 86% when tested on cleaned EEG data. The EEG dataset was collected from 18 pilots who participated in flight experiments and publicly released at NASA’s open portal. This study presents a reliable and efficient solution for detecting mental states in pilots and highlights the potential of EEG signals and ensemble learning algorithms in developing cognitive cockpit systems. The use of an automated preprocessing pipeline, feature extraction method based on Riemannian geometry analysis, and hybrid ensemble learning technique set this work apart from previous efforts in the field and demonstrates the innovative nature of the proposed approach.en_UK
dc.identifier.citationAlreshidi I, Moulitsas I, Jenkins KW. (2023) Multimodal approach for pilot mental state detection based on EEG. Sensors, Volume 23, Issue 17, August 2023, Article number 7350en_UK
dc.identifier.issn1424-8220
dc.identifier.urihttps://doi.org/10.3390/s23177350
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/20140
dc.language.isoenen_UK
dc.publisherMDPIen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectensemble learningen_UK
dc.subjectmachine learningen_UK
dc.subjectEEGen_UK
dc.subjectpilot deficienciesen_UK
dc.subjectartifact detectionen_UK
dc.subjecttangent spaceen_UK
dc.subjectEEG preprocessingen_UK
dc.subjectheterogeneous dataen_UK
dc.subjectmental states classificationen_UK
dc.subjectfeature extractionen_UK
dc.titleMultimodal approach for pilot mental state detection based on EEGen_UK
dc.typeArticleen_UK

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