{"id":"https://openalex.org/W4363648965","doi":"https://doi.org/10.1109/ciss56502.2023.10089662","title":"Decoding EEG Signals with Visibility Graphs to Predict Varying Levels of Mental Workload","display_name":"Decoding EEG Signals with Visibility Graphs to Predict Varying Levels of Mental Workload","publication_year":2023,"publication_date":"2023-03-22","ids":{"openalex":"https://openalex.org/W4363648965","doi":"https://doi.org/10.1109/ciss56502.2023.10089662"},"language":"en","primary_location":{"id":"doi:10.1109/ciss56502.2023.10089662","doi":"https://doi.org/10.1109/ciss56502.2023.10089662","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ciss56502.2023.10089662","pdf_url":null,"source":{"id":"https://openalex.org/S4306418066","display_name":"Conference on Information Sciences and Systems","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 57th Annual Conference on Information Sciences and Systems (CISS)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"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/A5005595499","display_name":"Arya Teymourlouei","orcid":"https://orcid.org/0000-0002-0102-5694","observed_orcids":["https://orcid.org/0000-0002-0102-5694"]},"institutions":[{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Arya Teymourlouei","raw_affiliation_strings":["University of Maryland,Department of Computer Science,College Park,USA","Department of Computer Science, University of Maryland, College Park, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Maryland,Department of Computer Science,College Park,USA","institution_ids":["https://openalex.org/I66946132"]},{"raw_affiliation_string":"Department of Computer Science, University of Maryland, College Park, USA","institution_ids":["https://openalex.org/I66946132"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076437195","display_name":"Rodolphe J. Gentili","orcid":"https://orcid.org/0000-0001-9171-8525","observed_orcids":["https://orcid.org/0000-0001-9171-8525"]},"institutions":[{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rodolphe J. Gentili","raw_affiliation_strings":["University of Maryland,Department of Kinesiology,College Park,USA","Department of Kinesiology, University of Maryland, College Park, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Maryland,Department of Kinesiology,College Park,USA","institution_ids":["https://openalex.org/I66946132"]},{"raw_affiliation_string":"Department of Kinesiology, University of Maryland, College Park, USA","institution_ids":["https://openalex.org/I66946132"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5050009555","display_name":"James A. Reggia","orcid":"https://orcid.org/0009-0003-8517-8405","observed_orcids":["https://orcid.org/0009-0003-8517-8405"]},"institutions":[{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"James Reggia","raw_affiliation_strings":["University of Maryland,Department of Computer Science,College Park,USA","Department of Computer Science, University of Maryland, College Park, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Maryland,Department of Computer Science,College Park,USA","institution_ids":["https://openalex.org/I66946132"]},{"raw_affiliation_string":"Department of Computer Science, University of Maryland, College Park, USA","institution_ids":["https://openalex.org/I66946132"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I66946132"],"apc_list":null,"apc_paid":null,"fwci":4.0528,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.9379361,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9800000190734863,"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":0.9800000190734863,"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/T10667","display_name":"Emotion and Mood Recognition","score":0.010200000368058681,"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"}},{"id":"https://openalex.org/T10525","display_name":"Human-Automation Interaction and Safety","score":0.0017000000225380063,"subfield":{"id":"https://openalex.org/subfields/3307","display_name":"Human Factors and Ergonomics"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/eeg-signals","display_name":"EEG signals","score":0.8289999961853027},{"id":"https://openalex.org/keywords/visibility-graph","display_name":"visibility graph","score":0.9380000233650208},{"id":"https://openalex.org/keywords/horizontal-visibility-graph","display_name":"horizontal visibility graph","score":0.9990000128746033},{"id":"https://openalex.org/keywords/graph-features","display_name":"graph features","score":0.8840000033378601},{"id":"https://openalex.org/keywords/frequency-band-decomposition","display_name":"frequency band decomposition","score":0.7620000243186951},{"id":"https://openalex.org/keywords/brain-computer-interface","display_name":"brain-computer interface","score":0.8370000123977661},{"id":"https://openalex.org/keywords/feature-selection","display_name":"feature selection","score":0.6169999837875366},{"id":"https://openalex.org/keywords/random-forest","display_name":"random forest","score":0.8050000071525574},{"id":"https://openalex.org/keywords/neural-network-classification","display_name":"neural network classification","score":0.765999972820282}],"concepts":[{"id":"https://openalex.org/C173362246","wikidata":"https://www.wikidata.org/wiki/Q8216024","display_name":"Visibility graph","level":3,"score":0.9266979694366455},{"id":"https://openalex.org/C2778476105","wikidata":"https://www.wikidata.org/wiki/Q628539","display_name":"Workload","level":2,"score":0.8394274711608887},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7250486016273499},{"id":"https://openalex.org/C123403432","wikidata":"https://www.wikidata.org/wiki/Q654068","display_name":"Visibility","level":2,"score":0.694390058517456},{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.6676506400108337},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5304999351501465},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5157414078712463},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5143364667892456},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.4957464635372162},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.440306156873703},{"id":"https://openalex.org/C19118579","wikidata":"https://www.wikidata.org/wiki/Q786423","display_name":"Frequency domain","level":2,"score":0.4217385947704315},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33737874031066895},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.288196861743927},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.20871615409851074},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.14032554626464844},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.13373494148254395},{"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C112680207","wikidata":"https://www.wikidata.org/wiki/Q714886","display_name":"Regular polygon","level":2,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","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},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ciss56502.2023.10089662","doi":"https://doi.org/10.1109/ciss56502.2023.10089662","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ciss56502.2023.10089662","pdf_url":null,"source":{"id":"https://openalex.org/S4306418066","display_name":"Conference on Information Sciences and Systems","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 57th Annual Conference on Information Sciences and Systems (CISS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being","score":0.428600013256073}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1974883811","https://openalex.org/W1980579221","https://openalex.org/W2011301426","https://openalex.org/W2040956707","https://openalex.org/W2055538060","https://openalex.org/W2076883103","https://openalex.org/W2093433505","https://openalex.org/W2101234009","https://openalex.org/W2112090702","https://openalex.org/W2116759966","https://openalex.org/W2132022337","https://openalex.org/W2149055390","https://openalex.org/W2152905082","https://openalex.org/W2165702601","https://openalex.org/W2169528473","https://openalex.org/W2894692871","https://openalex.org/W2954149050","https://openalex.org/W3092543291","https://openalex.org/W3106826216","https://openalex.org/W3127442980"],"related_works":["https://openalex.org/W7221390055","https://openalex.org/W4285340564","https://openalex.org/W4394984563","https://openalex.org/W2807173017","https://openalex.org/W7201996090","https://openalex.org/W7110649378","https://openalex.org/W3127442980","https://openalex.org/W4413087706","https://openalex.org/W4414272024","https://openalex.org/W7167708405"],"abstract_inverted_index":{"Recent":[0],"work":[1],"in":[2,13],"predicting":[3],"mental":[4,28,85,148],"workload":[5],"through":[6],"EEG":[7,37,170],"analysis":[8,131],"has":[9,126],"centered":[10],"around":[11],"features":[12,19,71,95],"the":[14,116,123,151,161,174],"frequency":[15,41,135],"domain.":[16],"However,":[17],"these":[18],"alone":[20],"may":[21],"not":[22],"be":[23],"enough":[24],"to":[25,77,101,144],"accurately":[26],"predict":[27],"workload.":[29,86,149],"We":[30],"propose":[31],"a":[32,141],"graph-based":[33,70],"approach":[34],"that":[35,133],"filters":[36],"channels":[38],"into":[39,52],"five":[40],"bands.":[42],"The":[43,58,94,113,129],"time":[44],"series":[45],"data":[46,100],"for":[47],"each":[48],"band":[49],"is":[50,89,120],"transformed":[51],"two":[53,102],"types":[54],"of":[55,81,115,147,167,176],"visibility":[56,60,64,153],"graphs.":[57],"natural":[59,152],"graph":[61,65,154],"and":[62,83,110,122],"horizontal":[63],"algorithms":[66,105],"are":[67,72,96,107],"used.":[68],"Six":[69],"then":[73],"calculated":[74],"which":[75,106],"seek":[76],"distinguish":[78],"between":[79],"EEGs":[80],"low":[82],"high":[84],"Feature":[87],"selection":[88],"evaluated":[90],"with":[91],"statistical":[92],"tests.":[93],"fed":[97],"as":[98],"input":[99],"machine":[103],"learning":[104],"random":[108,117],"forest":[109,118],"neural":[111,124],"network.":[112],"accuracy":[114],"method":[119,162],"90%,":[121],"network":[125],"86%":[127],"accuracy.":[128],"graphical":[130],"showed":[132],"higher":[134],"ranges":[136],"(alpha,":[137],"beta,":[138],"gamma)":[139],"had":[140,156],"stronger":[142],"ability":[143],"classify":[145],"levels":[146],"Unexpectedly,":[150],"algorithm":[155],"better":[157],"overall":[158],"performance.":[159],"Using":[160],"presented":[163],"here,":[164],"accurate":[165],"classification":[166],"MWL":[168],"using":[169],"signals":[171],"can":[172],"enable":[173],"development":[175],"robust":[177],"BCI.":[178]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":5}],"updated_date":"2026-10-14T16:39:01.820549","created_date":"2023-04-11T00:00:00"}