{"id":"https://openalex.org/W3019144816","doi":"https://doi.org/10.1109/civemsa45640.2019.9071630","title":"Using Long Short- Term Memory Network for Recognizing Motor Imagery Tasks","display_name":"Using Long Short- Term Memory Network for Recognizing Motor Imagery Tasks","publication_year":2019,"publication_date":"2019-06-01","ids":{"openalex":"https://openalex.org/W3019144816","doi":"https://doi.org/10.1109/civemsa45640.2019.9071630","mag":"3019144816"},"language":"en","primary_location":{"id":"doi:10.1109/civemsa45640.2019.9071630","is_oa":false,"landing_page_url":"https://doi.org/10.1109/civemsa45640.2019.9071630","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","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/A5101825789","display_name":"Xiaoyan Xu","orcid":"https://orcid.org/0009-0000-6483-7553"},"institutions":[{"id":"https://openalex.org/I4210142748","display_name":"Shandong Academy of Sciences","ror":"https://ror.org/04y8d6y55","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210142748"]},{"id":"https://openalex.org/I152269853","display_name":"Qilu University of Technology","ror":"https://ror.org/04hyzq608","country_code":"CN","type":"education","lineage":["https://openalex.org/I152269853"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiaoyan Xu","raw_affiliation_strings":["School of Electrical Engineering and Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China"],"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering and Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China","institution_ids":["https://openalex.org/I4210142748","https://openalex.org/I152269853"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004432231","display_name":"Fangzhou Xu","orcid":"https://orcid.org/0000-0001-7660-1206"},"institutions":[{"id":"https://openalex.org/I4210100255","display_name":"Beijing Academy of Artificial Intelligence","ror":"https://ror.org/016a74861","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210100255"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fangzhou Xu","raw_affiliation_strings":["China and Key Laboratory of Medical Artificial Intelligence, Jinan, China"],"affiliations":[{"raw_affiliation_string":"China and Key Laboratory of Medical Artificial Intelligence, Jinan, China","institution_ids":["https://openalex.org/I4210100255"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044470103","display_name":"Minglei Shu","orcid":"https://orcid.org/0000-0002-7136-1538"},"institutions":[{"id":"https://openalex.org/I152269853","display_name":"Qilu University of Technology","ror":"https://ror.org/04hyzq608","country_code":"CN","type":"education","lineage":["https://openalex.org/I152269853"]},{"id":"https://openalex.org/I4210142748","display_name":"Shandong Academy of Sciences","ror":"https://ror.org/04y8d6y55","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210142748"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Minglei Shu","raw_affiliation_strings":["Qilu University of Technology, (Shandong Academy of Sciences), Jinan, China"],"affiliations":[{"raw_affiliation_string":"Qilu University of Technology, (Shandong Academy of Sciences), Jinan, China","institution_ids":["https://openalex.org/I152269853","https://openalex.org/I4210142748"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100761349","display_name":"Yingchun Zhang","orcid":"https://orcid.org/0000-0002-1927-4103"},"institutions":[{"id":"https://openalex.org/I4210142748","display_name":"Shandong Academy of Sciences","ror":"https://ror.org/04y8d6y55","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210142748"]},{"id":"https://openalex.org/I152269853","display_name":"Qilu University of Technology","ror":"https://ror.org/04hyzq608","country_code":"CN","type":"education","lineage":["https://openalex.org/I152269853"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yingchun Zhang","raw_affiliation_strings":["Engineering Training Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China"],"affiliations":[{"raw_affiliation_string":"Engineering Training Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China","institution_ids":["https://openalex.org/I152269853","https://openalex.org/I4210142748"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019180907","display_name":"Qi Yuan","orcid":"https://orcid.org/0000-0002-1342-3374"},"institutions":[{"id":"https://openalex.org/I28006308","display_name":"Shandong Normal University","ror":"https://ror.org/01wy3h363","country_code":"CN","type":"education","lineage":["https://openalex.org/I28006308"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qi Yuan","raw_affiliation_strings":["School of Physics and Electronics, Shandong Normal University, Jinan, China"],"affiliations":[{"raw_affiliation_string":"School of Physics and Electronics, Shandong Normal University, Jinan, China","institution_ids":["https://openalex.org/I28006308"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5007211501","display_name":"Yuanjie Zheng","orcid":"https://orcid.org/0000-0002-5786-2491"},"institutions":[{"id":"https://openalex.org/I28006308","display_name":"Shandong Normal University","ror":"https://ror.org/01wy3h363","country_code":"CN","type":"education","lineage":["https://openalex.org/I28006308"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuanjie Zheng","raw_affiliation_strings":["Institute of Biomedical Sciences, Shandong Normal University, Jinan, China"],"affiliations":[{"raw_affiliation_string":"Institute of Biomedical Sciences, Shandong Normal University, Jinan, China","institution_ids":["https://openalex.org/I28006308"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5101825789"],"corresponding_institution_ids":["https://openalex.org/I152269853","https://openalex.org/I4210142748"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.21652857,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"10","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":1.0,"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"}},{"id":"https://openalex.org/T10784","display_name":"Muscle activation and electromyography studies","score":0.9916999936103821,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/T11707","display_name":"Gaze Tracking and Assistive Technology","score":0.9865999817848206,"subfield":{"id":"https://openalex.org/subfields/1709","display_name":"Human-Computer Interaction"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8270490169525146},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6843898892402649},{"id":"https://openalex.org/keywords/motor-imagery","display_name":"Motor imagery","score":0.5639418363571167},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.5406996011734009},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5042613744735718},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.48601609468460083},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.47087299823760986},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4672562777996063},{"id":"https://openalex.org/keywords/brain\u2013computer-interface","display_name":"Brain\u2013computer interface","score":0.4584784507751465},{"id":"https://openalex.org/keywords/long-short-term-memory","display_name":"Long short term memory","score":0.44007208943367004},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.42523854970932007},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.39458703994750977},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.34511715173721313},{"id":"https://openalex.org/keywords/electroencephalography","display_name":"Electroencephalography","score":0.3263150453567505}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8270490169525146},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6843898892402649},{"id":"https://openalex.org/C54808283","wikidata":"https://www.wikidata.org/wiki/Q6918191","display_name":"Motor imagery","level":4,"score":0.5639418363571167},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.5406996011734009},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5042613744735718},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.48601609468460083},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.47087299823760986},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4672562777996063},{"id":"https://openalex.org/C173201364","wikidata":"https://www.wikidata.org/wiki/Q897410","display_name":"Brain\u2013computer interface","level":3,"score":0.4584784507751465},{"id":"https://openalex.org/C133488467","wikidata":"https://www.wikidata.org/wiki/Q6673524","display_name":"Long short term memory","level":4,"score":0.44007208943367004},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.42523854970932007},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39458703994750977},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.34511715173721313},{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.3263150453567505},{"id":"https://openalex.org/C118552586","wikidata":"https://www.wikidata.org/wiki/Q7867","display_name":"Psychiatry","level":1,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/civemsa45640.2019.9071630","is_oa":false,"landing_page_url":"https://doi.org/10.1109/civemsa45640.2019.9071630","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W1536929369","https://openalex.org/W1591801644","https://openalex.org/W1601667204","https://openalex.org/W1611644280","https://openalex.org/W1815076433","https://openalex.org/W1899504021","https://openalex.org/W1968187292","https://openalex.org/W1970242638","https://openalex.org/W1987883337","https://openalex.org/W1994855515","https://openalex.org/W2006837676","https://openalex.org/W2032808202","https://openalex.org/W2037477351","https://openalex.org/W2039133703","https://openalex.org/W2041741035","https://openalex.org/W2046087145","https://openalex.org/W2050399430","https://openalex.org/W2064675550","https://openalex.org/W2076063813","https://openalex.org/W2085254417","https://openalex.org/W2088794999","https://openalex.org/W2102103192","https://openalex.org/W2102819635","https://openalex.org/W2119223881","https://openalex.org/W2144383914","https://openalex.org/W2461423132","https://openalex.org/W2507528282","https://openalex.org/W2517617279","https://openalex.org/W2525487527","https://openalex.org/W2592929672","https://openalex.org/W2606473278","https://openalex.org/W2753485470","https://openalex.org/W2796314858","https://openalex.org/W2797669432","https://openalex.org/W2834889150","https://openalex.org/W2890820256","https://openalex.org/W2905803260","https://openalex.org/W2919115771","https://openalex.org/W2962699674","https://openalex.org/W3003166701","https://openalex.org/W3101787898","https://openalex.org/W4285719527","https://openalex.org/W6638545294","https://openalex.org/W6675233730","https://openalex.org/W6685893538"],"related_works":["https://openalex.org/W1977940006","https://openalex.org/W2887556756","https://openalex.org/W2947925238","https://openalex.org/W195417223","https://openalex.org/W1513407214","https://openalex.org/W1984377984","https://openalex.org/W1961545574","https://openalex.org/W2510077457","https://openalex.org/W3045772920","https://openalex.org/W2047816336"],"abstract_inverted_index":{"Classifying":[0],"the":[1,13,17,38,94,136,141,154,166],"electrocorticogram":[2],"(ECoG)":[3],"signals":[4,48],"based":[5],"on":[6,146,165],"motor":[7],"imagery":[8],"(MI)":[9],"is":[10],"one":[11],"of":[12,16,35,40,117,144],"important":[14],"issues":[15],"BCI":[18,182],"systems.":[19],"Deep":[20],"learning":[21,59,71],"approaches":[22,179],"have":[23],"been":[24],"most":[25],"popularly":[26],"applied":[27],"to":[28,61,108],"learn":[29,109],"representations":[30,72],"and":[31,75,83],"classify":[32],"different":[33,160],"types":[34],"data.":[36],"However,":[37],"number":[39],"studies":[41],"that":[42,153],"modeling":[43],"cognitive":[44],"events":[45],"from":[46,73,124],"ECoG":[47,74,91,118,149],"are":[49],"very":[50],"limited.":[51],"In":[52],"this":[53],"paper,":[54],"we":[55,88,102],"propose":[56],"a":[57],"deep":[58],"method":[60,156],"use":[62],"long":[63],"short-term":[64],"memory":[65],"(LSTM)":[66],"recurrent":[67],"neural":[68,106],"networks":[69],"for":[70,79],"gradient":[76],"boosting":[77],"(GB)":[78],"classifying":[80],"MI":[81,161,167],"ECoG,":[82],"demonstrate":[84,152],"its":[85],"advantages.":[86],"First,":[87],"transform":[89],"multichannel":[90],"time-series":[92],"into":[93],"LSTM-GB":[95],"model":[96],"including":[97],"sequential":[98],"information.":[99],"After":[100],"that,":[101],"train":[103],"an":[104],"LSTM":[105,125,132],"network":[107],"robust":[110],"spatial-temporal":[111],"representations.":[112],"The":[113,131],"subtle":[114],"temporal":[115],"dependencies":[116],"data":[119],"streams":[120],"can":[121,139,157],"be":[122],"extracted":[123],"with":[126,135],"unique":[127],"information":[128],"processing":[129],"mechanism.":[130],"features":[133],"coupled":[134],"GB":[137],"classifier":[138],"yield":[140],"satisfactory":[142],"accuracy":[143,175],"100%":[145],"publicly":[147],"available":[148],"dataset.":[150],"Experiments":[151],"proposed":[155],"effectively":[158],"recognize":[159],"tasks.":[162],"Empirical":[163],"evaluation":[164],"classification":[168,174],"tasks":[169],"demonstrates":[170],"significant":[171],"improvements":[172],"in":[173,180],"over":[176],"current":[177],"state-of-the-art":[178],"MI-based":[181],"field.":[183]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
