[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122230-en":3,"doc-seo-122230-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122230,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Flight Crew’s Cognitive States Detection Using Psychophysiological Measurements and Machine Learning Techniques","This PhD thesis addresses the aviation-safety need for accurate assessment of pilots’ mental states by leveraging psychophysiological measurements, with a primary focus on Electroencephalogram (EEG) data. The dataset includes EEG alongside electrocardiogram, galvanic skin response, and respiration recordings drawn from attention-related human performance limiting states from a NASA open portal. The work analyzes EEG noise and evaluates preprocessing, finding common band-pass filtering plus Independent Component Analysis limited, then proposes hybrid ensemble learning using automated preprocessing and Riemannian geometry. It further fuses EEG with heterogeneous physiological signals via 1D Convolutional Neural Networks and Long Short-Term Memory, studies resampling for imbalance, and enhances interpretability through SHAP to identify features for distinct cognitive states.","IBRAHIM M. ALRESHIDI  \nFlight Crew’s Cognitive States Detection Using Psychophysiological Measurements and Machine Learning Techniques  \nSCHOOL OF AEROSPACE, TRANSPORT AND MANUFACTURING Computational Engineering Sciences  \nDOCTOR OF PHILOSOPHY (PhD) Academic Year: 2020-2023  \nSupervisor: Dr Irene Moulitsas Associate Supervisor: Karl W. Jenkins October 2023  \nSCHOOL OF AEROSPACE, TRANSPORT AND MANUFACTURING Computational Engineering Sciences  \nDOCTOR OF PHILOSOPHY (PhD) Academic Year 2020-2023  \nIBRAHIM M. ALRESHIDI  \nFlight Crew’s Cognitive States Detection Using Psychophysiological Measurements and Machine Learning Techniques  \nSupervisor: Dr Irene Moulitsas Associate Supervisor: Karl W. Jenkins October 2023  \nThis thesis is submitted in partial fulfilment of the requirements for  \nthe degree of PhD.  \n(NB. Remove if the degree award is based solely on examination of the thesis)  \n© Cranfield University 2023. All rights reserved. No part of this publication may be reproduced without the written permission of the  \ncopyright owner.  \nAcademic integrity declaration  \nI declare that:  \n• the thesis submitted has been written by me alone.  \n• the thesis submitted has not been previously submitted to this university or any other.  \n• that all content, including primary and/or secondary data, is true to the best of my knowledge.  \n• that all quotations and references have been duly acknowledged according to the requirements of academic research.  \nI understand that to knowingly submit work in violation of the above statement will be considered by examiners as academic misconduct.  \nABSTRACT  \nIn 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.  \nThe 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 EEGpreprocessing 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.  \nAs 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 ShortTerm 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.  \nTo 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.  \nKeywords:  \nElectroencephalography; EEG; Machine Learning; Deep Learning; Mental State Classification; Resampling Techniques; Aviation Safety; Pilot Behaviour; Ensemble Le","cbCaiovXqajyEiO1","https://ap.wps.com/l/cbCaiovXqajyEiO1","pdf",6729802,1,287,"English","en",105,"# Abstract\n## Data sources and study goals\n## EEG noise and preprocessing limitations\n## Hybrid ensemble learning with Riemannian geometry\n## Multimodal learning with CNN-LSTM\n## Handling class imbalance and model interpretability (SHAP)","[{\"question\":\"What psychophysiological data does the thesis use to detect flight crew cognitive states?\",\"answer\":\"The thesis primarily uses EEG and also incorporates electrocardiogram, galvanic skin response, and respiration data drawn from attention-related performance limiting states.\"},{\"question\":\"What issue does the thesis identify with common EEG preprocessing methods?\",\"answer\":\"It finds that prevalent preprocessing approaches—band-pass filtering combined with Independent Component Analysis—are not consistently effective due to noise challenges in EEG recordings.\"},{\"question\":\"How does the thesis improve both prediction performance and interpretability of cognitive-state models?\",\"answer\":\"It proposes hybrid ensemble learning (including advanced automated preprocessing with Riemannian geometry) and multimodal CNN-LSTM architectures, addresses data imbalance via resampling techniques, and uses SHAP values to connect model outputs to interpretable cognitive-state features.\"}]","Flight Crew’s Cognitive States Detection Using Psychophysiological Measurements and Machine Learning Techniques | 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psychophysiological data does the thesis use to detect flight crew cognitive states?","Question",{"text":75,"@type":76},"The thesis primarily uses EEG and also incorporates electrocardiogram, galvanic skin response, and respiration data drawn from attention-related performance limiting states.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What issue does the thesis identify with common EEG preprocessing methods?",{"text":80,"@type":76},"It finds that prevalent preprocessing approaches—band-pass filtering combined with Independent Component Analysis—are not consistently effective due to noise challenges in EEG recordings.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the thesis improve both prediction performance and interpretability of cognitive-state models?",{"text":84,"@type":76},"It proposes hybrid ensemble learning (including advanced automated preprocessing with Riemannian geometry) and multimodal CNN-LSTM architectures, addresses data imbalance via resampling 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