{"id":"https://openalex.org/W7125929404","doi":"https://doi.org/10.1109/smc58881.2025.11343600","title":"Time-Frequency-Spatial Neural Architecture for Decoding Visual Signals from Macaque ECoG","display_name":"Time-Frequency-Spatial Neural Architecture for Decoding Visual Signals from Macaque ECoG","publication_year":2025,"publication_date":"2025-10-05","ids":{"openalex":"https://openalex.org/W7125929404","doi":"https://doi.org/10.1109/smc58881.2025.11343600"},"language":null,"primary_location":{"id":"doi:10.1109/smc58881.2025.11343600","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc58881.2025.11343600","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","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/A5028374998","display_name":"Changqing Ji","orcid":null},"institutions":[{"id":"https://openalex.org/I201537933","display_name":"Tohoku University","ror":"https://ror.org/01dq60k83","country_code":"JP","type":"education","lineage":["https://openalex.org/I201537933"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Changqing Ji","raw_affiliation_strings":["TOHOKU University,Graduate School of Information Sciences,Japan"],"affiliations":[{"raw_affiliation_string":"TOHOKU University,Graduate School of Information Sciences,Japan","institution_ids":["https://openalex.org/I201537933"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055503286","display_name":"Keisuke Kawasaki","orcid":"https://orcid.org/0000-0002-3378-816X"},"institutions":[{"id":"https://openalex.org/I71395657","display_name":"Niigata University","ror":"https://ror.org/04ww21r56","country_code":"JP","type":"education","lineage":["https://openalex.org/I71395657"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Keisuke Kawasaki","raw_affiliation_strings":["NIIGATA University,Graduate School of Medical and Dental Sciences,Niigata,Japan"],"affiliations":[{"raw_affiliation_string":"NIIGATA University,Graduate School of Medical and Dental Sciences,Niigata,Japan","institution_ids":["https://openalex.org/I71395657"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012905432","display_name":"Isao Hasegawa","orcid":null},"institutions":[{"id":"https://openalex.org/I71395657","display_name":"Niigata University","ror":"https://ror.org/04ww21r56","country_code":"JP","type":"education","lineage":["https://openalex.org/I71395657"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Isao Hasegawa","raw_affiliation_strings":["NIIGATA University,Graduate School of Medical and Dental Sciences,Niigata,Japan"],"affiliations":[{"raw_affiliation_string":"NIIGATA University,Graduate School of Medical and Dental Sciences,Niigata,Japan","institution_ids":["https://openalex.org/I71395657"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5108619681","display_name":"Takayuki Okatani","orcid":null},"institutions":[{"id":"https://openalex.org/I201537933","display_name":"Tohoku University","ror":"https://ror.org/01dq60k83","country_code":"JP","type":"education","lineage":["https://openalex.org/I201537933"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Takayuki Okatani","raw_affiliation_strings":["TOHOKU University,Graduate School of Information Sciences,Japan"],"affiliations":[{"raw_affiliation_string":"TOHOKU University,Graduate School of Information Sciences,Japan","institution_ids":["https://openalex.org/I201537933"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5028374998"],"corresponding_institution_ids":["https://openalex.org/I201537933"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.73760284,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3254","last_page":"3259"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11094","display_name":"Face Recognition and Perception","score":0.6136000156402588,"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/T11094","display_name":"Face Recognition and Perception","score":0.6136000156402588,"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/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.22470000386238098,"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/T10667","display_name":"Emotion and Mood Recognition","score":0.04729999974370003,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.6417999863624573},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6087999939918518},{"id":"https://openalex.org/keywords/macaque","display_name":"Macaque","score":0.5056999921798706},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.4921000003814697},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4708999991416931},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.46389999985694885},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.450300008058548},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.44190001487731934},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.4117000102996826}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7396000027656555},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6841999888420105},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.6417999863624573},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6087999939918518},{"id":"https://openalex.org/C2778950215","wikidata":"https://www.wikidata.org/wiki/Q177601","display_name":"Macaque","level":2,"score":0.5056999921798706},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.4921000003814697},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4708999991416931},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.46389999985694885},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.450300008058548},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.44190001487731934},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4117000102996826},{"id":"https://openalex.org/C40743351","wikidata":"https://www.wikidata.org/wiki/Q7002049","display_name":"Neural decoding","level":3,"score":0.3971000015735626},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3790000081062317},{"id":"https://openalex.org/C2779345533","wikidata":"https://www.wikidata.org/wiki/Q75785","display_name":"Visual cortex","level":2,"score":0.35100001096725464},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.3495999872684479},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.3481000065803528},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.33970001339912415},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.33719998598098755},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.32199999690055847},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.3212999999523163},{"id":"https://openalex.org/C2780117969","wikidata":"https://www.wikidata.org/wiki/Q3587624","display_name":"Electrocorticography","level":3,"score":0.31470000743865967},{"id":"https://openalex.org/C2778251979","wikidata":"https://www.wikidata.org/wiki/Q7936617","display_name":"Visual processing","level":3,"score":0.31150001287460327},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.30959999561309814},{"id":"https://openalex.org/C160086991","wikidata":"https://www.wikidata.org/wiki/Q5939193","display_name":"Human visual system model","level":3,"score":0.298799991607666},{"id":"https://openalex.org/C2780103172","wikidata":"https://www.wikidata.org/wiki/Q1309721","display_name":"Visual Objects","level":3,"score":0.2948000133037567},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.28790000081062317},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.2793000042438507},{"id":"https://openalex.org/C178253425","wikidata":"https://www.wikidata.org/wiki/Q162668","display_name":"Visual perception","level":3,"score":0.27129998803138733},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.26969999074935913}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/smc58881.2025.11343600","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc58881.2025.11343600","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W2035715639","https://openalex.org/W2126892270","https://openalex.org/W2149090293","https://openalex.org/W2293634267","https://openalex.org/W2418296670","https://openalex.org/W2559463885","https://openalex.org/W2799678602","https://openalex.org/W2918941196","https://openalex.org/W3013945861","https://openalex.org/W3015738170","https://openalex.org/W3080249333","https://openalex.org/W3095788890","https://openalex.org/W3124617164","https://openalex.org/W3203162515","https://openalex.org/W4220974497","https://openalex.org/W4280631076"],"related_works":[],"abstract_inverted_index":{"Understanding":[0],"how":[1],"visual":[2,29,146,161],"information":[3,144],"is":[4,10],"encoded":[5],"in":[6,112,164],"electrocorticography":[7],"(ECoG)":[8],"signals":[9,65],"essential":[11],"for":[12,27,145,153],"developing":[13],"accurate":[14],"and":[15,82,124,137],"interpretable":[16],"decoding":[17],"models.":[18],"In":[19],"this":[20],"study,":[21],"we":[22],"propose":[23],"two":[24],"novel":[25],"approaches":[26],"multi-class":[28],"classification":[30,113],"based":[31],"on":[32],"ECoG":[33,64],"data":[34],"recorded":[35],"from":[36],"the":[37,57,90,141,157,165],"inferior":[38],"temporal":[39],"cortex":[40],"of":[41,122,131,156],"macaque":[42],"monkeys.":[43],"The":[44,71],"first":[45],"model,":[46,73],"MST-ECoGNet,":[47],"combines":[48],"traditional":[49],"signal":[50],"processing":[51],"with":[52,77],"neural":[53,158],"networks":[54],"by":[55,119,128],"employing":[56],"Modified":[58],"Stockwell":[59],"Transform":[60],"(MST)":[61],"to":[62,88,106],"map":[63],"into":[66],"a":[67,78,84,107,120,151],"structured":[68],"time-frequency-spatial":[69],"domain.":[70],"second":[72],"BiBand-3DECoGNet,":[74],"replaces":[75],"MST":[76],"learnable":[79],"convolutional":[80],"module":[81],"utilizes":[83],"3D":[85],"spatial":[86,136],"encoder":[87],"exploit":[89],"electrode":[91],"array":[92],"structure.":[93],"Experimental":[94],"results":[95],"show":[96],"that":[97,135],"our":[98],"models":[99],"significantly":[100],"outperform":[101],"prior":[102],"work,":[103],"achieving":[104],"up":[105],"12.87":[108],"percentage":[109],"point":[110],"improvement":[111],"accuracy":[114],"while":[115],"reducing":[116],"model":[117],"size":[118],"factor":[121],"ten":[123],"increasing":[125],"training":[126],"speed":[127],"sixfold.":[129],"Analysis":[130],"feature":[132],"dimensions":[133],"reveals":[134],"low-frequency":[138],"components":[139],"carry":[140],"most":[142],"relevant":[143],"decoding.":[147],"These":[148],"findings":[149],"provide":[150],"foundation":[152],"further":[154],"exploration":[155],"mechanisms":[159],"underlying":[160],"object":[162],"representation":[163],"brain.":[166]},"counts_by_year":[],"updated_date":"2026-01-29T23:17:01.242718","created_date":"2026-01-29T00:00:00"}
