{"id":"https://openalex.org/W2991374643","doi":"https://doi.org/10.1109/smc.2019.8914606","title":"A Reference-based Source Extraction Algorithm to Extract Movement Related Cortical Potentials for Brain-Computer Interface Applications","display_name":"A Reference-based Source Extraction Algorithm to Extract Movement Related Cortical Potentials for Brain-Computer Interface Applications","publication_year":2019,"publication_date":"2019-10-01","ids":{"openalex":"https://openalex.org/W2991374643","doi":"https://doi.org/10.1109/smc.2019.8914606","mag":"2991374643"},"language":"en","primary_location":{"id":"doi:10.1109/smc.2019.8914606","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc.2019.8914606","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)","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/A5078465729","display_name":"Fatemeh Karimi","orcid":"https://orcid.org/0000-0001-6805-277X"},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Fatemeh Karimi","raw_affiliation_strings":["Systems Design Engineering Department, University of Waterloo, Waterloo, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Systems Design Engineering Department, University of Waterloo, Waterloo, Canada","institution_ids":["https://openalex.org/I151746483"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5052756710","display_name":"Ning Jiang","orcid":"https://orcid.org/0000-0003-1579-3114"},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Ning Jiang","raw_affiliation_strings":["Systems Design Engineering Department, University of Waterloo, Waterloo, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Systems Design Engineering Department, University of Waterloo, Waterloo, Canada","institution_ids":["https://openalex.org/I151746483"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I151746483"],"apc_list":null,"apc_paid":null,"fwci":0.1246,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.50378597,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"86","issue":null,"first_page":"3603","last_page":"3607"},"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":0.9993000030517578,"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/T11601","display_name":"Neuroscience and Neural Engineering","score":0.9951000213623047,"subfield":{"id":"https://openalex.org/subfields/2804","display_name":"Cellular and Molecular Neuroscience"},"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/brain\u2013computer-interface","display_name":"Brain\u2013computer interface","score":0.8977923393249512},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7898301482200623},{"id":"https://openalex.org/keywords/electroencephalography","display_name":"Electroencephalography","score":0.5754563808441162},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5467430949211121},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5158268809318542},{"id":"https://openalex.org/keywords/interface","display_name":"Interface (matter)","score":0.5078311562538147},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5008692741394043},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.484493225812912},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.4766155779361725},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.470628023147583},{"id":"https://openalex.org/keywords/independent-component-analysis","display_name":"Independent component analysis","score":0.4620928168296814},{"id":"https://openalex.org/keywords/blind-signal-separation","display_name":"Blind signal separation","score":0.4409370422363281},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.3979324698448181},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.2872520685195923}],"concepts":[{"id":"https://openalex.org/C173201364","wikidata":"https://www.wikidata.org/wiki/Q897410","display_name":"Brain\u2013computer interface","level":3,"score":0.8977923393249512},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7898301482200623},{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.5754563808441162},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5467430949211121},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5158268809318542},{"id":"https://openalex.org/C113843644","wikidata":"https://www.wikidata.org/wiki/Q901882","display_name":"Interface (matter)","level":4,"score":0.5078311562538147},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5008692741394043},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.484493225812912},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.4766155779361725},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.470628023147583},{"id":"https://openalex.org/C51432778","wikidata":"https://www.wikidata.org/wiki/Q1259145","display_name":"Independent component analysis","level":2,"score":0.4620928168296814},{"id":"https://openalex.org/C120317606","wikidata":"https://www.wikidata.org/wiki/Q17105967","display_name":"Blind signal separation","level":3,"score":0.4409370422363281},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3979324698448181},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.2872520685195923},{"id":"https://openalex.org/C118552586","wikidata":"https://www.wikidata.org/wiki/Q7867","display_name":"Psychiatry","level":1,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C157915830","wikidata":"https://www.wikidata.org/wiki/Q2928001","display_name":"Bubble","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/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C129307140","wikidata":"https://www.wikidata.org/wiki/Q6795880","display_name":"Maximum bubble pressure method","level":3,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/smc.2019.8914606","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc.2019.8914606","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)","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":20,"referenced_works":["https://openalex.org/W271314235","https://openalex.org/W1514159701","https://openalex.org/W1984862406","https://openalex.org/W1990116193","https://openalex.org/W1994771629","https://openalex.org/W2049166497","https://openalex.org/W2059517562","https://openalex.org/W2068184837","https://openalex.org/W2080987602","https://openalex.org/W2099509424","https://openalex.org/W2141538032","https://openalex.org/W2142638745","https://openalex.org/W2148525162","https://openalex.org/W2158035059","https://openalex.org/W2170980780","https://openalex.org/W2172045185","https://openalex.org/W2199391040","https://openalex.org/W2213896284","https://openalex.org/W2535841181","https://openalex.org/W2732772830"],"related_works":["https://openalex.org/W2390344110","https://openalex.org/W2046761971","https://openalex.org/W2364896863","https://openalex.org/W2361066326","https://openalex.org/W2182042810","https://openalex.org/W2156932837","https://openalex.org/W2380698615","https://openalex.org/W374502268","https://openalex.org/W2103029460","https://openalex.org/W1785857632"],"abstract_inverted_index":{"Brain-Computer":[0],"Interface":[1],"(BCI)":[2],"systems":[3,41],"aim":[4],"at":[5],"providing":[6],"a":[7,21,57,76,87,109,201],"channel":[8],"through":[9],"electroencephalogram":[10],"(EEG)":[11],"to":[12,32,61,92,98,126,173],"control":[13,33],"external":[14],"devices.":[15],"Movement-Related":[16],"Cortical":[17],"Potential":[18],"(MRCP)":[19],"is":[20,56,96,106,132,269],"low-frequency":[22],"and":[23,68,86,163,178,190,241,275,278],"low-amplitude":[24],"EEG":[25,65],"component":[26],"that":[27,181,265],"has":[28],"been":[29],"recently":[30],"introduced":[31],"real-time":[34],"BCI":[35,40,94],"systems.":[36],"The":[37,101,115,129,184,213,259],"performance":[38,102,185,202,245],"of":[39,50,64,71,103,186,209,246],"based":[42,81,148],"on":[43,47,82,149,194],"MRCP":[44,53,197,282],"highly":[45],"depends":[46],"the":[48,93,104,119,123,175,179,187,195,206,210,217,244,247,250,266],"accuracy":[49],"single":[51],"trial":[52],"detection,":[54],"which":[55],"challenging":[58],"task":[59],"due":[60],"high":[62,280],"amount":[63],"background":[66],"noise":[67],"various":[69],"types":[70],"artifacts.":[72],"In":[73],"this":[74,262],"paper,":[75],"semi-blind":[77],"source":[78,145],"extraction":[79,146,192],"algorithm":[80,177,189,219],"second":[83,150,251],"order":[84,151],"statistics":[85],"reference":[88,116],"signal,":[89],"designed":[90],"according":[91],"paradigm,":[95],"presented":[97],"extract":[99,127],"MRCP.":[100,128,212],"method":[105,131,248,268],"investigated":[107,234],"during":[108],"complex":[110],"motor":[111],"task,":[112],"gait":[113,167],"initiation.":[114],"signal":[117],"utilizes":[118],"temporal":[120],"information":[121],"from":[122,261],"training":[124,155,224,274],"set":[125,225],"proposed":[130,176,188,218,267],"compared":[133],"with":[134,216,221,238],"two":[135,144],"commonly":[136],"used":[137,172],"spatial":[138],"filtering":[139],"techniques":[140],"as":[141,143],"well":[142],"methods":[147,180,193,255],"statistics.":[152],"Two":[153],"different":[154],"sets":[156,277],"including":[157],"only":[158,223],"ankle":[159,164],"dorsiflexion":[160,165],"data":[161,170],"(AD)":[162],"plus":[166],"initiation":[168],"(GI)":[169],"was":[171,198,229,249],"train":[174],"require":[182],"training.":[183],"other":[191,233],"extracted":[196,211],"evaluated":[199],"by":[200],"index":[203],"(PI)":[204],"quantifying":[205],"signal-to-noise":[207],"ratio":[208],"PI":[214],"obtained":[215],"trained":[220,237],"AD":[222,240],"(2.43":[226],"\u00b1":[227,257],"1.23)":[228],"greater":[230],"than":[231],"all":[232,254],"methods.":[235],"When":[236],"both":[239],"GI":[242],"trials,":[243],"best":[252],"among":[253],"(2.52":[256],"0.83).":[258],"results":[260],"study":[263],"show":[264],"robust":[270],"against":[271],"differences":[272],"between":[273],"testing":[276],"provides":[279],"quality":[281],"extraction.":[283]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}
