{"id":"https://openalex.org/W3169170731","doi":"https://doi.org/10.1109/syscon48628.2021.9447106","title":"Systems Design for EEG Signal Classification of Sensorimotor Activity Using Machine Learning","display_name":"Systems Design for EEG Signal Classification of Sensorimotor Activity Using Machine Learning","publication_year":2021,"publication_date":"2021-04-15","ids":{"openalex":"https://openalex.org/W3169170731","doi":"https://doi.org/10.1109/syscon48628.2021.9447106","mag":"3169170731"},"language":"en","primary_location":{"id":"doi:10.1109/syscon48628.2021.9447106","is_oa":false,"landing_page_url":"https://doi.org/10.1109/syscon48628.2021.9447106","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Systems Conference (SysCon)","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/A5078265630","display_name":"Jacqueline Heaton","orcid":null},"institutions":[{"id":"https://openalex.org/I204722609","display_name":"Queen's University","ror":"https://ror.org/02y72wh86","country_code":"CA","type":"education","lineage":["https://openalex.org/I204722609"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Jacqueline Heaton","raw_affiliation_strings":["School of Computing, Queen\u2019s University of Kingston, 557 Goodwin Hall, Kingston, ON, Canada","School of Computing, Queen's University of Kingston, 557 Goodwin Hall, Kingston, ON, Canada"],"affiliations":[{"raw_affiliation_string":"School of Computing, Queen\u2019s University of Kingston, 557 Goodwin Hall, Kingston, ON, Canada","institution_ids":["https://openalex.org/I204722609"]},{"raw_affiliation_string":"School of Computing, Queen's University of Kingston, 557 Goodwin Hall, Kingston, ON, Canada","institution_ids":["https://openalex.org/I204722609"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5025867957","display_name":"Sidney Givigi","orcid":"https://orcid.org/0000-0002-3829-3545"},"institutions":[{"id":"https://openalex.org/I204722609","display_name":"Queen's University","ror":"https://ror.org/02y72wh86","country_code":"CA","type":"education","lineage":["https://openalex.org/I204722609"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Sidney Givigi","raw_affiliation_strings":["School of Computing, Queen\u2019s University of Kingston, 557 Goodwin Hall, Kingston, ON, Canada","School of Computing, Queen's University of Kingston, 557 Goodwin Hall, Kingston, ON, Canada"],"affiliations":[{"raw_affiliation_string":"School of Computing, Queen\u2019s University of Kingston, 557 Goodwin Hall, Kingston, ON, Canada","institution_ids":["https://openalex.org/I204722609"]},{"raw_affiliation_string":"School of Computing, Queen's University of Kingston, 557 Goodwin Hall, Kingston, ON, Canada","institution_ids":["https://openalex.org/I204722609"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5078265630"],"corresponding_institution_ids":["https://openalex.org/I204722609"],"apc_list":null,"apc_paid":null,"fwci":0.2331,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.47829451,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"56","issue":null,"first_page":"1","last_page":"7"},"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/T10241","display_name":"Functional Brain Connectivity Studies","score":0.9810000061988831,"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":0.9764000177383423,"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/support-vector-machine","display_name":"Support vector machine","score":0.7677776217460632},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7508336901664734},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7490208745002747},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6189028024673462},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.5676405429840088},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.547821044921875},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5187075734138489},{"id":"https://openalex.org/keywords/electroencephalography","display_name":"Electroencephalography","score":0.4840233027935028},{"id":"https://openalex.org/keywords/brain\u2013computer-interface","display_name":"Brain\u2013computer interface","score":0.4468289613723755},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4327367842197418}],"concepts":[{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.7677776217460632},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7508336901664734},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7490208745002747},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6189028024673462},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.5676405429840088},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.547821044921875},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5187075734138489},{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.4840233027935028},{"id":"https://openalex.org/C173201364","wikidata":"https://www.wikidata.org/wiki/Q897410","display_name":"Brain\u2013computer interface","level":3,"score":0.4468289613723755},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4327367842197418},{"id":"https://openalex.org/C118552586","wikidata":"https://www.wikidata.org/wiki/Q7867","display_name":"Psychiatry","level":1,"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/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/syscon48628.2021.9447106","is_oa":false,"landing_page_url":"https://doi.org/10.1109/syscon48628.2021.9447106","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Systems Conference (SysCon)","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":19,"referenced_works":["https://openalex.org/W1563140143","https://openalex.org/W1976974032","https://openalex.org/W1979512884","https://openalex.org/W1983620297","https://openalex.org/W2038642630","https://openalex.org/W2083742331","https://openalex.org/W2091435129","https://openalex.org/W2096673078","https://openalex.org/W2101629643","https://openalex.org/W2137958317","https://openalex.org/W2143013739","https://openalex.org/W2162800060","https://openalex.org/W2443376560","https://openalex.org/W2590377595","https://openalex.org/W2749183303","https://openalex.org/W2804949908","https://openalex.org/W2915893085","https://openalex.org/W2949609440","https://openalex.org/W6681207463"],"related_works":["https://openalex.org/W3202969339","https://openalex.org/W4237513258","https://openalex.org/W1994410349","https://openalex.org/W3177028067","https://openalex.org/W2889342546","https://openalex.org/W1913385466","https://openalex.org/W2015048155","https://openalex.org/W3004117467","https://openalex.org/W2106231951","https://openalex.org/W2032664813"],"abstract_inverted_index":{"This":[0],"paper":[1],"proposes":[2],"a":[3,16,151,191,198,241],"systems":[4,86],"design":[5,87],"for":[6,59,63,207,215,222,238],"classifying":[7],"EEG":[8,21],"motor":[9,22],"movement":[10,23,47],"signals":[11,24,40],"using":[12,82],"AI":[13],"that":[14,127,230],"achieves":[15],"high":[17],"degree":[18],"of":[19,43,46,91,142,184,194,202,227],"accuracy.":[20],"are":[25,41,49],"generated":[26],"by":[27],"the":[28,31,44,55,64,75,89,100,110,124,140,148,176,187,205,216,223],"brain":[29],"when":[30],"subject":[32],"consciously":[33],"attempts":[34],"to":[35,51,71,103,175,213,220],"move":[36],"their":[37],"body.":[38],"These":[39],"reflective":[42],"kind":[45],"they":[48],"attempting":[50],"achieve,":[52],"and":[53,107,115,156,169,218],"improving":[54],"classification":[56,68,144,239],"would":[57],"allow":[58],"better":[60],"assislive":[61],"devices":[62],"physically":[65],"disabled.":[66],"Al":[67],"requires":[69],"features":[70,95,129],"be":[72,80,236],"extracted":[73,81],"from":[74,99,211],"raw":[76],"data.":[77],"Features":[78],"can":[79,235],"different":[83,92,143,208,231],"algorithms.":[84],"The":[85,94,137,178,225],"allows":[88,139],"selection":[90],"features.":[93,118],"used":[96,132,237],"arc":[97],"calculated":[98],"dalapuints":[101],"corresponding":[102],"1":[104],"second":[105],"windows":[106],"transformed":[108],"into":[109],"sigma":[111],"(\u03a3),":[112],"phi":[113],"(\u03a6),":[114],"omega":[116],"(\u03a9)":[117],"To":[119],"our":[120],"knowledge,":[121],"this":[122],"is":[123,244],"first":[125],"time":[126],"these":[128,167],"have":[130],"been":[131],"with":[133,150],"machine":[134,232],"learning":[135,233],"techniques.":[136],"approach":[138],"use":[141],"models.":[145],"We":[146],"test":[147],"system":[149],"Support":[152],"Vector":[153],"Machine":[154],"(SVM)":[155],"an":[157,181],"Artificial":[158],"Neural":[159],"Network":[160],"(ANN),":[161],"which":[162],"were":[163],"both":[164],"trained":[165],"on":[166],"features,":[168],"each":[170],"window":[171],"classified":[172],"independently":[173],"according":[174],"model.":[177],"SVM":[179,217],"had":[180,190],"average":[182],"accuracy":[183,193,206],"88%,":[185],"while":[186],"neural":[188],"network":[189],"higher":[192],"94%.":[195],"There":[196],"was":[197],"relatively":[199],"large":[200],"amount":[201],"variance":[203],"in":[204],"subjects,":[209],"ranging":[210],"45.9%":[212],"99.6%":[214],"243%":[219],"99.7%":[221],"ANN.":[224],"proof":[226],"concept":[228],"demonstrates":[229],"algorithms":[234],"if":[240],"pipeline":[242],"architecture":[243],"used.":[245]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
