{"id":"https://openalex.org/W4361790898","doi":"https://doi.org/10.1145/3577530.3577576","title":"Research on classification of motor imagery EEG signals based on TQWT-CSP","display_name":"Research on classification of motor imagery EEG signals based on TQWT-CSP","publication_year":2022,"publication_date":"2022-12-09","ids":{"openalex":"https://openalex.org/W4361790898","doi":"https://doi.org/10.1145/3577530.3577576"},"language":"en","primary_location":{"id":"doi:10.1145/3577530.3577576","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3577530.3577576","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 6th International Conference on Computer Science and Artificial Intelligence","raw_type":"proceedings-article"},"type":"conference-paper","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/A5088999364","display_name":"Han Wu","orcid":"https://orcid.org/0000-0002-5051-7134"},"institutions":[{"id":"https://openalex.org/I37802460","display_name":"Northwest University","ror":"https://ror.org/00z3td547","country_code":"CN","type":"education","lineage":["https://openalex.org/I37802460"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Han Wu","raw_affiliation_strings":["School of Mathematics, Medical big data research center, Northwest University, China"],"raw_orcid":"https://orcid.org/0000-0002-5051-7134","affiliations":[{"raw_affiliation_string":"School of Mathematics, Medical big data research center, Northwest University, China","institution_ids":["https://openalex.org/I37802460"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101696477","display_name":"Jian Jia","orcid":"https://orcid.org/0000-0002-2895-846X"},"institutions":[{"id":"https://openalex.org/I37802460","display_name":"Northwest University","ror":"https://ror.org/00z3td547","country_code":"CN","type":"education","lineage":["https://openalex.org/I37802460"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jian Jia","raw_affiliation_strings":["School of Mathematics, Medical big data research center, Northwest University, China"],"raw_orcid":"https://orcid.org/0000-0002-2895-846X","affiliations":[{"raw_affiliation_string":"School of Mathematics, Medical big data research center, Northwest University, China","institution_ids":["https://openalex.org/I37802460"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101551923","display_name":"Rui Zhang","orcid":"https://orcid.org/0000-0001-9547-2585"},"institutions":[{"id":"https://openalex.org/I37802460","display_name":"Northwest University","ror":"https://ror.org/00z3td547","country_code":"CN","type":"education","lineage":["https://openalex.org/I37802460"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rui Zhang","raw_affiliation_strings":["School of Mathematics, Medical big data research center, Northwest University, China"],"raw_orcid":"https://orcid.org/0000-0001-9547-2585","affiliations":[{"raw_affiliation_string":"School of Mathematics, Medical big data research center, Northwest University, China","institution_ids":["https://openalex.org/I37802460"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I37802460"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"289","last_page":"295"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9997000098228455,"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":0.9997000098228455,"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/T11707","display_name":"Gaze Tracking and Assistive Technology","score":0.9713000059127808,"subfield":{"id":"https://openalex.org/subfields/1709","display_name":"Human-Computer Interaction"},"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9287999868392944,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.7284160256385803},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6602363586425781},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6526644229888916},{"id":"https://openalex.org/keywords/sliding-window-protocol","display_name":"Sliding window protocol","score":0.6446603536605835},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.6356329321861267},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.6234762668609619},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6138159036636353},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.5835788249969482},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5789964199066162},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5536099076271057},{"id":"https://openalex.org/keywords/motor-imagery","display_name":"Motor imagery","score":0.5189449787139893},{"id":"https://openalex.org/keywords/linear-discriminant-analysis","display_name":"Linear discriminant analysis","score":0.45322638750076294},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.4176179766654968},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.33872663974761963},{"id":"https://openalex.org/keywords/window","display_name":"Window (computing)","score":0.23502421379089355},{"id":"https://openalex.org/keywords/electroencephalography","display_name":"Electroencephalography","score":0.20096594095230103},{"id":"https://openalex.org/keywords/brain\u2013computer-interface","display_name":"Brain\u2013computer interface","score":0.1390104591846466}],"concepts":[{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.7284160256385803},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6602363586425781},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6526644229888916},{"id":"https://openalex.org/C102392041","wikidata":"https://www.wikidata.org/wiki/Q592860","display_name":"Sliding window protocol","level":3,"score":0.6446603536605835},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.6356329321861267},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.6234762668609619},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6138159036636353},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.5835788249969482},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5789964199066162},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5536099076271057},{"id":"https://openalex.org/C54808283","wikidata":"https://www.wikidata.org/wiki/Q6918191","display_name":"Motor imagery","level":4,"score":0.5189449787139893},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.45322638750076294},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.4176179766654968},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33872663974761963},{"id":"https://openalex.org/C2778751112","wikidata":"https://www.wikidata.org/wiki/Q835016","display_name":"Window (computing)","level":2,"score":0.23502421379089355},{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.20096594095230103},{"id":"https://openalex.org/C173201364","wikidata":"https://www.wikidata.org/wiki/Q897410","display_name":"Brain\u2013computer interface","level":3,"score":0.1390104591846466},{"id":"https://openalex.org/C118552586","wikidata":"https://www.wikidata.org/wiki/Q7867","display_name":"Psychiatry","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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3577530.3577576","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3577530.3577576","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 6th International Conference on Computer Science and Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7","score":0.6800000071525574}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1994307947","https://openalex.org/W2038120455","https://openalex.org/W2097443486","https://openalex.org/W2098391699","https://openalex.org/W2132360759","https://openalex.org/W2145302786","https://openalex.org/W2152863712","https://openalex.org/W2291176881","https://openalex.org/W2611482981","https://openalex.org/W2611808763","https://openalex.org/W2778455075","https://openalex.org/W2955714720","https://openalex.org/W2964596665","https://openalex.org/W3041615496","https://openalex.org/W3126102196","https://openalex.org/W3127935830","https://openalex.org/W3132435413","https://openalex.org/W3139270893","https://openalex.org/W3158764126","https://openalex.org/W3161696860","https://openalex.org/W3169470951","https://openalex.org/W4253953323","https://openalex.org/W4296980820","https://openalex.org/W6677014235","https://openalex.org/W6834477340"],"related_works":["https://openalex.org/W2041547723","https://openalex.org/W2951280790","https://openalex.org/W3100645810","https://openalex.org/W2353818951","https://openalex.org/W2250488071","https://openalex.org/W2356150353","https://openalex.org/W2069106223","https://openalex.org/W2944066407","https://openalex.org/W3088957430","https://openalex.org/W2353313924"],"abstract_inverted_index":{"In":[0,131],"this":[1],"paper,":[2],"a":[3,187],"novel":[4],"feature":[5,33,106,112,121],"extraction":[6],"method":[7,179],"called":[8],"TQWT-CSP":[9],"based":[10],"on":[11],"sliding":[12,87],"time":[13,79,88],"window":[14],"is":[15,42,49,62,75,101,115,127,185],"proposed,":[16],"which":[17,81],"can":[18],"extract":[19],"the":[20,45,55,60,86,98,105,109,119,132,137,146,149,177,181,191,197],"spatial":[21],"features":[22],"of":[23,54,138,148,173],"multiple":[24,67],"time-frequency":[25,95],"blocks,":[26],"so":[27,90],"as":[28,91],"to":[29,51,92,103,117,144],"select":[30,52,118],"different":[31,36],"optimal":[32,120],"sets":[34],"for":[35,129],"individuals":[37],"before":[38],"motor":[39],"imagery":[40],"classification":[41,182],"completed.":[43],"Firstly,":[44],"linear":[46],"discriminant":[47],"criterion":[48],"used":[50,102,116,128,143],"channels":[53],"signal":[56,61],"after":[57],"preprocessing;":[58],"Secondly,":[59],"decomposed":[63],"and":[64,72,123,158,168,184],"reconstructed":[65],"into":[66,77],"subbands":[68],"by":[69,84],"using":[70,85,160],"TQWT,":[71],"each":[73],"subband":[74],"divided":[76],"11":[78],"periods":[80],"are":[82,142],"overlap":[83],"window,":[89],"get":[93],"several":[94],"blocks;":[96],"Next,":[97],"CSP":[99],"algorithm":[100,114],"obtain":[104],"set;":[107],"Finally,":[108],"random":[110],"forest":[111],"selection":[113],"subset":[122],"support":[124],"vector":[125],"machine":[126],"classification.":[130],"experiment,":[133],"10":[134],"subjects":[135],"in":[136,196],"GigaDB":[139],"Motor":[140],"Dataset":[141],"verify":[145],"performance":[147,183],"proposed":[150,178],"method.":[151],"The":[152,171],"average":[153],"accuracy,":[154],"kappa":[155],"coefficient,":[156],"sensitivity":[157],"specificity":[159],"5-fold":[161],"cross":[162],"validation":[163],"were":[164],"82.14%,":[165],"64.11%,":[166],"80.60%":[167],"83.71%":[169],"respectively.":[170],"results":[172],"experiment":[174],"show":[175],"that":[176],"improve":[180],"at":[186],"high":[188],"level":[189],"among":[190],"numerical":[192],"measurement":[193],"indicators":[194],"given":[195],"state-of-":[198],"the-art":[199],"research":[200],"literature.":[201]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-26T07:53:14.480251","created_date":"2025-10-10T00:00:00"}