{"id":"https://openalex.org/W3201768671","doi":null,"title":"SIGNAL PROCESSING APPROACH IN P300 EVENT DETECTION AND CLASSIFICATIONFROM SINGLE TRIAL EEG FOR ATTENTION ASSESSMENT STUDIES","display_name":"SIGNAL PROCESSING APPROACH IN P300 EVENT DETECTION AND CLASSIFICATIONFROM SINGLE TRIAL EEG FOR ATTENTION ASSESSMENT STUDIES","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3201768671","mag":"3201768671"},"language":"en","primary_location":{"id":"mag:3201768671","doi":null,"is_oa":false,"landing_page_url":"https://www.jcdronline.org/?mno=116735","pdf_url":null,"source":{"id":"https://openalex.org/S2764617691","display_name":"Journal of Cardiovascular Disease Research","issn_l":"0975-3583","issn":["0975-3583","0976-2833"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","poland-20"],"host_organization":"https://openalex.org/P4310320448","host_organization_name":"Medknow","host_organization_lineage":["https://openalex.org/P4310320448","https://openalex.org/P4310318547"],"host_organization_lineage_names":["Medknow","Wolters Kluwer"],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"Journal of Cardiovascular Disease Research","raw_type":null},"volume":null,"type":"article","is_traditional":true,"indexed_in":[],"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/A5101889481","display_name":"P. Prema","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"P. Prema","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5101889481"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.13801729,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"12","issue":"5","first_page":"659","last_page":"675"},"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/T11707","display_name":"Gaze Tracking and Assistive Technology","score":0.0,"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/T10667","display_name":"Emotion and Mood Recognition","score":0.0,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/p300","display_name":"P300","score":0.8840000033378601},{"id":"https://openalex.org/keywords/event-related-potentials","display_name":"event-related potentials","score":0.9200000166893005},{"id":"https://openalex.org/keywords/single-trial-eeg","display_name":"single-trial EEG","score":0.8700000047683716},{"id":"https://openalex.org/keywords/brain-computer-interface","display_name":"brain-computer interface","score":0.8889999985694885},{"id":"https://openalex.org/keywords/attention-assessment","display_name":"attention assessment","score":0.7850000262260437},{"id":"https://openalex.org/keywords/p300-classification","display_name":"P300 classification","score":0.7609999775886536},{"id":"https://openalex.org/keywords/template-matching","display_name":"template matching","score":0.656000018119812},{"id":"https://openalex.org/keywords/signal-to-noise-ratio","display_name":"signal-to-noise ratio","score":0.796999990940094},{"id":"https://openalex.org/keywords/bagged-trees","display_name":"bagged trees","score":0.5339999794960022},{"id":"https://openalex.org/keywords/weighted-k-nearest-neighbors","display_name":"weighted k-nearest neighbors","score":0.7239999771118164},{"id":"https://openalex.org/keywords/quadratic-discriminant-analysis","display_name":"quadratic discriminant analysis","score":0.8849999904632568},{"id":"https://openalex.org/keywords/gaussian-naive-bayes","display_name":"Gaussian naive Bayes","score":0.968999981880188},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"logistic regression","score":0.8349999785423279},{"id":"https://openalex.org/keywords/portable-bci","display_name":"portable BCI","score":0.8209999799728394}],"concepts":[{"id":"https://openalex.org/C173201364","wikidata":"https://www.wikidata.org/wiki/Q897410","display_name":"Brain\u2013computer interface","level":3,"score":0.655478835105896},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6107686161994934},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.5612542629241943},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5455242991447449},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5287299156188965},{"id":"https://openalex.org/C67359045","wikidata":"https://www.wikidata.org/wiki/Q14026181","display_name":"Event-related potential","level":3,"score":0.5081734657287598},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5006687641143799},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.4793556332588196},{"id":"https://openalex.org/C52620605","wikidata":"https://www.wikidata.org/wiki/Q7268357","display_name":"Quadratic classifier","level":3,"score":0.4229475259780884},{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.4139097332954407},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.3336446285247803},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.20474892854690552},{"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":"mag:3201768671","doi":null,"is_oa":false,"landing_page_url":"https://www.jcdronline.org/?mno=116735","pdf_url":null,"source":{"id":"https://openalex.org/S2764617691","display_name":"Journal of Cardiovascular Disease Research","issn_l":"0975-3583","issn":["0975-3583","0976-2833"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","poland-20"],"host_organization":"https://openalex.org/P4310320448","host_organization_name":"Medknow","host_organization_lineage":["https://openalex.org/P4310320448","https://openalex.org/P4310318547"],"host_organization_lineage_names":["Medknow","Wolters Kluwer"],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"Journal of Cardiovascular Disease Research","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4043000042438507,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4297798865","https://openalex.org/W91663998","https://openalex.org/W2148851302","https://openalex.org/W2892814043","https://openalex.org/W2542371477","https://openalex.org/W2980078914","https://openalex.org/W2066040014","https://openalex.org/W2616825927","https://openalex.org/W4200477180","https://openalex.org/W2277692782"],"abstract_inverted_index":{"To":[0],"detect":[1],"P300":[2,68,127],"Event":[3],"Related":[4],"Potential(ERP)":[5],"in":[6,51,108,178],"Brain":[7],"Computer":[8],"Interface":[9],"(BCI)":[10],"experiments":[11],"is":[12,32,60,152],"a\r\nchallenging":[13],"task":[14],"because":[15],"of":[16,35,67,126,146],"its":[17],"poor":[18],"Signal":[19],"to":[20,26,41,62,83],"Noise":[21],"Ratio":[22],"(SNR)":[23],"and":[24,65,86,112,124,128,148,185],"trial":[25,27],"variability.":[28],"The":[29,57,119,139],"second":[30],"major\r\nchallenge":[31],"the":[33,70,110,135,166,171],"choice":[34],"electrodes":[36,73],"that":[37,77,160,182],"varies":[38],"from":[39,69,116],"subject":[40,42],"which":[43,151],"involves":[44],"more":[45],"computation":[46,88],"time\r\nfor":[47],"better":[48,164],"classification":[49,66],"accuracy":[50],"a":[52,175],"dense":[53],"electrode":[54],"array":[55],"system.":[56],"proposed":[58,167],"work":[59],"intended":[61],"improve\r\nthe":[63],"extraction":[64],"standard":[71],"mid-line":[72],"FZ,":[74],"Pz,":[75],"Cz":[76],"are":[78,134],"subject\r\nindependent":[79],"with":[80,144,155],"improved":[81],"signal":[82],"noise":[84],"ratio":[85],"reduced":[87],"complexity":[89],".":[90],"Five":[91],"classifiers":[92],"namely,\r\nWeighted":[93],"KNN(W-kNN),":[94],"Quadratic":[95],"Discriminant":[96],"analysis(QD),":[97],"Bagged":[98],"Trees(BT),":[99],"Guassian":[100],"Naive":[101],"Bayes(GNB)\r\nand":[102],"Logistic":[103],"Regression(LR)":[104],"performance":[105],"were":[106],"compared":[107],"classifying":[109],"target":[111,114],"non":[113],"P300\r\nERPs":[115],"single":[117],"trial.":[118],"time":[120],"domain":[121],"markers":[122],"peak":[123,184],"latency":[125,186],"correlation":[129],"coefficient":[130],"obtained\r\nby":[131],"template":[132],"matching":[133],"three":[136],"features":[137],"used.":[138],"bagged":[140],"tree":[141],"classifier":[142],"outperformed":[143],"classification\r\naccuracy":[145],"87.9%":[147],"AUC":[149],"0.95":[150],"comparatively":[153],"good":[154],"other":[156],"existing":[157],"baseline":[158],"approaches":[159],"uses\r\n3":[161],"electrodes.":[162],"With":[163],"pre-processing":[165],"method":[168],"will":[169],"reduce":[170],"computational":[172],"load":[173],"for":[174],"portable\r\nBCI":[176],"application":[177],"attention":[179],"assessment":[180],"studies":[181],"uses":[183],"features.":[187]},"counts_by_year":[],"updated_date":"2026-11-06T12:05:46.655746","created_date":"2021-10-11T00:00:00"}
