{"id":"https://openalex.org/W3007761137","doi":"https://doi.org/10.3233/ida-184388","title":"EEG-based tonic cold pain assessment using extreme learning machine","display_name":"EEG-based tonic cold pain assessment using extreme learning machine","publication_year":2020,"publication_date":"2020-02-25","ids":{"openalex":"https://openalex.org/W3007761137","doi":"https://doi.org/10.3233/ida-184388","mag":"3007761137"},"language":"en","primary_location":{"id":"doi:10.3233/ida-184388","is_oa":false,"landing_page_url":"https://doi.org/10.3233/ida-184388","pdf_url":null,"source":{"id":"https://openalex.org/S2498839158","display_name":"Intelligent Data Analysis","issn_l":"1088-467X","issn":["1088-467X","1571-4128"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Intelligent Data Analysis","raw_type":"journal-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/A5026352201","display_name":"Mingxin Yu","orcid":"https://orcid.org/0000-0001-8823-0852"},"institutions":[{"id":"https://openalex.org/I78675632","display_name":"Beijing Information Science & Technology University","ror":"https://ror.org/04xnqep60","country_code":"CN","type":"education","lineage":["https://openalex.org/I78675632"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mingxin Yu","raw_affiliation_strings":["Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science and Technology University, Beijing 100015, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science and Technology University, Beijing 100015, China","institution_ids":["https://openalex.org/I78675632"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100708034","display_name":"Hao Yan","orcid":"https://orcid.org/0000-0002-4255-9262"},"institutions":[{"id":"https://openalex.org/I78675632","display_name":"Beijing Information Science & Technology University","ror":"https://ror.org/04xnqep60","country_code":"CN","type":"education","lineage":["https://openalex.org/I78675632"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hao Yan","raw_affiliation_strings":["Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science and Technology University, Beijing 100015, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science and Technology University, Beijing 100015, China","institution_ids":["https://openalex.org/I78675632"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069921704","display_name":"Jing Han","orcid":"https://orcid.org/0000-0001-5776-6849"},"institutions":[{"id":"https://openalex.org/I4210119028","display_name":"Beijing Anzhen Hospital","ror":"https://ror.org/02h2j1586","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210119028"]},{"id":"https://openalex.org/I183519381","display_name":"Capital Medical University","ror":"https://ror.org/013xs5b60","country_code":"CN","type":"education","lineage":["https://openalex.org/I183519381"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jing Han","raw_affiliation_strings":["Emergency and Critical Care Center, Beijing Anzhen Hospital, Capital Medical University, Beijing 100029, China"],"affiliations":[{"raw_affiliation_string":"Emergency and Critical Care Center, Beijing Anzhen Hospital, Capital Medical University, Beijing 100029, China","institution_ids":["https://openalex.org/I4210119028","https://openalex.org/I183519381"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063169738","display_name":"Yingzi Lin","orcid":"https://orcid.org/0000-0001-7120-4359"},"institutions":[{"id":"https://openalex.org/I12912129","display_name":"Northeastern University","ror":"https://ror.org/04t5xt781","country_code":"US","type":"education","lineage":["https://openalex.org/I12912129"]},{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN","US"],"is_corresponding":true,"raw_author_name":"Yingzi Lin","raw_affiliation_strings":["Intelligent Human-Machine Systems Lab, College of Engineering, Northeastern University, Boston, MA 02115, USA","School of Life Sciences, Beijing Institute of Technology, Beijing 100086, China"],"affiliations":[{"raw_affiliation_string":"Intelligent Human-Machine Systems Lab, College of Engineering, Northeastern University, Boston, MA 02115, USA","institution_ids":["https://openalex.org/I12912129"]},{"raw_affiliation_string":"School of Life Sciences, Beijing Institute of Technology, Beijing 100086, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100785538","display_name":"Lianqing Zhu","orcid":"https://orcid.org/0000-0003-0728-2349"},"institutions":[{"id":"https://openalex.org/I78675632","display_name":"Beijing Information Science & Technology University","ror":"https://ror.org/04xnqep60","country_code":"CN","type":"education","lineage":["https://openalex.org/I78675632"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Lianqing Zhu","raw_affiliation_strings":["Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science and Technology University, Beijing 100015, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science and Technology University, Beijing 100015, China","institution_ids":["https://openalex.org/I78675632"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001406512","display_name":"Xiaoying Tang","orcid":"https://orcid.org/0000-0002-9610-0318"},"institutions":[{"id":"https://openalex.org/I12912129","display_name":"Northeastern University","ror":"https://ror.org/04t5xt781","country_code":"US","type":"education","lineage":["https://openalex.org/I12912129"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiaoying Tang","raw_affiliation_strings":["Intelligent Human-Machine Systems Lab, College of Engineering, Northeastern University, Boston, MA 02115, USA","Fax: +1 6173732921","E-mail:"],"affiliations":[{"raw_affiliation_string":"Intelligent Human-Machine Systems Lab, College of Engineering, Northeastern University, Boston, MA 02115, USA","institution_ids":["https://openalex.org/I12912129"]},{"raw_affiliation_string":"Fax: +1 6173732921","institution_ids":[]},{"raw_affiliation_string":"E-mail:","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100690604","display_name":"Guangkai Sun","orcid":"https://orcid.org/0000-0002-3417-6077"},"institutions":[{"id":"https://openalex.org/I78675632","display_name":"Beijing Information Science & Technology University","ror":"https://ror.org/04xnqep60","country_code":"CN","type":"education","lineage":["https://openalex.org/I78675632"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guangkai Sun","raw_affiliation_strings":["Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science and Technology University, Beijing 100015, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science and Technology University, Beijing 100015, China","institution_ids":["https://openalex.org/I78675632"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045331954","display_name":"Yan\u2010Lin He","orcid":"https://orcid.org/0000-0001-9999-3679"},"institutions":[{"id":"https://openalex.org/I78675632","display_name":"Beijing Information Science & Technology University","ror":"https://ror.org/04xnqep60","country_code":"CN","type":"education","lineage":["https://openalex.org/I78675632"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanlin He","raw_affiliation_strings":["Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science and Technology University, Beijing 100015, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science and Technology University, Beijing 100015, China","institution_ids":["https://openalex.org/I78675632"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5028235090","display_name":"Yikang Guo","orcid":"https://orcid.org/0000-0002-1340-1765"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yikang Guo","raw_affiliation_strings":["School of Life Sciences, Beijing Institute of Technology, Beijing 100086, China"],"affiliations":[{"raw_affiliation_string":"School of Life Sciences, Beijing Institute of Technology, Beijing 100086, China","institution_ids":["https://openalex.org/I125839683"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":9,"corresponding_author_ids":["https://openalex.org/A5063169738","https://openalex.org/A5100785538"],"corresponding_institution_ids":["https://openalex.org/I125839683","https://openalex.org/I12912129","https://openalex.org/I78675632"],"apc_list":null,"apc_paid":null,"fwci":0.5302,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.7143852,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":"24","issue":"1","first_page":"163","last_page":"182"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":0.9976000189781189,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":0.9976000189781189,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9764000177383423,"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/T12580","display_name":"Vagus Nerve Stimulation Research","score":0.9534000158309937,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.7144080400466919},{"id":"https://openalex.org/keywords/tonic","display_name":"Tonic (physiology)","score":0.7106800675392151},{"id":"https://openalex.org/keywords/extreme-learning-machine","display_name":"Extreme learning machine","score":0.696171760559082},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6865202784538269},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6425949931144714},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6105654835700989},{"id":"https://openalex.org/keywords/electroencephalography","display_name":"Electroencephalography","score":0.5828196406364441},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5784744620323181},{"id":"https://openalex.org/keywords/perception","display_name":"Perception","score":0.41996270418167114},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.359713077545166},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.25965768098831177},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.2448863983154297},{"id":"https://openalex.org/keywords/neuroscience","display_name":"Neuroscience","score":0.14205104112625122}],"concepts":[{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.7144080400466919},{"id":"https://openalex.org/C127573956","wikidata":"https://www.wikidata.org/wiki/Q7821376","display_name":"Tonic (physiology)","level":2,"score":0.7106800675392151},{"id":"https://openalex.org/C2780150128","wikidata":"https://www.wikidata.org/wiki/Q21948731","display_name":"Extreme learning machine","level":3,"score":0.696171760559082},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6865202784538269},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6425949931144714},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6105654835700989},{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.5828196406364441},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5784744620323181},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.41996270418167114},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.359713077545166},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.25965768098831177},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.2448863983154297},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.14205104112625122}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3233/ida-184388","is_oa":false,"landing_page_url":"https://doi.org/10.3233/ida-184388","pdf_url":null,"source":{"id":"https://openalex.org/S2498839158","display_name":"Intelligent Data Analysis","issn_l":"1088-467X","issn":["1088-467X","1571-4128"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Intelligent Data Analysis","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.6800000071525574,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W80788188","https://openalex.org/W1537132448","https://openalex.org/W1965960317","https://openalex.org/W1978998130","https://openalex.org/W1982334948","https://openalex.org/W1987546049","https://openalex.org/W1990938413","https://openalex.org/W1991874176","https://openalex.org/W2003059629","https://openalex.org/W2003261833","https://openalex.org/W2004812062","https://openalex.org/W2008994198","https://openalex.org/W2011485792","https://openalex.org/W2019477923","https://openalex.org/W2021166197","https://openalex.org/W2025912864","https://openalex.org/W2026131661","https://openalex.org/W2040241382","https://openalex.org/W2042525157","https://openalex.org/W2055173179","https://openalex.org/W2057712082","https://openalex.org/W2058692714","https://openalex.org/W2060005910","https://openalex.org/W2061685536","https://openalex.org/W2063100453","https://openalex.org/W2072675853","https://openalex.org/W2079164751","https://openalex.org/W2084518320","https://openalex.org/W2105799279","https://openalex.org/W2106043670","https://openalex.org/W2111072639","https://openalex.org/W2119764024","https://openalex.org/W2122040390","https://openalex.org/W2127601388","https://openalex.org/W2141695047","https://openalex.org/W2145936750","https://openalex.org/W2148209654","https://openalex.org/W2151141174","https://openalex.org/W2163790949","https://openalex.org/W2166259743","https://openalex.org/W2317354861","https://openalex.org/W2775259072","https://openalex.org/W2895436688","https://openalex.org/W6637705100"],"related_works":["https://openalex.org/W2067443264","https://openalex.org/W31566076","https://openalex.org/W4297902562","https://openalex.org/W2741186499","https://openalex.org/W2804652951","https://openalex.org/W2556335056","https://openalex.org/W2002678693","https://openalex.org/W2041310765","https://openalex.org/W1584764049","https://openalex.org/W2743832667"],"abstract_inverted_index":{"The":[0,38,118],"purpose":[1],"of":[2,18,43,47,67,84,107,120,188,192],"this":[3,23],"study":[4],"is":[5,41,77,96,115],"to":[6,64,148],"present":[7],"a":[8,92,99,183],"novel":[9],"method":[10,40,76,141,165],"which":[11,52],"can":[12,123],"objectively":[13],"identify":[14],"the":[15,34,105,113,139,143,150,153,159,167,172,179,189],"subjective":[16,190],"perception":[17,191],"tonic":[19,48,85,132,144,193],"pain.":[20,134,194],"To":[21],"achieve":[22],"goal,":[24],"scalp":[25],"EEG":[26,89],"data":[27],"are":[28],"recorded":[29],"from":[30,87],"16":[31],"subjects":[32],"under":[33],"cold":[35,133],"stimuli":[36],"condition.":[37],"proposed":[39,79,140],"capable":[42],"classifying":[44],"four":[45],"classes":[46],"pai":[49],"n":[50],"states,":[51],"include":[53],"No":[54],"pain,":[55],"Minor":[56],"Pain,":[57,59],"Moderate":[58],"and":[60,127,185],"Severe":[61],"Pain.":[62],"Due":[63],"multi-class":[65,131],"problem":[66],"our":[68],"research":[69],"an":[70,125],"extended":[71],"Common":[72],"Spatial":[73],"Pattern":[74],"(ECSP)":[75],"first":[78],"for":[80,101,130,152,181],"accurately":[81],"extracting":[82],"features":[83],"pain":[86,102,145],"captured":[88],"data.":[90],"Then,":[91],"single-hidden-layer":[93],"feedforward":[94],"network":[95],"used":[97],"as":[98],"classifier":[100,114,122,169],"identification.":[103],"With":[104],"aid":[106],"extreme":[108],"learning":[109],"machine":[110,163],"(ELM)":[111],"algorithm,":[112],"trained":[116],"here.":[117],"advantages":[119],"ELM-based":[121,154,168],"obtain":[124],"optimal":[126],"generalized":[128],"solution":[129],"Experimental":[135],"results":[136,157],"demonstrate":[137],"that":[138],"discriminates":[142],"successfully.":[146],"Additionally,":[147],"show":[149,166],"superiority":[151],"classifier,":[155],"compared":[156],"with":[158],"well-known":[160],"support":[161],"vector":[162],"(SVM)":[164],"outperform":[170],"than":[171],"SVM-based":[173],"classifier.":[174],"These":[175],"findings":[176],"may":[177],"pay":[178],"way":[180],"providing":[182],"direct":[184],"objective":[186],"measure":[187]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}