Flight crew’s cognitive states detection using psychophysiological measurements and machine learning techniques

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dc.contributor.advisorMoulitsas, Irene
dc.contributor.advisorJenkins, Karl W.
dc.contributor.authorAlreshidi, Ibrahim M.
dc.date.accessioned2025-05-07T13:58:29Z
dc.date.available2025-05-07T13:58:29Z
dc.date.freetoread2025-05-07
dc.date.issued2023-10
dc.description.abstractIn the ever-evolving landscape of aviation safety, the accurate assessment of pilots' mental states is of paramount significance. This thesis elucidates the critical role of Electroencephalogram (EEG) data in comprehending pilots' cognitive conditions. The dataset, sourced from attention-related human performance limiting states, was publicly available on the NASA open portal website and encompasses EEG, electrocardiogram, galvanic skin response, and respiration data. The initial analyses delved into the challenges posed by noise within EEG recordings. After rigorous testing, it was observed that prevalent preprocessing techniques, specifically band-pass filtering coupled with Independent Component Analysis, were not always effective. This inefficiency underscored the need for more advanced methodologies to optimize machine learning outcomes. In response, subsequent research stages proposed a hybrid ensemble learning approach. This innovative approach integrated advanced automated EEG preprocessing with Riemannian geometry. Through rigorous experimentation and validation, it was determined that this methodology accentuated the profound advantages of refined preprocessing, significantly enhancing the accuracy and reliability of EEG data interpretation. As the inquiry advanced, a more integrative approach was adopted, amalgamating EEG with other physiological data. A novel methodology, synergizing one-dimensional Convolutional Neural Networks with Long Short- Term Memory architectures, was unveiled. Additionally, the impact of employing methods to handle data imbalance on machine learning performance was thoroughly examined. In the concluding phases, the research placed a heightened emphasis on model interpretability. Through the integration of SHapley Additive exPlanations values, a bridge was constructed between intricate model predictions and nuanced human comprehension, delineating paramount features for distinct cognitive states. To encapsulate, this thesis offers a meticulous dissection of EEG data manipulation, machine learning, and deep learning constructs, positing a blueprint for the augmentation of aviation safety through in-depth cognitive state evaluations.
dc.description.coursenamePhD in Aerospace
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/23870
dc.language.isoen
dc.publisherCranfield University
dc.publisher.departmentSATM
dc.rights© Cranfield University, 2023. All rights reserved. No part of this publication may be reproduced without the written permission of the copyright holder.
dc.subjectElectroencephalography
dc.subjectEEG
dc.subjectMachine Learning
dc.subjectDeep Learning
dc.subjectMental State Classification
dc.subjectResampling Techniques
dc.subjectAviation Safety
dc.subjectPilot Behaviour
dc.subjectEnsemble Learning
dc.subjectPilot Deficiencies
dc.subjectArtefact Detection
dc.subjectTangent Space
dc.subjectEEG Preprocessing
dc.subjectHeterogeneous Data
dc.titleFlight crew’s cognitive states detection using psychophysiological measurements and machine learning techniques
dc.typeThesis
dc.type.qualificationlevelDoctoral
dc.type.qualificationnamePhD

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