{"id":"https://openalex.org/W4403677455","doi":"https://doi.org/10.1109/case59546.2024.10711658","title":"Unveiling Designers' Cognitive States in Early Engineering Design Stages: An Autoencoder-DNN Approach based on EEG Data*","display_name":"Unveiling Designers' Cognitive States in Early Engineering Design Stages: An Autoencoder-DNN Approach based on EEG Data*","publication_year":2024,"publication_date":"2024-08-28","ids":{"openalex":"https://openalex.org/W4403677455","doi":"https://doi.org/10.1109/case59546.2024.10711658"},"language":"en","primary_location":{"id":"doi:10.1109/case59546.2024.10711658","is_oa":false,"landing_page_url":"https://doi.org/10.1109/case59546.2024.10711658","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 20th International Conference on Automation Science and Engineering (CASE)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100684503","display_name":"Mingrui Li","orcid":"https://orcid.org/0009-0005-9444-4706"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Mingrui Li","raw_affiliation_strings":["Beihang University,School of Mechanical Engineering and Automation,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Beihang University,School of Mechanical Engineering and Automation,Beijing,China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037251172","display_name":"Zuoxu Wang","orcid":"https://orcid.org/0000-0003-2524-1217"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zuoxu Wang","raw_affiliation_strings":["Beihang University,School of Mechanical Engineering and Automation,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Beihang University,School of Mechanical Engineering and Automation,Beijing,China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100373560","display_name":"Fan Li","orcid":"https://orcid.org/0000-0002-2348-4488"},"institutions":[{"id":"https://openalex.org/I14243506","display_name":"Hong Kong Polytechnic University","ror":"https://ror.org/0030zas98","country_code":"HK","type":"education","lineage":["https://openalex.org/I14243506"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Fan Li","raw_affiliation_strings":["The Hong Kong Polytechnic University,Department of Aeronautical and Aviation Engineering,Hong Kong Special Administrative Region,999077"],"affiliations":[{"raw_affiliation_string":"The Hong Kong Polytechnic University,Department of Aeronautical and Aviation Engineering,Hong Kong Special Administrative Region,999077","institution_ids":["https://openalex.org/I14243506"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100422747","display_name":"Jihong Liu","orcid":"https://orcid.org/0000-0002-0733-4746"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jihong Liu","raw_affiliation_strings":["Beihang University,School of Mechanical Engineering and Automation,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Beihang University,School of Mechanical Engineering and Automation,Beijing,China","institution_ids":["https://openalex.org/I82880672"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100684503"],"corresponding_institution_ids":["https://openalex.org/I82880672"],"apc_list":null,"apc_paid":null,"fwci":0.2252,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.50001619,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1361","last_page":"1366"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10672","display_name":"Design Education and Practice","score":0.9857000112533569,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10672","display_name":"Design Education and Practice","score":0.9857000112533569,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T14306","display_name":"Technology Assessment and Management","score":0.9222000241279602,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11159","display_name":"Manufacturing Process and Optimization","score":0.9151999950408936,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.8717939853668213},{"id":"https://openalex.org/keywords/electroencephalography","display_name":"Electroencephalography","score":0.777790904045105},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6270265579223633},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5031635165214539},{"id":"https://openalex.org/keywords/cognition","display_name":"Cognition","score":0.45443668961524963},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.32066798210144043},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.2642786502838135},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.19256368279457092},{"id":"https://openalex.org/keywords/neuroscience","display_name":"Neuroscience","score":0.09440359473228455}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.8717939853668213},{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.777790904045105},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6270265579223633},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5031635165214539},{"id":"https://openalex.org/C169900460","wikidata":"https://www.wikidata.org/wiki/Q2200417","display_name":"Cognition","level":2,"score":0.45443668961524963},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32066798210144043},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2642786502838135},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.19256368279457092},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.09440359473228455}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/case59546.2024.10711658","is_oa":false,"landing_page_url":"https://doi.org/10.1109/case59546.2024.10711658","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 20th International Conference on Automation Science and Engineering (CASE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.4099999964237213,"display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W2100495367","https://openalex.org/W2136922672","https://openalex.org/W2919897588","https://openalex.org/W2946571866","https://openalex.org/W2998550520","https://openalex.org/W3015476915","https://openalex.org/W3016097197","https://openalex.org/W3049206623","https://openalex.org/W3114810070","https://openalex.org/W3119241130","https://openalex.org/W4211169440","https://openalex.org/W4308413201","https://openalex.org/W4384028212","https://openalex.org/W4387123982","https://openalex.org/W4387499030"],"related_works":["https://openalex.org/W3013693939","https://openalex.org/W2159052453","https://openalex.org/W2566616303","https://openalex.org/W3131327266","https://openalex.org/W2734887215","https://openalex.org/W4297051394","https://openalex.org/W2752972570","https://openalex.org/W4220775285","https://openalex.org/W2033914206","https://openalex.org/W2042327336"],"abstract_inverted_index":{"Early":[0],"engineering":[1,72,107],"product":[2,11],"design":[3,9,47,73,89,108,155],"stages,":[4],"such":[5,96],"as":[6],"innovative":[7],"conceptual":[8],"and":[10,84,114,147,168,174],"form":[12],"design,":[13],"require":[14],"intensive":[15],"thinking":[16],"by":[17],"designers.":[18],"During":[19],"these":[20],"tasks,":[21],"designers":[22,51,182],"experience":[23],"constantly":[24],"shifting":[25],"cognitive":[26,37,62,102,133,144],"states":[27,38,171],"of":[28,60,78,153,163,172],"either":[29],"Trance,":[30],"Concentration":[31,173],"or":[32],"Confusion.":[33],"Accurately":[34],"recognizing":[35],"designer\u2019s":[36],"is":[39,56,91],"a":[40,101,111],"prerequisite":[41],"task":[42],"to":[43,50,130,179],"provide":[44],"assistance,":[45],"e.g.":[46],"knowledge":[48],"recommendation,":[49],"timely.":[52],"Electroencephalogram":[53],"(EEG)":[54],"data":[55,146],"the":[57,85,88,137,151,161,164],"external":[58],"expression":[59],"designers\u2019":[61],"states.":[63],"However,":[64],"current":[65],"research":[66],"on":[67],"applying":[68],"EEG":[69,82],"technology":[70],"in":[71,150],"scenarios":[74],"lacks":[75],"automatic":[76],"selection":[77],"significant":[79,128],"features":[80,129],"from":[81],"information,":[83],"connection":[86],"with":[87,95],"process":[90],"also":[92],"limited.":[93],"Faced":[94],"issues,":[97],"this":[98],"study":[99],"proposes":[100],"state":[103,134,145],"recognition":[104,112,123,166],"procedure":[105],"for":[106],"scenarios,":[109],"including":[110],"model":[113,124],"an":[115],"experiment":[116,139,158],"protocol.":[117],"The":[118,157],"Autoencoder-Deep":[119],"Neural":[120],"Network":[121],"(DNN)-based":[122],"can":[125,141,176],"flexibly":[126],"select":[127],"achieve":[131],"accurate":[132],"recognition,":[135],"while":[136],"two-stage":[138],"protocol":[140],"collect":[142],"standard":[143],"perform":[148],"validation":[149],"simulation":[152],"real":[154],"scenarios.":[156],"results":[159],"demonstrate":[160],"validity":[162],"proposed":[165],"procedure,":[167],"confirmed":[169],"that":[170],"Confusion":[175],"be":[177],"utilized":[178],"determine":[180],"whether":[181],"need":[183],"assistance.":[184]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-12-19T19:40:27.379048","created_date":"2025-10-10T00:00:00"}