{"id":"https://openalex.org/W4303982382","doi":"https://doi.org/10.3390/s22197623","title":"EEG/fNIRS Based Workload Classification Using Functional Brain Connectivity and Machine Learning","display_name":"EEG/fNIRS Based Workload Classification Using Functional Brain Connectivity and Machine Learning","publication_year":2022,"publication_date":"2022-10-08","ids":{"openalex":"https://openalex.org/W4303982382","doi":"https://doi.org/10.3390/s22197623","pmid":"https://pubmed.ncbi.nlm.nih.gov/36236725"},"language":"en","primary_location":{"id":"doi:10.3390/s22197623","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s22197623","pdf_url":"https://www.mdpi.com/1424-8220/22/19/7623/pdf?version=1665232458","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/1424-8220/22/19/7623/pdf?version=1665232458","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5079140622","display_name":"Jun Cao","orcid":"https://orcid.org/0000-0003-2121-7631"},"institutions":[{"id":"https://openalex.org/I82284825","display_name":"Cranfield University","ror":"https://ror.org/05cncd958","country_code":"GB","type":"education","lineage":["https://openalex.org/I82284825"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Jun Cao","raw_affiliation_strings":["School of Aerospace, Transport and Manufacturing, Cranfield University, Bedfordshire MK43 0AL, UK"],"affiliations":[{"raw_affiliation_string":"School of Aerospace, Transport and Manufacturing, Cranfield University, Bedfordshire MK43 0AL, UK","institution_ids":["https://openalex.org/I82284825"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037886906","display_name":"Enara Martin Garro","orcid":"https://orcid.org/0009-0007-1071-0718"},"institutions":[{"id":"https://openalex.org/I82284825","display_name":"Cranfield University","ror":"https://ror.org/05cncd958","country_code":"GB","type":"education","lineage":["https://openalex.org/I82284825"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Enara Martin Garro","raw_affiliation_strings":["School of Aerospace, Transport and Manufacturing, Cranfield University, Bedfordshire MK43 0AL, UK"],"affiliations":[{"raw_affiliation_string":"School of Aerospace, Transport and Manufacturing, Cranfield University, Bedfordshire MK43 0AL, UK","institution_ids":["https://openalex.org/I82284825"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5087731729","display_name":"Yifan Zhao","orcid":"https://orcid.org/0000-0003-2383-5724"},"institutions":[{"id":"https://openalex.org/I82284825","display_name":"Cranfield University","ror":"https://ror.org/05cncd958","country_code":"GB","type":"education","lineage":["https://openalex.org/I82284825"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Yifan Zhao","raw_affiliation_strings":["School of Aerospace, Transport and Manufacturing, Cranfield University, Bedfordshire MK43 0AL, UK"],"affiliations":[{"raw_affiliation_string":"School of Aerospace, Transport and Manufacturing, Cranfield University, Bedfordshire MK43 0AL, UK","institution_ids":["https://openalex.org/I82284825"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5087731729"],"corresponding_institution_ids":["https://openalex.org/I82284825"],"apc_list":{"value":2400,"currency":"CHF","value_usd":2598},"apc_paid":{"value":2400,"currency":"CHF","value_usd":2598},"fwci":5.9918,"has_fulltext":true,"cited_by_count":53,"citation_normalized_percentile":{"value":0.97348201,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"22","issue":"19","first_page":"7623","last_page":"7623"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9998000264167786,"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":0.9998000264167786,"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/T11196","display_name":"Non-Invasive Vital Sign Monitoring","score":0.9944000244140625,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10745","display_name":"Heart Rate Variability and Autonomic Control","score":0.9943000078201294,"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/electroencephalography","display_name":"Electroencephalography","score":0.8683377504348755},{"id":"https://openalex.org/keywords/workload","display_name":"Workload","score":0.6846016049385071},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5869737863540649},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5390858054161072},{"id":"https://openalex.org/keywords/functional-near-infrared-spectroscopy","display_name":"Functional near-infrared spectroscopy","score":0.49409177899360657},{"id":"https://openalex.org/keywords/univariate","display_name":"Univariate","score":0.48777610063552856},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4617585837841034},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.2978084683418274},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2801503539085388},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.20495426654815674},{"id":"https://openalex.org/keywords/neuroscience","display_name":"Neuroscience","score":0.133195698261261},{"id":"https://openalex.org/keywords/cognition","display_name":"Cognition","score":0.08493316173553467}],"concepts":[{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.8683377504348755},{"id":"https://openalex.org/C2778476105","wikidata":"https://www.wikidata.org/wiki/Q628539","display_name":"Workload","level":2,"score":0.6846016049385071},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5869737863540649},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5390858054161072},{"id":"https://openalex.org/C130796691","wikidata":"https://www.wikidata.org/wiki/Q750537","display_name":"Functional near-infrared spectroscopy","level":4,"score":0.49409177899360657},{"id":"https://openalex.org/C199163554","wikidata":"https://www.wikidata.org/wiki/Q1681619","display_name":"Univariate","level":3,"score":0.48777610063552856},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4617585837841034},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.2978084683418274},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2801503539085388},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.20495426654815674},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.133195698261261},{"id":"https://openalex.org/C169900460","wikidata":"https://www.wikidata.org/wiki/Q2200417","display_name":"Cognition","level":2,"score":0.08493316173553467},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C2781195155","wikidata":"https://www.wikidata.org/wiki/Q18680","display_name":"Prefrontal cortex","level":3,"score":0.0}],"mesh":[{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D001921","descriptor_name":"Brain","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D001921","descriptor_name":"Brain","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D001921","descriptor_name":"Brain","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D004569","descriptor_name":"Electroencephalography","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D004569","descriptor_name":"Electroencephalography","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D004569","descriptor_name":"Electroencephalography","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D010108","descriptor_name":"Oxyhemoglobins","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D010108","descriptor_name":"Oxyhemoglobins","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D010108","descriptor_name":"Oxyhemoglobins","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D019265","descriptor_name":"Spectroscopy, Near-Infrared","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":true},{"descriptor_ui":"D019265","descriptor_name":"Spectroscopy, Near-Infrared","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":true},{"descriptor_ui":"D019265","descriptor_name":"Spectroscopy, Near-Infrared","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":true}],"locations_count":7,"locations":[{"id":"doi:10.3390/s22197623","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s22197623","pdf_url":"https://www.mdpi.com/1424-8220/22/19/7623/pdf?version=1665232458","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},{"id":"pmid:36236725","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/36236725","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors (Basel, Switzerland)","raw_type":null},{"id":"pmh:oai:doaj.org/article:1dcdb59688b14f7987a4d0f041781556","is_oa":true,"landing_page_url":"https://doaj.org/article/1dcdb59688b14f7987a4d0f041781556","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors, Vol 22, Iss 19, p 7623 (2022)","raw_type":"article"},{"id":"pmh:oai:dspace.lib.cranfield.ac.uk:1826/18554","is_oa":true,"landing_page_url":"https://dspace.lib.cranfield.ac.uk/handle/1826/18554","pdf_url":null,"source":{"id":"https://openalex.org/S4306401778","display_name":"CERES (Cranfield University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I82284825","host_organization_name":"Cranfield University","host_organization_lineage":["https://openalex.org/I82284825"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Article"},{"id":"pmh:oai:mdpi.com:/1424-8220/22/19/7623/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/s22197623","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors; Volume 22; Issue 19; Pages: 7623","raw_type":"Text"},{"id":"pmh:oai:pubmedcentral.nih.gov:9571712","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/9571712","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors (Basel)","raw_type":"Text"},{"id":"pmh:oai:pure.atira.dk:publications/505ae0b5-a90c-4874-ac3d-2abf4caef8b3","is_oa":true,"landing_page_url":"https://research.birmingham.ac.uk/en/publications/505ae0b5-a90c-4874-ac3d-2abf4caef8b3","pdf_url":null,"source":{"id":"https://openalex.org/S4306402634","display_name":"University of Birmingham Research Portal (University of Birmingham)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I79619799","host_organization_name":"University of Birmingham","host_organization_lineage":["https://openalex.org/I79619799"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Cao , J , Garro , E M & Zhao , Y 2022 , ' EEG/fNIRS Based Workload Classification Using Functional Brain Connectivity and Machine Learning ' , Sensors , vol. 22 , no. 19 , 7623 . https://doi.org/10.3390/s22197623","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/s22197623","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s22197623","pdf_url":"https://www.mdpi.com/1424-8220/22/19/7623/pdf?version=1665232458","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.46000000834465027,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4303982382.pdf","grobid_xml":"https://content.openalex.org/works/W4303982382.grobid-xml"},"referenced_works_count":45,"referenced_works":["https://openalex.org/W1969715890","https://openalex.org/W1974893143","https://openalex.org/W2007333865","https://openalex.org/W2054845099","https://openalex.org/W2098226206","https://openalex.org/W2122311608","https://openalex.org/W2127714370","https://openalex.org/W2168763307","https://openalex.org/W2530555723","https://openalex.org/W2533322963","https://openalex.org/W2533651630","https://openalex.org/W2593144425","https://openalex.org/W2603958320","https://openalex.org/W2606337139","https://openalex.org/W2606574754","https://openalex.org/W2734549536","https://openalex.org/W2751761087","https://openalex.org/W2782501049","https://openalex.org/W2794345050","https://openalex.org/W2801360775","https://openalex.org/W2886301707","https://openalex.org/W2901610515","https://openalex.org/W2951179062","https://openalex.org/W2975948128","https://openalex.org/W2976887998","https://openalex.org/W2978496518","https://openalex.org/W2979650466","https://openalex.org/W2991283300","https://openalex.org/W2994501102","https://openalex.org/W3081651508","https://openalex.org/W3090929564","https://openalex.org/W3113890127","https://openalex.org/W3135582951","https://openalex.org/W3138535131","https://openalex.org/W3154116618","https://openalex.org/W3158453422","https://openalex.org/W3159137383","https://openalex.org/W3164347092","https://openalex.org/W3178835531","https://openalex.org/W3185411607","https://openalex.org/W3206678152","https://openalex.org/W3213515642","https://openalex.org/W4210737249","https://openalex.org/W4224303284","https://openalex.org/W6782517708"],"related_works":["https://openalex.org/W1828158523","https://openalex.org/W2047547195","https://openalex.org/W986318368","https://openalex.org/W2000785801","https://openalex.org/W204175656","https://openalex.org/W2384410913","https://openalex.org/W1993992974","https://openalex.org/W2352878646","https://openalex.org/W2990194547","https://openalex.org/W1512294453"],"abstract_inverted_index":{"There":[0],"is":[1,190],"high":[2],"demand":[3],"for":[4,14,77,132,138,156,186,199],"techniques":[5],"to":[6,33,60,178],"estimate":[7],"human":[8],"mental":[9,64,184],"workload":[10,65,185],"during":[11],"some":[12],"activities":[13],"productivity":[15],"enhancement":[16],"or":[17],"accident":[18],"prevention.":[19],"Most":[20],"studies":[21],"focus":[22],"on":[23,47],"a":[24,42,129,143,161],"single":[25],"physiological":[26],"sensing":[27],"modality":[28],"and":[29,92,104,115,118,136,148,158,167,188,210],"use":[30],"univariate":[31,72],"methods":[32],"analyse":[34],"multi-channel":[35],"electroencephalography":[36],"(EEG)":[37],"data.":[38],"This":[39],"paper":[40],"proposes":[41],"new":[43],"framework":[44],"that":[45,152,174],"relies":[46],"the":[48,70,90,109,112,121,153,164,175,180,183,193,197,200,207,215],"features":[49,59,88],"of":[50,69,95,111,182],"hybrid":[51],"EEG-functional":[52],"near-infrared":[53],"spectroscopy":[54],"(EEG-fNIRS),":[55],"supported":[56],"by":[57],"machine-learning":[58],"deal":[61],"with":[62],"multi-level":[63],"classification.":[66],"Furthermore,":[67],"instead":[68],"well-used":[71],"power":[73],"spectral":[74],"density":[75],"(PSD)":[76],"EEG":[78,157,187,205],"recording,":[79],"we":[80],"propose":[81],"using":[82,142],"bivariate":[83],"functional":[84],"brain":[85],"connectivity":[86],"(FBC)":[87],"in":[89,206,214],"time":[91],"frequency":[93],"domains":[94],"three":[96],"bands:":[97],"delta":[98],"(0.5-4":[99],"Hz),":[100],"theta":[101],"(4-7":[102],"Hz)":[103],"alpha":[105,208],"(8-15":[106],"Hz).":[107],"With":[108],"assistance":[110],"fNIRS":[113,159,189,211],"oxyhemoglobin":[114],"deoxyhemoglobin":[116],"(HbO":[117],"HbR)":[119],"indicators,":[120],"FBC":[122],"technique":[123],"significantly":[124],"improved":[125],"classification":[126],"performance":[127],"at":[128],"77%":[130],"accuracy":[131],"0-back":[133,139],"vs.":[134,140],"2-back":[135,166],"83%":[137],"3-back":[141,168],"public":[144],"dataset.":[145],"Moreover,":[146],"topographic":[147],"heat-map":[149],"visualisation":[150],"indicated":[151],"distinguishing":[154],"regions":[155],"showed":[160],"difference":[162],"among":[163],"0-back,":[165],"test":[169],"results.":[170],"It":[171],"was":[172],"determined":[173],"best":[176,198],"region":[177,218],"assist":[179],"discrimination":[181],"different.":[191],"Specifically,":[192],"posterior":[194,201],"area":[195],"performed":[196],"midline":[202],"occipital":[203],"(POz)":[204],"band":[209],"had":[212],"superiority":[213],"right":[216],"frontal":[217],"(AF8).":[219]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":19},{"year":2024,"cited_by_count":21},{"year":2023,"cited_by_count":11}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}