{"id":"https://openalex.org/W4309308052","doi":"https://doi.org/10.3389/fncom.2022.1016516","title":"Sparse measures with swarm-based pliable hidden Markov model and deep learning for EEG classification","display_name":"Sparse measures with swarm-based pliable hidden Markov model and deep learning for EEG classification","publication_year":2022,"publication_date":"2022-11-16","ids":{"openalex":"https://openalex.org/W4309308052","doi":"https://doi.org/10.3389/fncom.2022.1016516","pmid":"https://pubmed.ncbi.nlm.nih.gov/36465961"},"language":"en","primary_location":{"id":"doi:10.3389/fncom.2022.1016516","is_oa":true,"landing_page_url":"https://doi.org/10.3389/fncom.2022.1016516","pdf_url":"https://www.frontiersin.org/articles/10.3389/fncom.2022.1016516/pdf","source":{"id":"https://openalex.org/S19778766","display_name":"Frontiers in Computational Neuroscience","issn_l":"1662-5188","issn":["1662-5188"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Computational Neuroscience","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.frontiersin.org/articles/10.3389/fncom.2022.1016516/pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102743010","display_name":"Sunil Kumar Prabhakar","orcid":"https://orcid.org/0000-0003-4019-2345"},"institutions":[{"id":"https://openalex.org/I146824383","display_name":"Hallym University","ror":"https://ror.org/03sbhge02","country_code":"KR","type":"education","lineage":["https://openalex.org/I146824383"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Sunil Kumar Prabhakar","raw_affiliation_strings":["Department of Artificial Intelligence Convergence, Hallym University, Chuncheon, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Artificial Intelligence Convergence, Hallym University, Chuncheon, South Korea","institution_ids":["https://openalex.org/I146824383"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024299572","display_name":"Young-Gi Ju","orcid":"https://orcid.org/0000-0003-4104-6243"},"institutions":[{"id":"https://openalex.org/I146824383","display_name":"Hallym University","ror":"https://ror.org/03sbhge02","country_code":"KR","type":"education","lineage":["https://openalex.org/I146824383"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Young-Gi Ju","raw_affiliation_strings":["Department of Artificial Intelligence Convergence, Hallym University, Chuncheon, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Artificial Intelligence Convergence, Hallym University, Chuncheon, South Korea","institution_ids":["https://openalex.org/I146824383"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022354867","display_name":"Harikumar Rajaguru","orcid":"https://orcid.org/0000-0002-2792-0945"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Harikumar Rajaguru","raw_affiliation_strings":["Department of Electronics and Communication Engineering, Bannari Amman Institute of Technology, Sathyamangalam, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronics and Communication Engineering, Bannari Amman Institute of Technology, Sathyamangalam, India","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5027428193","display_name":"Dong-Ok Won","orcid":"https://orcid.org/0000-0002-2839-6524"},"institutions":[{"id":"https://openalex.org/I146824383","display_name":"Hallym University","ror":"https://ror.org/03sbhge02","country_code":"KR","type":"education","lineage":["https://openalex.org/I146824383"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Dong-Ok Won","raw_affiliation_strings":["Department of Artificial Intelligence Convergence, Hallym University, Chuncheon, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Artificial Intelligence Convergence, Hallym University, Chuncheon, South Korea","institution_ids":["https://openalex.org/I146824383"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5027428193"],"corresponding_institution_ids":["https://openalex.org/I146824383"],"apc_list":{"value":3295,"currency":"USD","value_usd":3295},"apc_paid":{"value":3295,"currency":"USD","value_usd":3295},"fwci":0.31,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.50748789,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"16","issue":null,"first_page":"1016516","last_page":"1016516"},"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11021","display_name":"ECG Monitoring and Analysis","score":0.9934999942779541,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7333536148071289},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.7316291332244873},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7137609720230103},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7039628028869629},{"id":"https://openalex.org/keywords/sparse-approximation","display_name":"Sparse approximation","score":0.5236675143241882},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.49908947944641113},{"id":"https://openalex.org/keywords/particle-swarm-optimization","display_name":"Particle swarm optimization","score":0.483960896730423},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4369199872016907},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4315299093723297},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4224355220794678}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7333536148071289},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.7316291332244873},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7137609720230103},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7039628028869629},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.5236675143241882},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.49908947944641113},{"id":"https://openalex.org/C85617194","wikidata":"https://www.wikidata.org/wiki/Q2072794","display_name":"Particle swarm optimization","level":2,"score":0.483960896730423},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4369199872016907},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4315299093723297},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4224355220794678},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.3389/fncom.2022.1016516","is_oa":true,"landing_page_url":"https://doi.org/10.3389/fncom.2022.1016516","pdf_url":"https://www.frontiersin.org/articles/10.3389/fncom.2022.1016516/pdf","source":{"id":"https://openalex.org/S19778766","display_name":"Frontiers in Computational Neuroscience","issn_l":"1662-5188","issn":["1662-5188"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Computational Neuroscience","raw_type":"journal-article"},{"id":"pmid:36465961","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/36465961","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in computational neuroscience","raw_type":null},{"id":"pmh:oai:doaj.org/article:4eabba011937486583c3d55a5a5723c3","is_oa":true,"landing_page_url":"https://doaj.org/article/4eabba011937486583c3d55a5a5723c3","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Frontiers in Computational Neuroscience, Vol 16 (2022)","raw_type":"article"},{"id":"pmh:oai:pubmedcentral.nih.gov:9709410","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/9709410","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Front Comput Neurosci","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3389/fncom.2022.1016516","is_oa":true,"landing_page_url":"https://doi.org/10.3389/fncom.2022.1016516","pdf_url":"https://www.frontiersin.org/articles/10.3389/fncom.2022.1016516/pdf","source":{"id":"https://openalex.org/S19778766","display_name":"Frontiers in Computational Neuroscience","issn_l":"1662-5188","issn":["1662-5188"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Computational Neuroscience","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1048892396","display_name":null,"funder_award_id":"2022 R1A5A8019303","funder_id":"https://openalex.org/F4320328359","funder_display_name":"Ministry of Science and ICT, South Korea"},{"id":"https://openalex.org/G2156757918","display_name":null,"funder_award_id":"2017-0-00451","funder_id":"https://openalex.org/F4320335489","funder_display_name":"Institute for Information and Communications Technology Promotion"},{"id":"https://openalex.org/G4318034154","display_name":null,"funder_award_id":"2022R1A5A8019303","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"},{"id":"https://openalex.org/G5740704912","display_name":null,"funder_award_id":"2017-0-00451","funder_id":"https://openalex.org/F4320328359","funder_display_name":"Ministry of Science and ICT, South Korea"}],"funders":[{"id":"https://openalex.org/F4320318847","display_name":"Korea Medical Device Development Fund","ror":null},{"id":"https://openalex.org/F4320320671","display_name":"National Research Foundation","ror":"https://ror.org/05s0g1g46"},{"id":"https://openalex.org/F4320321681","display_name":"Ministry of Trade, Industry and Energy","ror":"https://ror.org/008nkqk13"},{"id":"https://openalex.org/F4320322014","display_name":"Ministry of Food and Drug Safety","ror":"https://ror.org/01f7dp456"},{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"},{"id":"https://openalex.org/F4320324891","display_name":"Iran Telecommunication Research Center","ror":"https://ror.org/01a3g2z22"},{"id":"https://openalex.org/F4320328359","display_name":"Ministry of Science and ICT, South Korea","ror":"https://ror.org/01wpjm123"},{"id":"https://openalex.org/F4320335489","display_name":"Institute for Information and Communications Technology Promotion","ror":"https://ror.org/01g0hqq23"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4309308052.pdf","grobid_xml":"https://content.openalex.org/works/W4309308052.grobid-xml"},"referenced_works_count":36,"referenced_works":["https://openalex.org/W1544328127","https://openalex.org/W1589606770","https://openalex.org/W2004518996","https://openalex.org/W2053744708","https://openalex.org/W2080195234","https://openalex.org/W2081377128","https://openalex.org/W2173226459","https://openalex.org/W2290883490","https://openalex.org/W2416750880","https://openalex.org/W2518936540","https://openalex.org/W2521265470","https://openalex.org/W2742638082","https://openalex.org/W2744480067","https://openalex.org/W2775399334","https://openalex.org/W2793694534","https://openalex.org/W2804035092","https://openalex.org/W2866637366","https://openalex.org/W2885516027","https://openalex.org/W2912885887","https://openalex.org/W2921691756","https://openalex.org/W2942361437","https://openalex.org/W2945968413","https://openalex.org/W2955989361","https://openalex.org/W2963568316","https://openalex.org/W2964267916","https://openalex.org/W3007994488","https://openalex.org/W3015929423","https://openalex.org/W3035302273","https://openalex.org/W3035485822","https://openalex.org/W3097678189","https://openalex.org/W3107281365","https://openalex.org/W3108524749","https://openalex.org/W3119640139","https://openalex.org/W3130743277","https://openalex.org/W3131943692","https://openalex.org/W4281647854"],"related_works":["https://openalex.org/W2053269318","https://openalex.org/W2364370872","https://openalex.org/W2025614924","https://openalex.org/W2294335174","https://openalex.org/W3145575561","https://openalex.org/W2097963413","https://openalex.org/W2995886640","https://openalex.org/W1591475660","https://openalex.org/W2001275470","https://openalex.org/W2164162849"],"abstract_inverted_index":{"In":[0,69],"comparison":[1],"to":[2,55,120,174],"other":[3],"biomedical":[4],"signals,":[5],"electroencephalography":[6],"(EEG)":[7],"signals":[8,89],"are":[9,128,172],"quite":[10],"complex":[11],"in":[12,30],"nature,":[13],"so":[14],"it":[15,168],"requires":[16],"a":[17,57,72,92,152,188,240],"versatile":[18,61],"model":[19,75],"for":[20,63,86,113],"feature":[21,42],"extraction":[22,43],"and":[23,44,60,67,90,140,169,207,210,230],"classification.":[24,68,115],"The":[25,48,116,199],"structural":[26],"information":[27],"that":[28,213],"prevails":[29],"the":[31,78,87,114,122,126,147,157,170,175,182,185,222],"originally":[32],"featured":[33],"matrix":[34],"is":[35,54,83,111,192,201,217,228,234,246],"usually":[36],"lost":[37],"when":[38,214,231],"dealing":[39],"with":[40,77,101,156,167,219,236],"standard":[41,176],"conventional":[45],"classification":[46,224,242],"techniques.":[47],"main":[49],"intention":[50],"of":[51,80,95,125,159,184,226,244],"this":[52,70],"work":[53],"propose":[56],"very":[58],"novel":[59,93],"approach":[62],"EEG":[64,88,197],"signal":[65],"modeling":[66],"work,":[71],"sparse":[73,98,215,232],"representation":[74,99,216,233],"along":[76],"analysis":[79,191,209],"sparseness":[81],"measures":[82,100],"done":[84,193],"initially":[85],"then":[91],"convergence":[94],"utilizing":[96],"these":[97],"Swarm":[102,130],"Intelligence":[103],"(SI)":[104],"techniques":[105,118],"based":[106],"Hidden":[107],"Markov":[108],"Model":[109],"(HMM)":[110],"utilized":[112,119],"SI":[117],"compute":[121],"hidden":[123],"states":[124],"HMM":[127,148,238],"Particle":[129],"Optimization":[131,137],"(PSO),":[132],"Differential":[133],"Evolution":[134],"(DE),":[135],"Whale":[136],"Algorithm":[138,143],"(WOA),":[139],"Backtracking":[141],"Search":[142],"(BSA),":[144],"thereby":[145],"making":[146],"more":[149],"pliable.":[150],"Later,":[151],"deep":[153,220],"learning":[154],"methodology":[155],"help":[158],"Convolutional":[160],"Neural":[161],"Network":[162],"(CNN)":[163],"was":[164],"also":[165],"developed":[166],"results":[171,211],"compared":[173],"pattern":[177],"recognition":[178],"classifiers.":[179],"To":[180],"validate":[181],"efficacy":[183],"proposed":[186],"methodology,":[187],"comprehensive":[189],"experimental":[190],"over":[194],"publicly":[195],"available":[196],"datasets.":[198],"method":[200],"supported":[202],"by":[203],"strong":[204],"statistical":[205],"tests":[206],"theoretical":[208],"show":[212],"implemented":[218,235],"learning,":[221],"highest":[223],"accuracy":[225,243],"98.94%":[227],"obtained":[229],"SI-based":[237],"method,":[239],"high":[241],"95.70%":[245],"obtained.":[247]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1}],"updated_date":"2026-08-05T07:39:15.569665","created_date":"2025-10-10T00:00:00"}