[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128053-en":3,"doc-seo-128053-105":31,"detail-sidebar-cat-0-en-105":92},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128053,5909887256941,"Levi","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Context-aware machine learning for low-burden brain-computer interfaces - A dissertation","The dissertation investigates how context-aware artificial intelligence can enable mobile assistive systems, such as reconnaissance drones, to support real-time situational awareness in fast-paced high-stakes scenarios like disaster relief. The goal is to minimize additional cognitive and physical burden on users by recognizing intent with electroencephalography in an object recognition waypoint selection paradigm. It proposes Human Intent-Guided Autonomous Systems (HIGAS) and introduces the Context-Signal Decision Fusion (CSDF) model that merges EEG with imagery. A 42-subject experiment evaluates viability, requirements, performance, and dynamics, including subject-independence, architectural variation, and explainability-oriented mechanisms using relatively low-cost portable hardware.","University of Alabama in Huntsville  \nLOUIS  \n\n| Dissertations | UAH Electronic Theses and Dissertations |\n| --- | --- |\n| 2024\u003Cbr>Context-aware machine learning for low-burden brain-computer interfaces\u003Cbr>T. Warren de Wit\u003Cbr>Follow this and additional works at: [https://louis.uah.edu/uah-dissertations](https://louis.uah.edu/uah-dissertations) |  |\n\nRecommended Citation  \nde Wit, T. Warren, \"Context-aware machine learning for low-burden brain-computer interfaces\" (2024) . Dissertations. 417.  \n[https://louis.uah.edu/uah-dissertations/417](https://louis.uah.edu/uah-dissertations/417)  \nThis Dissertation is brought to you for free and open access by the UAH Electronic Theses and Dissertations at LOUIS. It has been accepted for inclusion in Dissertations by an authorized administrator of LOUIS.  \nCONTEXT-AWARE MACHINE LEARNING FOR LOW-BURDEN BRAIN-COMPUTER  \nINTERFACES  \nT . Warren de Wit  \nA DISSERTATION  \nSubmitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy  \nin  \nThe Department of Computer Science  \nto  \nThe Graduate School  \nof  \nThe University of Alabama in Huntsville August 2024  \nApproved by:  \nDr. Vineetha Menon, Research Advisor/Committee Chair Dr. Huaming Zhang, Committee Member  \nDr. Tathagata Mukherjee, Committee Member Dr. Nathan L. Tenhundfeld, Committee Member  \nDr. Bryan Mesmer, Committee Member Dr. Letha Etzkorn, Department Chair Dr. Rainer Steinwandt, College Dean Dr. Jon Hakkila, Graduate Dean  \nAbstract  \nCONTEXT-AWARE MACHINE LEARNING FOR LOW-BURDEN BRAIN-COMPUTER INTERFACES  \nT . Warren de Wit  \nA dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy  \nComputer Science  \nThe University of Alabama in Huntsville  \nAugust 2024  \nWe are interested in the utility that artificially intelligent mobile systems such as drones offer to personnel in fast-paced, high-stakes situations such as disaster relief that demand real-time situational awareness. Ideally, these assistive systems place no additional cognitive or physical burden on their user; rather, they should respond to the user’s intent with minimal physical or cognitive impact. Artificial intelligence (AI) and machine learning (ML) are already widely applied in both brain-computer interfaces (BCI) and drone navigation. We propose leveraging the robust computervision based AI that exists on modern drones to use objects as waypoints and fly a reconnaissance drone mostly autonomously, with electroencephalography (EEG) inan object recognition paradigm for selecting the drone’s waypoint. In this work, our goal is to provide a proof-of-concept for the intent recognition portion of this design through a context fusion approach that allows selecting a waypoint without using existing techniques that require environmental modification or techniques such as Rapid Serial Visual Presentation (RSVP) that do not translate to kinetic situations. We outline a framework we call Human Intent-Guided Autonomous Systems (HIGAS) as a general paradigm for this type of system-of-systems that facilitate human-  \nAI teaming by using decision fusion between biosignal-based intent recognition and sensor-borne context awareness. We introduce the Context-Signal Decision Fusion (CSDF) model to merge EEG with imagery and conduct a 42-subject experiment to explore its viability, requirements, performance, and dynamics. In the end, we show that CSDF shows potential for implementation in the wider HI-GAS framework even with relatively low-cost, portable hardware. We evaluate the model under a variety of dynamics, identify results regarding subject-independence and architectural variation, and present mechanisms to explore the model from an explainability perspective.  \niv  \nAcknowledgements  \nI would like to thank many people for their generosity of time, resources, tenacity, and support that made this work possible.  \nMy advisor, Dr. Vineetha Menon, has continuously and tirelessly worked to enable both my ","cbCaikhMYTfquYUU","https://ap.wps.com/l/cbCaikhMYTfquYUU","pdf",28803718,2,1,153,"English","en",105,"# Abstract\n# Acknowledgements\n# Table of Contents\n# List of Figures","[{\"question\":\"What is the main research goal of this dissertation?\",\"answer\":\"To provide a proof-of-concept for intent recognition in a context-aware system that can select waypoints for a mostly autonomous reconnaissance drone while minimizing user cognitive and physical burden.\"},{\"question\":\"What framework and model does the dissertation propose?\",\"answer\":\"It outlines Human Intent-Guided Autonomous Systems (HIGAS) and introduces the Context-Signal Decision Fusion (CSDF) model to merge EEG with imagery for waypoint selection.\"},{\"question\":\"How is the CSDF model evaluated and what factors are studied?\",\"answer\":\"The dissertation conducts a 42-subject experiment to assess viability, requirements, performance, and dynamics, including subject-independence, architectural variation, and explainability-oriented mechanisms.\"}]","Context-aware machine learning for low-burden brain-computer interfaces - A dissertation | PDF",1785944498,386,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"context-aware-machine-learning-for-low-burden-brain-computer-interfaces-a-dissertation","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/context-aware-machine-learning-for-low-burden-brain-computer-interfaces-a-dissertation/128053/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-27","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What is the main research goal of this dissertation?","Question",{"text":76,"@type":77},"To provide a proof-of-concept for intent recognition in a context-aware system that can select waypoints for a mostly autonomous reconnaissance drone while minimizing user cognitive and physical burden.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What framework and model does the dissertation propose?",{"text":81,"@type":77},"It outlines Human Intent-Guided Autonomous Systems (HIGAS) and introduces the Context-Signal Decision Fusion (CSDF) model to merge EEG with imagery for waypoint selection.",{"name":83,"@type":74,"acceptedAnswer":84},"How is the CSDF model evaluated and what factors are studied?",{"text":85,"@type":77},"The dissertation conducts a 42-subject experiment to assess viability, requirements, performance, and dynamics, including subject-independence, architectural variation, and explainability-oriented mechanisms.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]