[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-662687-105":59,"doc-detail-662687-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","inference-of-cognitive-load-when-understanding-mechanical-drawings-by-electroencephalography-for-skill-acquisition-interviews","Inference of Cognitive Load When Understanding Mechanical Drawings by Electroencephalography for Skill Acquisition Interviews","","Process planning requires strong standardization to achieve high-quality machining without relying on an operator’s skill level, yet automation remains difficult because interview-based skill extraction involves complicated decision processes. Electroencephalography (EEG) is used to capture brain activity related to unconscious judgment during mechanical-drawing interpretation. The study classifies EEG by frequency bands, visualizes alpha/beta power ratios, and builds a machine-learning model using time-series EEG via a common spatial pattern method. Results show the feasibility of identifying time-varying cognitive load to support skill acquisition interviews.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/inference-of-cognitive-load-when-understanding-mechanical-drawings-by-electroencephalography-for-skill-acquisition-interviews/662687/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/inference-of-cognitive-load-when-understanding-mechanical-drawings-by-electroencephalography-for-skill-acquisition-interviews/662687.png","ImageObject",300,407,{"name":92,"@type":93},"McQueen","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-29","2026-10-28",true,{"@type":102,"interactionType":103,"userInteractionCount":8},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why is process planning difficult to automate from operator interviews?","Question",{"text":112,"@type":113},"Process planning decision-making is complicated, and extracting usable skills through interviews is hard to automate reliably from skilled operators.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does the study quantify cognitive load from EEG?",{"text":117,"@type":113},"EEG data are classified by frequency bands (e.g., alpha and beta), and cognitive load characteristics are visualized using the power spectrum ratio of alpha over beta.",{"name":119,"@type":110,"acceptedAnswer":120},"How is cognitive load inference used to support skill acquisition interviews?",{"text":121,"@type":113},"A machine-learning model is trained using time-series EEG (with a common spatial pattern method), then applied to EEG collected during mechanical-drawing understanding to classify difficulty levels and identify time-varying cognitive load.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},662687,1793221714,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":8,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":39,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":129,"read_time":46},5909890329169,"https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc","[https://doi.org/10.20965/ijat.2025.p0733](https://doi.org/10.20965/ijat.2025.p0733)  \nResearch Paper:  \nInference of Cognitive Load When Understanding Mechanical Drawings by Electroencephalography for Skill Acquisition Interviews  \nHideto Sairenchi, Hikaru Yokoyama, and Keiichi Nakamoto†  \nTokyo University of Agriculture and Technology  \n2-24-16 Naka-cho, Koganei, Tokyo 184-8588, Japan  \n†Corresponding author, E-mail: [nakamoto@cc.tuat.ac.jp](nakamoto@cc.tuat.ac.jp)  \n[Received February 28, 2025; accepted June 10, 2025]  \nProcess planning needs to be strongly standardized to achieve high-quality and efficient machining without depending on the skill level of the operator. However, it is difficult to automate process planning by acquiring the skills from interviews with skillful operators due to complicated decision-making processes. In contrast, electroencephalography (EEG) has attracted considerable attention recently to obtain brain activity as a key to revealing operators’ decision-making processes. Therefore, this study aims to infer the cognitive load of the operators by EEG when understanding mechanical drawings for skills acquisition interviews. EEG data were classified by frequency bands, such as alpha and beta. The brain activity within these frequency bands reflects the characteristics of cognitive load across tasks. The degree of alpha over beta was visualized by calculating the power spectrum ratio of the EEG data. Multiple types of preliminary tasks were prepared with different difficulty levels to infer cognitive load. Subsequently, a machine-learning model was constructed by adapting the common spatial pattern method to infer cognitive load, which varied according to the difficulty level, from the obtained timeseries EEG data. After validation of cognitive load inference, a machine-learning model was applied to the EEG data obtained during the understanding of mechanical drawings to classify the difficulty levels. The inference results demonstrated the possibility of identifying time-varying cognitive load and support the interviews to acquire machining skills.  \nKeywords: machining skill, process planning, cognitive load, electroencephalography, machine learning  \n1 . Introduction  \nProcess planning is typically performed by recognizing and interpreting the shapes and annotations of twodimensional (2D) mechanical drawings during part machining. Consequently, the machining process strongly depends on the skill level of the operator. Therefore, the  \nstandardization of planning is necessary to realize highquality machining without depending on the skill level of the operator. Recently, research on acquiring and utilizing operators’ skills across different fields has gained significant attention [1–8] . For instance, the acquisition of rehabilitation-related skills through interviews with skilled nurses in the medical field [1, 2] . Similarly, a decisionmaking support system in the manufacturing industry has been developed for computer numerical control operators, utilizing the accumulated knowledge of skilled operators to assist unskilled operators [3] . Moreover, data mining technology has been applied to tool catalogs to support rational decision-making in process planning for end milling by extracting useful knowledge [4] . In addition, machinelearning techniques have been employed to enhance process planning in machining [5–7], and augmented reality has been applied to support the setup of machining processes [8] .  \nIn particular, eye-tracking technology is a novel approach for efficiently capturing implicit knowledge. Several studies have demonstrated the use of this technology to acquire implicit knowledge of skilled operators in machining in the manufacturing industry [9–12] . In addition, the gaze data of operators when understanding mechanical drawings have been obtained by several researchers [13–16] . However, gaze data are not always related to expected machining skills. Therefore, data cleansing wa","cbCaioHwkguRZfFP","https://ap.wps.com/l/cbCaioHwkguRZfFP","pdf",4106649,"English","# Introduction\n## Process planning and operator skill dependence\n## Eye-tracking based implicit knowledge acquisition\n## EEG-based cognitive load inference and related research\n## Study objective and approach","[{\"question\":\"Why is process planning difficult to automate from operator interviews?\",\"answer\":\"Process planning decision-making is complicated, and extracting usable skills through interviews is hard to automate reliably from skilled operators.\"},{\"question\":\"How does the study quantify cognitive load from EEG?\",\"answer\":\"EEG data are classified by frequency bands (e.g., alpha and beta), and cognitive load characteristics are visualized using the power spectrum ratio of alpha over beta.\"},{\"question\":\"How is cognitive load inference used to support skill acquisition interviews?\",\"answer\":\"A machine-learning model is trained using time-series EEG (with a common spatial pattern method), then applied to EEG collected during mechanical-drawing understanding to classify difficulty levels and identify time-varying cognitive load.\"}]","Inference of Cognitive Load When Understanding Mechanical Drawings by Electroencephalography for Skill Acquisition Interviews | PDF"]