{"id":"https://openalex.org/W7127437476","doi":"https://doi.org/10.1016/j.compeleceng.2026.110990","title":"A novel EEG motor imagery classification model using feature fusion of temporal convolution and attention","display_name":"A novel EEG motor imagery classification model using feature fusion of temporal convolution and attention","publication_year":2026,"publication_date":"2026-02-03","ids":{"openalex":"https://openalex.org/W7127437476","doi":"https://doi.org/10.1016/j.compeleceng.2026.110990"},"language":"en","primary_location":{"id":"doi:10.1016/j.compeleceng.2026.110990","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.compeleceng.2026.110990","pdf_url":null,"source":{"id":"https://openalex.org/S121340289","display_name":"Computers & Electrical Engineering","issn_l":"0045-7906","issn":["0045-7906","1879-0755"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers and Electrical Engineering","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1016/j.compeleceng.2026.110990","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5098940808","display_name":"Mohammad Bdaqli","orcid":"https://orcid.org/0000-0002-3175-4708"},"institutions":[{"id":"https://openalex.org/I41832843","display_name":"University of Tabriz","ror":"https://ror.org/01papkj44","country_code":"IR","type":"education","lineage":["https://openalex.org/I41832843"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Mohammad Bdaqli","raw_affiliation_strings":["Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran"],"raw_orcid":"https://orcid.org/0000-0002-3175-4708","affiliations":[{"raw_affiliation_string":"Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran","institution_ids":["https://openalex.org/I41832843"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050501500","display_name":"Saeed Meshgini","orcid":"https://orcid.org/0000-0001-5023-0961"},"institutions":[{"id":"https://openalex.org/I41832843","display_name":"University of Tabriz","ror":"https://ror.org/01papkj44","country_code":"IR","type":"education","lineage":["https://openalex.org/I41832843"]}],"countries":["IR"],"is_corresponding":true,"raw_author_name":"Saeed Meshgini","raw_affiliation_strings":["Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran"],"raw_orcid":"https://orcid.org/0000-0001-5023-0961","affiliations":[{"raw_affiliation_string":"Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran","institution_ids":["https://openalex.org/I41832843"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5028833219","display_name":"Reza Afrouzian","orcid":null},"institutions":[{"id":"https://openalex.org/I41832843","display_name":"University of Tabriz","ror":"https://ror.org/01papkj44","country_code":"IR","type":"education","lineage":["https://openalex.org/I41832843"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Reza Afrouzian","raw_affiliation_strings":["Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran","institution_ids":["https://openalex.org/I41832843"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5050501500"],"corresponding_institution_ids":["https://openalex.org/I41832843"],"apc_list":{"value":3100,"currency":"USD","value_usd":3100},"apc_paid":{"value":3100,"currency":"USD","value_usd":3100},"fwci":10.1171,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.95632194,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"132","issue":null,"first_page":"110990","last_page":"110990"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9980000257492065,"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":0.9980000257492065,"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/T10977","display_name":"Optical Imaging and Spectroscopy Techniques","score":9.999999747378752e-05,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11707","display_name":"Gaze Tracking and Assistive Technology","score":9.999999747378752e-05,"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/motor-imagery","display_name":"Motor imagery","score":0.789900004863739},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.7544000148773193},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6642000079154968},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5978000164031982},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.572700023651123},{"id":"https://openalex.org/keywords/electroencephalography","display_name":"Electroencephalography","score":0.5699999928474426},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5521000027656555},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5271999835968018},{"id":"https://openalex.org/keywords/brain\u2013computer-interface","display_name":"Brain\u2013computer interface","score":0.5231999754905701}],"concepts":[{"id":"https://openalex.org/C54808283","wikidata":"https://www.wikidata.org/wiki/Q6918191","display_name":"Motor imagery","level":4,"score":0.789900004863739},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7730000019073486},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7544000148773193},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7488999962806702},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6642000079154968},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5978000164031982},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.572700023651123},{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.5699999928474426},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5521000027656555},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5271999835968018},{"id":"https://openalex.org/C173201364","wikidata":"https://www.wikidata.org/wiki/Q897410","display_name":"Brain\u2013computer interface","level":3,"score":0.5231999754905701},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.42160001397132874},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4088999927043915},{"id":"https://openalex.org/C113843644","wikidata":"https://www.wikidata.org/wiki/Q901882","display_name":"Interface (matter)","level":4,"score":0.3944000005722046},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3549000024795532},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3190999925136566},{"id":"https://openalex.org/C173414695","wikidata":"https://www.wikidata.org/wiki/Q5510276","display_name":"Fusion mechanism","level":4,"score":0.3100999891757965},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.28929999470710754},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.27390000224113464},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.27149999141693115},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.2700999975204468},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.267300009727478},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.2669000029563904},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.25870001316070557},{"id":"https://openalex.org/C104122410","wikidata":"https://www.wikidata.org/wiki/Q1416406","display_name":"Network model","level":2,"score":0.25839999318122864}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1016/j.compeleceng.2026.110990","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.compeleceng.2026.110990","pdf_url":null,"source":{"id":"https://openalex.org/S121340289","display_name":"Computers & Electrical Engineering","issn_l":"0045-7906","issn":["0045-7906","1879-0755"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers and Electrical Engineering","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1016/j.compeleceng.2026.110990","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.compeleceng.2026.110990","pdf_url":null,"source":{"id":"https://openalex.org/S121340289","display_name":"Computers & Electrical Engineering","issn_l":"0045-7906","issn":["0045-7906","1879-0755"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers and Electrical Engineering","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.7809336185455322,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1975717934","https://openalex.org/W2087704839","https://openalex.org/W2559463885","https://openalex.org/W2741907166","https://openalex.org/W2752782242","https://openalex.org/W2896920298","https://openalex.org/W2919115771","https://openalex.org/W2951758041","https://openalex.org/W3009232581","https://openalex.org/W3037047196","https://openalex.org/W3160936371","https://openalex.org/W3171454251","https://openalex.org/W3192648431","https://openalex.org/W4223895421","https://openalex.org/W4286587846","https://openalex.org/W4300942166","https://openalex.org/W4322730835","https://openalex.org/W4323317194","https://openalex.org/W4381436595","https://openalex.org/W4382725844","https://openalex.org/W4400512529","https://openalex.org/W4404112119"],"related_works":[],"abstract_inverted_index":{"Motor":[0],"imagery":[1,96,215],"classification":[2,111,181],"using":[3,167],"electroencephalography":[4],"(EEG)":[5],"signals":[6,37,97],"is":[7],"a":[8,53,69],"fundamental":[9],"component":[10],"of":[11,35,63,172],"Brain-Computer":[12],"Interface":[13],"(BCI)":[14],"systems.":[15],"It":[16],"enables":[17],"individuals":[18],"with":[19],"physical":[20],"disabilities":[21],"to":[22,88,109],"control":[23],"robotic":[24],"limbs":[25],"and":[26,78,80,113,127,151,162,177,194,200],"perform":[27],"various":[28],"movements.":[29],"However,":[30],"the":[31,86,122,128,133,138,146,169,184,198,208],"inherently":[32],"noisy":[33],"nature":[34],"EEG":[36],"poses":[38],"significant":[39],"challenges":[40],"for":[41,92,157,175,197],"their":[42],"effective":[43],"utilization":[44],"in":[45],"this":[46,49],"domain.":[47],"In":[48],"study,":[50],"we":[51],"propose":[52],"novel":[54,105],"end-to-end":[55],"deep":[56,65],"learning":[57,66],"model":[58,87,142,210],"based":[59],"on":[60,145,183],"feature":[61,106],"fusion":[62,107],"multiple":[64],"blocks,":[67],"including":[68],"Convolutional":[70,75],"Neural":[71],"Network":[72,76],"(CNN),":[73],"Temporal":[74],"(TCN),":[77],"Squeeze":[79],"Excitation":[81],"(SE)":[82],"attention":[83,130],"mechanism,":[84],"enabling":[85],"learn":[89],"discriminative":[90],"features":[91,136],"classifying":[93],"raw":[94],"motor":[95,214],"without":[98],"any":[99],"preprocessing.":[100],"The":[101,116,141,179],"proposed":[102,209],"architecture":[103],"employs":[104],"strategies":[108],"maximize":[110],"performance":[112],"computational":[114],"efficiency.":[115],"CNN":[117,139],"extracts":[118],"initial":[119],"spatial":[120],"features,":[121],"TCN":[123],"captures":[124],"temporal":[125],"dependencies,":[126],"SE":[129],"mechanism":[131],"emphasizes":[132],"most":[134],"informative":[135],"from":[137],"output.":[140],"was":[143,155],"evaluated":[144],"BCI":[147],"Competition":[148],"IV":[149],"2a":[150,199],"2b":[152,201],"datasets.":[153],"Training":[154],"conducted":[156],"500":[158],"epochs":[159,164],"(2a":[160],"dataset)":[161],"200":[163],"(2b":[165],"dataset),":[166],"only":[168],"first":[170],"session":[171],"each":[173],"subject":[174],"training":[176],"validation.":[178],"average":[180],"accuracies":[182],"completely":[185],"isolated":[186],"test":[187],"sets":[188],"(second":[189],"session)":[190],"were":[191],"78.12":[192],"%":[193,196],"85.72":[195],"datasets,":[202],"respectively.":[203],"These":[204],"results":[205],"demonstrate":[206],"that":[207],"effectively":[211],"classifies":[212],"multi-class":[213],"signals.":[216]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-02-07T06:11:34.122080","created_date":"2026-02-04T00:00:00"}
