{"id":"https://openalex.org/W3139962588","doi":"https://doi.org/10.1145/3444884.3444889","title":"Using Hidden Markov Model for Identification Based on EEG Signals","display_name":"Using Hidden Markov Model for Identification Based on EEG Signals","publication_year":2020,"publication_date":"2020-11-06","ids":{"openalex":"https://openalex.org/W3139962588","doi":"https://doi.org/10.1145/3444884.3444889","mag":"3139962588"},"language":"en","primary_location":{"id":"doi:10.1145/3444884.3444889","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3444884.3444889","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 7th International Conference on Biomedical and Bioinformatics Engineering","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/A5040629419","display_name":"Wenxiao Zhong","orcid":"https://orcid.org/0000-0001-9068-428X"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Wenxiao Zhong","raw_affiliation_strings":["Tianjin University, China"],"affiliations":[{"raw_affiliation_string":"Tianjin University, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013041561","display_name":"Xingwei An","orcid":"https://orcid.org/0000-0002-5417-508X"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xingwei An","raw_affiliation_strings":["Tianjin University"],"affiliations":[{"raw_affiliation_string":"Tianjin University","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027735819","display_name":"Yang Di","orcid":"https://orcid.org/0000-0002-8152-7351"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yang Di","raw_affiliation_strings":["Tianjin University"],"affiliations":[{"raw_affiliation_string":"Tianjin University","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100427814","display_name":"Lixin Zhang","orcid":"https://orcid.org/0000-0002-4789-238X"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lixin Zhang","raw_affiliation_strings":["Tianjin University"],"affiliations":[{"raw_affiliation_string":"Tianjin University","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5011769657","display_name":"Dong Ming","orcid":"https://orcid.org/0000-0002-8192-2538"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dong Ming","raw_affiliation_strings":["Tianjin University"],"affiliations":[{"raw_affiliation_string":"Tianjin University","institution_ids":["https://openalex.org/I162868743"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5040629419"],"corresponding_institution_ids":["https://openalex.org/I162868743"],"apc_list":null,"apc_paid":null,"fwci":0.1115,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.45514119,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"22","last_page":"26"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9839000105857849,"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":0.9717000126838684,"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/hidden-markov-model","display_name":"Hidden Markov model","score":0.7926950454711914},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.7554693222045898},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7224547266960144},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6595666408538818},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.6586197018623352},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6469920873641968},{"id":"https://openalex.org/keywords/mel-frequency-cepstrum","display_name":"Mel-frequency cepstrum","score":0.6286641359329224},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.584605872631073},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5607028007507324},{"id":"https://openalex.org/keywords/electroencephalography","display_name":"Electroencephalography","score":0.4891352951526642},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.4540272057056427},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.43959981203079224},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4128798842430115},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.3695494532585144},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3555895686149597},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.15066838264465332},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.14525890350341797},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.11071154475212097}],"concepts":[{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.7926950454711914},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7554693222045898},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7224547266960144},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6595666408538818},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.6586197018623352},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6469920873641968},{"id":"https://openalex.org/C151989614","wikidata":"https://www.wikidata.org/wiki/Q440370","display_name":"Mel-frequency cepstrum","level":3,"score":0.6286641359329224},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.584605872631073},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5607028007507324},{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.4891352951526642},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.4540272057056427},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.43959981203079224},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4128798842430115},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3695494532585144},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3555895686149597},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.15066838264465332},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.14525890350341797},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.11071154475212097},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"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/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3444884.3444889","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3444884.3444889","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 7th International Conference on Biomedical and Bioinformatics Engineering","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":11,"referenced_works":["https://openalex.org/W2011584637","https://openalex.org/W2134577338","https://openalex.org/W2181315643","https://openalex.org/W2291176881","https://openalex.org/W2768708026","https://openalex.org/W2790837801","https://openalex.org/W2883827556","https://openalex.org/W2916700883","https://openalex.org/W2933506701","https://openalex.org/W2940492946","https://openalex.org/W2956072276"],"related_works":["https://openalex.org/W2053269318","https://openalex.org/W2546021431","https://openalex.org/W2581127593","https://openalex.org/W4317383455","https://openalex.org/W2548511587","https://openalex.org/W4293232884","https://openalex.org/W2422472940","https://openalex.org/W2019475500","https://openalex.org/W2548162870","https://openalex.org/W2144773493"],"abstract_inverted_index":{"The":[0],"researches":[1],"on":[2,7,29,188],"individual":[3,34],"identification":[4,22,35,191],"approaches":[5],"based":[6],"EEG":[8,37],"signals":[9],"draw":[10],"lots":[11],"of":[12,18,33,40,42,54,84,90,125,131,161,168],"attention":[13],"in":[14,49,189],"recent":[15],"years.":[16],"Few":[17],"them":[19,55],"got":[20],"time-robust":[21,123],"performance.":[23],"In":[24],"this":[25,50],"study,":[26],"we":[27,128],"focused":[28],"the":[30,62,81,122,139,148,159,165],"time":[31,63,82,174],"robustness":[32],"using":[36],"under":[38,158],"conditions":[39,160],"resting-state":[41],"eye":[43],"open/closed":[44],"(REO/REC).":[45],"Ten":[46],"subjects":[47,170],"participated":[48],"study":[51,182],"and":[52,80,96,111,138,163,175],"each":[53,78,85],"conducted":[56],"three":[57,75,132],"independent":[58],"runs":[59,67,133],"experiment,":[60],"with":[61],"intervals":[64],"between":[65],"adjacent":[66],"were":[68,74,101,116],"at":[69],"least":[70],"two":[71,141],"weeks.":[72],"There":[73],"sessions":[76],"within":[77],"run,":[79],"duration":[83],"session":[86],"is":[87,152,155],"150":[88],"seconds":[89],"REO/REC.":[91],"Two":[92],"features,":[93],"auto-regressive":[94],"(AR)":[95],"Mel-frequency":[97],"cepstrum":[98],"coefficients":[99],"(MFCC)":[100],"calculated":[102],"as":[103,118,135,142],"identity":[104,166],"features.":[105],"Then":[106],"Support":[107],"vector":[108],"machines":[109],"(SVM)":[110],"Hidden":[112],"Markov":[113],"model":[114],"(HMM)":[115],"used":[117,129,178],"classifiers.":[119],"To":[120],"access":[121],"performance":[124],"our":[126],"methods,":[127],"one":[130],"data":[134],"test":[136],"set":[137],"other":[140],"training":[143],"set.":[144],"Results":[145],"show":[146],"that":[147,157],"best":[149],"classification":[150],"accuracy":[151],"80%.":[153],"It":[154],"believed":[156],"REC":[162],"REO,":[164],"features":[167],"most":[169],"are":[171],"robust":[172],"across":[173],"can":[176],"be":[177],"for":[179],"identification.":[180],"This":[181],"will":[183],"have":[184],"an":[185],"important":[186],"impact":[187],"EEG-based":[190],"system.":[192]},"counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}