{"id":"https://openalex.org/W4399169036","doi":"https://doi.org/10.1109/access.2024.3407690","title":"Application of Deep Learning to Enhance Finger Movement Classification Accuracy From UHD-EEG Signals","display_name":"Application of Deep Learning to Enhance Finger Movement Classification Accuracy From UHD-EEG Signals","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4399169036","doi":"https://doi.org/10.1109/access.2024.3407690"},"language":"en","primary_location":{"id":"doi:10.1109/access.2024.3407690","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3407690","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10542102.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10542102.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5075380511","display_name":"\u00c1d\u00e1m Gyula Nemes","orcid":null},"institutions":[{"id":"https://openalex.org/I103356709","display_name":"Obuda University","ror":"https://ror.org/00ax71d21","country_code":"HU","type":"education","lineage":["https://openalex.org/I103356709"]}],"countries":["HU"],"is_corresponding":true,"raw_author_name":"\u00c1d\u00e1m Gyula Nemes","raw_affiliation_strings":["Applied Informatics and Applied Mathematics Doctoral School, Obuda University, Budapest, Hungary"],"affiliations":[{"raw_affiliation_string":"Applied Informatics and Applied Mathematics Doctoral School, Obuda University, Budapest, Hungary","institution_ids":["https://openalex.org/I103356709"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077451025","display_name":"Gy\u00f6rgy Eigner","orcid":"https://orcid.org/0000-0001-8038-2210"},"institutions":[{"id":"https://openalex.org/I103356709","display_name":"Obuda University","ror":"https://ror.org/00ax71d21","country_code":"HU","type":"education","lineage":["https://openalex.org/I103356709"]}],"countries":["HU"],"is_corresponding":false,"raw_author_name":"Gy\u00f6rgy Eigner","raw_affiliation_strings":["University Research and Innovation Center, Physiological Controls Research Center, Obuda University, Budapest, Hungary"],"affiliations":[{"raw_affiliation_string":"University Research and Innovation Center, Physiological Controls Research Center, Obuda University, Budapest, Hungary","institution_ids":["https://openalex.org/I103356709"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100739668","display_name":"Peng Shi","orcid":"https://orcid.org/0000-0001-8218-586X"},"institutions":[{"id":"https://openalex.org/I103356709","display_name":"Obuda University","ror":"https://ror.org/00ax71d21","country_code":"HU","type":"education","lineage":["https://openalex.org/I103356709"]}],"countries":["HU"],"is_corresponding":false,"raw_author_name":"Peng Shi","raw_affiliation_strings":["University Research and Innovation Center, Physiological Controls Research Center, Obuda University, Budapest, Hungary"],"affiliations":[{"raw_affiliation_string":"University Research and Innovation Center, Physiological Controls Research Center, Obuda University, Budapest, Hungary","institution_ids":["https://openalex.org/I103356709"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5075380511"],"corresponding_institution_ids":["https://openalex.org/I103356709"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":1.0285,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.74036641,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":"12","issue":null,"first_page":"139937","last_page":"139945"},"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/T11021","display_name":"ECG Monitoring and Analysis","score":0.9915000200271606,"subfield":{"id":"https://openalex.org/subfields/2705","display_name":"Cardiology and Cardiovascular Medicine"},"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/T10784","display_name":"Muscle activation and electromyography studies","score":0.9889000058174133,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8108268976211548},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.7315285801887512},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7254102826118469},{"id":"https://openalex.org/keywords/electroencephalography","display_name":"Electroencephalography","score":0.7119326591491699},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.6287093162536621},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5812134742736816},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5196048617362976},{"id":"https://openalex.org/keywords/binary-classification","display_name":"Binary classification","score":0.5194820165634155},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.47999662160873413},{"id":"https://openalex.org/keywords/brain\u2013computer-interface","display_name":"Brain\u2013computer interface","score":0.47825613617897034},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.44480782747268677},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.41814297437667847},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4131561815738678},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.3747998774051666},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3516443371772766}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8108268976211548},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.7315285801887512},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7254102826118469},{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.7119326591491699},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.6287093162536621},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5812134742736816},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5196048617362976},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.5194820165634155},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.47999662160873413},{"id":"https://openalex.org/C173201364","wikidata":"https://www.wikidata.org/wiki/Q897410","display_name":"Brain\u2013computer interface","level":3,"score":0.47825613617897034},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.44480782747268677},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.41814297437667847},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4131561815738678},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3747998774051666},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3516443371772766},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C118552586","wikidata":"https://www.wikidata.org/wiki/Q7867","display_name":"Psychiatry","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2024.3407690","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3407690","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10542102.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:6d8cd72dc00a4cd1b42c545a7e47bf68","is_oa":true,"landing_page_url":"https://doaj.org/article/6d8cd72dc00a4cd1b42c545a7e47bf68","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 12, Pp 139937-139945 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2024.3407690","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3407690","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10542102.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G576991101","display_name":null,"funder_award_id":"2019-1.3.1-KK","funder_id":"https://openalex.org/F4320336675","funder_display_name":"National Research, Development and Innovation Office"}],"funders":[{"id":"https://openalex.org/F4320336675","display_name":"National Research, Development and Innovation Office","ror":null}],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4399169036.pdf"},"referenced_works_count":43,"referenced_works":["https://openalex.org/W1206391664","https://openalex.org/W1973481739","https://openalex.org/W1988988635","https://openalex.org/W1994677652","https://openalex.org/W1995842976","https://openalex.org/W2039975619","https://openalex.org/W2074591889","https://openalex.org/W2094882416","https://openalex.org/W2099509424","https://openalex.org/W2116308679","https://openalex.org/W2123626849","https://openalex.org/W2128909182","https://openalex.org/W2142280324","https://openalex.org/W2152489932","https://openalex.org/W2313498279","https://openalex.org/W2505007294","https://openalex.org/W2559463885","https://openalex.org/W2780011893","https://openalex.org/W2791656050","https://openalex.org/W2888508477","https://openalex.org/W2910825437","https://openalex.org/W2915550230","https://openalex.org/W2949676527","https://openalex.org/W2965485674","https://openalex.org/W3003670190","https://openalex.org/W3008898666","https://openalex.org/W3009896248","https://openalex.org/W3035818007","https://openalex.org/W3102455230","https://openalex.org/W3102935907","https://openalex.org/W3111451368","https://openalex.org/W3152887310","https://openalex.org/W3178204380","https://openalex.org/W3183494245","https://openalex.org/W4205121227","https://openalex.org/W4226454579","https://openalex.org/W4239510810","https://openalex.org/W4306842916","https://openalex.org/W4388051593","https://openalex.org/W4389051866","https://openalex.org/W6627949721","https://openalex.org/W6635935089","https://openalex.org/W6782703875"],"related_works":["https://openalex.org/W3202969339","https://openalex.org/W4237513258","https://openalex.org/W2044053727","https://openalex.org/W1994410349","https://openalex.org/W3177028067","https://openalex.org/W1913385466","https://openalex.org/W2914170859","https://openalex.org/W2889342546","https://openalex.org/W2015048155","https://openalex.org/W2106231951"],"abstract_inverted_index":{"This":[0,115],"study":[1],"investigates":[2],"the":[3,15,76,81,88,103,111,122,131,147],"classification":[4,46,58],"of":[5,17,50,60,90,125,133,149],"Ultra-High-Density":[6],"Electroencephalography":[7],"(UHD-EEG)":[8],"signals":[9],"corresponding":[10],"to":[11,47,79,146],"finger":[12,42,91,126],"movements":[13],"through":[14],"application":[16],"machine":[18,135],"learning":[19,113,136],"techniques,":[20],"namely":[21],"Support":[22],"Vector":[23],"Machines":[24],"(SVM)":[25],"and":[26,93,108,152],"Multi-Layer":[27],"Perceptrons":[28],"(MLP).":[29],"We":[30],"analyzed":[31],"UHD-EEG":[32,140],"data":[33],"from":[34,75],"five":[35],"subjects":[36],"engaged":[37],"in":[38,110,138],"motor":[39],"tasks":[40],"involving":[41],"extensions,":[43],"applying":[44],"binary":[45],"each":[48],"pair":[49],"fingers.":[51],"The":[52],"MLP":[53,77],"models":[54,78],"achieved":[55],"an":[56],"average":[57],"accuracy":[59],"65.68%,":[61],"demonstrating":[62],"a":[63,143],"considerable":[64],"improvement":[65],"over":[66],"SVMs":[67],"(60.4%).":[68],"Further,":[69],"we":[70],"utilized":[71],"saliency":[72,99],"maps":[73,100],"generated":[74],"identify":[80],"periods":[82,107],"most":[83,104],"critical":[84],"for":[85],"classification,":[86],"uncovering":[87],"phases":[89],"flexion":[92],"relaxation":[94],"as":[95],"particularly":[96],"informative.":[97],"These":[98],"succesfully":[101],"visualized":[102],"important":[105],"time":[106],"channels":[109],"deep":[112],"predictions.":[114],"work":[116],"not":[117],"only":[118],"sheds":[119],"light":[120],"on":[121],"neural":[123,150],"mechanisms":[124],"movement":[127],"but":[128],"also":[129],"underscores":[130],"efficacy":[132],"advanced":[134],"methodologies":[137],"decoding":[139],"signals,":[141],"marking":[142],"substantial":[144],"contribution":[145],"field":[148],"engineering":[151],"rehabilitation":[153],"technology.":[154]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
