{"id":"https://openalex.org/W4388937477","doi":"https://doi.org/10.1109/icccnt56998.2023.10306824","title":"Cognitive State Classification Using a single-channel Headset: An EEG Analysis Approach","display_name":"Cognitive State Classification Using a single-channel Headset: An EEG Analysis Approach","publication_year":2023,"publication_date":"2023-07-06","ids":{"openalex":"https://openalex.org/W4388937477","doi":"https://doi.org/10.1109/icccnt56998.2023.10306824"},"language":"en","primary_location":{"id":"doi:10.1109/icccnt56998.2023.10306824","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icccnt56998.2023.10306824","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)","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/A5100623242","display_name":"Rakesh Kumar","orcid":"https://orcid.org/0000-0002-8967-3855"},"institutions":[{"id":"https://openalex.org/I152869788","display_name":"Motilal Nehru National Institute of Technology","ror":"https://ror.org/04dp7tp96","country_code":"IN","type":"education","lineage":["https://openalex.org/I152869788"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Rakesh Kumar Rai","raw_affiliation_strings":["CSED, MNNIT Allahabad,Prayagraj,India"],"affiliations":[{"raw_affiliation_string":"CSED, MNNIT Allahabad,Prayagraj,India","institution_ids":["https://openalex.org/I152869788"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080419784","display_name":"Dushyant Kumar Singh","orcid":"https://orcid.org/0000-0002-1897-1660"},"institutions":[{"id":"https://openalex.org/I152869788","display_name":"Motilal Nehru National Institute of Technology","ror":"https://ror.org/04dp7tp96","country_code":"IN","type":"education","lineage":["https://openalex.org/I152869788"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Dushyant Kumar Singh","raw_affiliation_strings":["CSED, MNNIT Allahabad,Prayagraj,India"],"affiliations":[{"raw_affiliation_string":"CSED, MNNIT Allahabad,Prayagraj,India","institution_ids":["https://openalex.org/I152869788"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016541448","display_name":"Lalit Kumar","orcid":"https://orcid.org/0000-0002-8441-1696"},"institutions":[{"id":"https://openalex.org/I152869788","display_name":"Motilal Nehru National Institute of Technology","ror":"https://ror.org/04dp7tp96","country_code":"IN","type":"education","lineage":["https://openalex.org/I152869788"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Lalit Kumar","raw_affiliation_strings":["CSED, MNNIT Allahabad,Prayagraj,India"],"affiliations":[{"raw_affiliation_string":"CSED, MNNIT Allahabad,Prayagraj,India","institution_ids":["https://openalex.org/I152869788"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5022093778","display_name":"M. A. Ansari","orcid":"https://orcid.org/0000-0002-9083-1523"},"institutions":[{"id":"https://openalex.org/I82571370","display_name":"GLA University","ror":"https://ror.org/05fnxgv12","country_code":"IN","type":"education","lineage":["https://openalex.org/I82571370"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Mohd. Aquib Ansari","raw_affiliation_strings":["DCEA, GLA University,Mathura,India"],"affiliations":[{"raw_affiliation_string":"DCEA, GLA University,Mathura,India","institution_ids":["https://openalex.org/I82571370"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100623242"],"corresponding_institution_ids":["https://openalex.org/I152869788"],"apc_list":null,"apc_paid":null,"fwci":0.3455,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.59183894,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":null,"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/T10581","display_name":"Neural dynamics and brain function","score":0.9585999846458435,"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":0.9539999961853027,"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/electroencephalography","display_name":"Electroencephalography","score":0.7427095770835876},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.7090635895729065},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.6810398101806641},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6724510192871094},{"id":"https://openalex.org/keywords/headset","display_name":"Headset","score":0.6572811007499695},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6033477187156677},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.586948573589325},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"Gradient boosting","score":0.5389953255653381},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5170915126800537},{"id":"https://openalex.org/keywords/brain\u2013computer-interface","display_name":"Brain\u2013computer interface","score":0.46433010697364807},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.45516738295555115},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4275904595851898},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.36292093992233276},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.09574559330940247}],"concepts":[{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.7427095770835876},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.7090635895729065},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.6810398101806641},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6724510192871094},{"id":"https://openalex.org/C2780657452","wikidata":"https://www.wikidata.org/wiki/Q1193170","display_name":"Headset","level":2,"score":0.6572811007499695},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6033477187156677},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.586948573589325},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.5389953255653381},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5170915126800537},{"id":"https://openalex.org/C173201364","wikidata":"https://www.wikidata.org/wiki/Q897410","display_name":"Brain\u2013computer interface","level":3,"score":0.46433010697364807},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45516738295555115},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4275904595851898},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.36292093992233276},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.09574559330940247},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icccnt56998.2023.10306824","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icccnt56998.2023.10306824","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Climate action","score":0.5299999713897705,"id":"https://metadata.un.org/sdg/13"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1977884228","https://openalex.org/W2796422904","https://openalex.org/W2887425726","https://openalex.org/W2897863426","https://openalex.org/W2904463929","https://openalex.org/W3024454747","https://openalex.org/W3159625140","https://openalex.org/W3164305153","https://openalex.org/W3187370891","https://openalex.org/W3198526660","https://openalex.org/W4206759433","https://openalex.org/W4210397188","https://openalex.org/W4214902753","https://openalex.org/W4224100298","https://openalex.org/W4280622035","https://openalex.org/W4281655165","https://openalex.org/W4281683447","https://openalex.org/W4282977860","https://openalex.org/W4288460594","https://openalex.org/W4295943083","https://openalex.org/W4362669929"],"related_works":["https://openalex.org/W2894943710","https://openalex.org/W2808042781","https://openalex.org/W2141511365","https://openalex.org/W2967733078","https://openalex.org/W3204430031","https://openalex.org/W3137904399","https://openalex.org/W4310492845","https://openalex.org/W4386690025","https://openalex.org/W2885778889","https://openalex.org/W2766514146"],"abstract_inverted_index":{"A":[0],"growing":[1],"number":[2],"of":[3,43,99,109,129,142],"people":[4],"are":[5],"using":[6,33,55],"electroencephalography":[7],"(EEG)":[8],"as":[9,158,165],"a":[10,22,159,170],"noninvasive":[11],"way":[12],"to":[13,38,47,88],"record":[14],"and":[15,63,85,117,157],"monitor":[16],"their":[17],"brainwaves.":[18],"An":[19],"EEG":[20],"is":[21,46],"technique":[23],"that":[24],"captures":[25],"the":[26,31,39,49,58,93,100,107,125,133,146,149,166],"electric":[27],"signals":[28],"generated":[29],"by":[30,132,155],"brain":[32],"tiny":[34],"metal":[35],"discs":[36],"connected":[37],"scalp.":[40],"The":[41,97,120],"goal":[42],"this":[44,67],"paper":[45],"classify":[48],"cognitive":[50,95],"state":[51],"into":[52],"two":[53],"categories":[54],"information":[56],"from":[57],"Neurosky":[59],"headset,":[60],"namely":[61],"maths":[62],"relax":[64],"label.":[65],"In":[66],"paper,":[68],"we":[69],"have":[70],"used":[71],"logistic":[72],"regression,":[73],"linear":[74],"SVM,":[75],"non-linear":[76],"SVC,":[77],"random":[78,121],"forests,":[79],"XG":[80,134,150,161],"boosting,":[81,84],"MLP,":[82],"gradient":[83],"neural":[86],"network":[87],"make":[89],"accurate":[90],"predictions":[91],"about":[92],"subject's":[94],"state.":[96],"performance":[98],"machine":[101],"learning":[102],"models":[103],"were":[104],"analyzed":[105],"on":[106],"basis":[108],"various":[110],"evaluation":[111],"parameters":[112],"like":[113],"accuracy,":[114],"precision,":[115],"recall":[116],"F-1":[118],"score.":[119],"forest":[122],"algorithm":[123],"had":[124,138],"highest":[126],"accuracy":[127,140,153],"rate":[128,141],"89%,":[130],"followed":[131],"boosting":[135,151,162],"algorithm,":[136],"which":[137],"an":[139],"87%.":[143],"After":[144],"tuning":[145],"hyper":[147],"parameters,":[148],"algorithm's":[152],"increased":[154],"3":[156],"result,":[160],"model":[163],"emerged":[164],"best":[167],"model,":[168],"with":[169],"90%":[171],"prediction":[172],"accuracy.":[173]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2026-04-16T15:07:20.185449","created_date":"2025-10-10T00:00:00"}
