{"id":"https://openalex.org/W4393406637","doi":"https://doi.org/10.1109/bci60775.2024.10480494","title":"Integrated Convolutional and Graph Attention Neural Networks for Electroencephalography","display_name":"Integrated Convolutional and Graph Attention Neural Networks for Electroencephalography","publication_year":2024,"publication_date":"2024-02-26","ids":{"openalex":"https://openalex.org/W4393406637","doi":"https://doi.org/10.1109/bci60775.2024.10480494"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/bci60775.2024.10480494","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"proceedings-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/A5101326360","display_name":"Jae-Eon Kang","orcid":null},"institutions":[{"id":"https://openalex.org/I197347611","display_name":"Korea University","ror":"https://ror.org/047dqcg40","country_code":"KR","type":"education","lineage":["https://openalex.org/I197347611"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jae-Eon Kang","raw_affiliation_strings":["Dept. of Brain and Cognitive Engineering, Korea University, Seoul, Korea"],"affiliations":[{"raw_affiliation_string":"Dept. of Brain and Cognitive Engineering, Korea University, Seoul, Korea","institution_ids":["https://openalex.org/I197347611"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100731256","display_name":"Changha Lee","orcid":"https://orcid.org/0000-0003-3687-2989"},"institutions":[{"id":"https://openalex.org/I197347611","display_name":"Korea University","ror":"https://ror.org/047dqcg40","country_code":"KR","type":"education","lineage":["https://openalex.org/I197347611"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Changha Lee","raw_affiliation_strings":["Dept. of Brain and Cognitive Engineering, Korea University, Seoul, Korea"],"affiliations":[{"raw_affiliation_string":"Dept. of Brain and Cognitive Engineering, Korea University, Seoul, Korea","institution_ids":["https://openalex.org/I197347611"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5033376623","display_name":"Jong\u2010Hwan Lee","orcid":"https://orcid.org/0000-0002-8902-6009"},"institutions":[{"id":"https://openalex.org/I197347611","display_name":"Korea University","ror":"https://ror.org/047dqcg40","country_code":"KR","type":"education","lineage":["https://openalex.org/I197347611"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jong-Hwan Lee","raw_affiliation_strings":["Dept. of Brain and Cognitive Engineering, Korea University, Seoul, Korea"],"affiliations":[{"raw_affiliation_string":"Dept. of Brain and Cognitive Engineering, Korea University, Seoul, Korea","institution_ids":["https://openalex.org/I197347611"]}]}],"institution_assertions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":0,"max":83},"biblio":{"volume":"16","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9242,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9242,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9037,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[],"concepts":[{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.7384608},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6934701},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.62037325},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.49346605},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38258696},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.21646038},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.15345404},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.11159846}],"mesh":[],"locations_count":1,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/bci60775.2024.10480494","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false}],"best_oa_location":null,"sustainable_development_goals":[],"grants":[{"funder":"https://openalex.org/F4320320671","funder_display_name":"National Research Foundation","award_id":null},{"funder":"https://openalex.org/F4320322093","funder_display_name":"Electronics and Telecommunications Research Institute","award_id":null}],"datasets":[],"versions":[],"referenced_works_count":24,"referenced_works":["https://openalex.org/W2002055708","https://openalex.org/W2010371409","https://openalex.org/W2029934372","https://openalex.org/W2035118515","https://openalex.org/W2045663602","https://openalex.org/W2141627625","https://openalex.org/W2149490624","https://openalex.org/W2154384688","https://openalex.org/W2623902889","https://openalex.org/W2741907166","https://openalex.org/W2776975398","https://openalex.org/W2794345050","https://openalex.org/W2890363742","https://openalex.org/W2946344027","https://openalex.org/W3014541730","https://openalex.org/W3090080743","https://openalex.org/W3102455230","https://openalex.org/W4226149324","https://openalex.org/W4291974142","https://openalex.org/W4295517390","https://openalex.org/W4297733535","https://openalex.org/W4307815351","https://openalex.org/W4320167334","https://openalex.org/W4379141350"],"related_works":["https://openalex.org/W4308951944","https://openalex.org/W4293226380","https://openalex.org/W2922348724","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2376932109","https://openalex.org/W2358668433","https://openalex.org/W2130428257","https://openalex.org/W200322357","https://openalex.org/W2001405890"],"abstract_inverted_index":{"In":[0],"the":[1,16,24,35,47,63,71,87,97,104,108,111],"present":[2],"study,":[3],"we":[4],"propose":[5],"a":[6],"novel":[7],"deep":[8],"neural":[9,37],"network":[10,38,43],"(DNN)":[11],"model":[12],"designed":[13],"to":[14],"enhance":[15],"performance":[17],"and":[18,40,55,67,90,106],"interpretability":[19,109],"of":[20,34,86,110],"DNN":[21,99],"based":[22],"on":[23],"attention":[25,42],"module.":[26],"The":[27,76,93],"proposed":[28,94],"EEG-Graph":[29],"Attention":[30],"Network":[31],"(EEGAT)":[32],"consists":[33],"convolutional":[36],"(CNN)":[39],"graph":[41],"(GAT).":[44],"We":[45],"evaluated":[46],"EEGAT":[48,64,95],"using":[49],"three":[50],"heterogeneous":[51],"datasets:":[52],"Fatigue,":[53],"DEAP,":[54],"BCI":[56],"Competition":[57],"IV":[58],"2a.":[59],"Convolutional":[60],"kernels":[61],"in":[62],"extract":[65],"temporal":[66],"spatial":[68],"features":[69,79,85],"from":[70],"minimally":[72],"preprocessed":[73],"EEG":[74],"signals.":[75],"extracted":[77],"spatiotemporal":[78],"were":[80],"then":[81],"constructed":[82],"as":[83],"node":[84],"GAT":[88,102],"layers":[89],"subsequently":[91],"updated.":[92],"outperformed":[96],"alternative":[98],"models":[100],"without":[101],"across":[103],"datasets":[105],"enhanced":[107],"prediction":[112],"performance.":[113]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4393406637","counts_by_year":[],"updated_date":"2025-01-07T02:21:27.669005","created_date":"2024-04-03"}