{"id":"https://openalex.org/W4410358708","doi":"https://doi.org/10.1109/access.2025.3570134","title":"Monte Carlo-Based Strategy for Assessing the Impact of EEG Data Uncertainty on Confidence in Convolutional Neural Network Classification","display_name":"Monte Carlo-Based Strategy for Assessing the Impact of EEG Data Uncertainty on Confidence in Convolutional Neural Network Classification","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4410358708","doi":"https://doi.org/10.1109/access.2025.3570134"},"language":"en","primary_location":{"id":"doi:10.1109/access.2025.3570134","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3570134","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2025.3570134","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5094198380","display_name":"Pierre Sedi Nzakuna","orcid":"https://orcid.org/0009-0005-1799-4667"},"institutions":[{"id":"https://openalex.org/I131729948","display_name":"University of Salerno","ror":"https://ror.org/0192m2k53","country_code":"IT","type":"education","lineage":["https://openalex.org/I131729948"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Pierre Sedi Nzakuna","raw_affiliation_strings":["Department of Industrial Engineering, University of Salerno, Fisciano, Italy","Dpt. of Industrial Engineering, University of Salerno, Fisciano, Italy"],"raw_orcid":"https://orcid.org/0009-0005-1799-4667","affiliations":[{"raw_affiliation_string":"Department of Industrial Engineering, University of Salerno, Fisciano, Italy","institution_ids":["https://openalex.org/I131729948"]},{"raw_affiliation_string":"Dpt. of Industrial Engineering, University of Salerno, Fisciano, Italy","institution_ids":["https://openalex.org/I131729948"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034503288","display_name":"Vincenzo Gallo","orcid":"https://orcid.org/0000-0002-9873-3784"},"institutions":[{"id":"https://openalex.org/I131729948","display_name":"University of Salerno","ror":"https://ror.org/0192m2k53","country_code":"IT","type":"education","lineage":["https://openalex.org/I131729948"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Vincenzo Gallo","raw_affiliation_strings":["Department of Industrial Engineering, University of Salerno, Fisciano, Italy","Dpt. of Industrial Engineering, University of Salerno, Fisciano, Italy"],"raw_orcid":"https://orcid.org/0000-0002-9873-3784","affiliations":[{"raw_affiliation_string":"Department of Industrial Engineering, University of Salerno, Fisciano, Italy","institution_ids":["https://openalex.org/I131729948"]},{"raw_affiliation_string":"Dpt. of Industrial Engineering, University of Salerno, Fisciano, Italy","institution_ids":["https://openalex.org/I131729948"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077842218","display_name":"Vincenzo Paciello","orcid":"https://orcid.org/0000-0002-9284-1741"},"institutions":[{"id":"https://openalex.org/I131729948","display_name":"University of Salerno","ror":"https://ror.org/0192m2k53","country_code":"IT","type":"education","lineage":["https://openalex.org/I131729948"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Vincenzo Paciello","raw_affiliation_strings":["Department of Industrial Engineering, University of Salerno, Fisciano, Italy","Dpt. of Industrial Engineering, University of Salerno, Fisciano, Italy"],"raw_orcid":"https://orcid.org/0000-0002-9284-1741","affiliations":[{"raw_affiliation_string":"Department of Industrial Engineering, University of Salerno, Fisciano, Italy","institution_ids":["https://openalex.org/I131729948"]},{"raw_affiliation_string":"Dpt. of Industrial Engineering, University of Salerno, Fisciano, Italy","institution_ids":["https://openalex.org/I131729948"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021330369","display_name":"A. Lay-Ekuakille","orcid":"https://orcid.org/0000-0002-1762-419X"},"institutions":[{"id":"https://openalex.org/I142910587","display_name":"University of Salento","ror":"https://ror.org/03fc1k060","country_code":"IT","type":"education","lineage":["https://openalex.org/I142910587"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Aim\u00e9 Lay-Ekuakille","raw_affiliation_strings":["Department of Engineering for Innovation, University of Salento, Lecce, Italy","Dpt. of Engineering for Innovation, University of Salento, Lecce, Italy"],"raw_orcid":"https://orcid.org/0000-0002-1762-419X","affiliations":[{"raw_affiliation_string":"Department of Engineering for Innovation, University of Salento, Lecce, Italy","institution_ids":["https://openalex.org/I142910587"]},{"raw_affiliation_string":"Dpt. of Engineering for Innovation, University of Salento, Lecce, Italy","institution_ids":["https://openalex.org/I142910587"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5117414632","display_name":"Angelo Kuti Lusala","orcid":"https://orcid.org/0009-0007-2177-0185"},"institutions":[{"id":"https://openalex.org/I34942010","display_name":"University of Kinshasa","ror":"https://ror.org/05rrz2q74","country_code":"CD","type":"education","lineage":["https://openalex.org/I34942010"]}],"countries":["CD"],"is_corresponding":false,"raw_author_name":"Angelo Kuti Lusala","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Polytechnic Faculty, University of Kinshasa, Kinshasa, Democratic Republic of the Congo","Dpt. of Electrical and Computer Engineering, Polytechnic Faculty, University of Kinshasa, Kinshasa, DR Congo"],"raw_orcid":"https://orcid.org/0009-0007-2177-0185","affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Polytechnic Faculty, University of Kinshasa, Kinshasa, Democratic Republic of the Congo","institution_ids":["https://openalex.org/I34942010"]},{"raw_affiliation_string":"Dpt. of Electrical and Computer Engineering, Polytechnic Faculty, University of Kinshasa, Kinshasa, DR Congo","institution_ids":["https://openalex.org/I34942010"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":3.5793,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.92576761,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":"13","issue":null,"first_page":"85342","last_page":"85362"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.8291000127792358,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.8291000127792358,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.7735000252723694,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.7440999746322632,"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/computer-science","display_name":"Computer science","score":0.7527666091918945},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7434712648391724},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.688780665397644},{"id":"https://openalex.org/keywords/electroencephalography","display_name":"Electroencephalography","score":0.5671131610870361},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.479190468788147},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4115540087223053},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.35131174325942993},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.23454052209854126},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10079935193061829}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7527666091918945},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7434712648391724},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.688780665397644},{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.5671131610870361},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.479190468788147},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4115540087223053},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35131174325942993},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.23454052209854126},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10079935193061829},{"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":2,"locations":[{"id":"doi:10.1109/access.2025.3570134","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3570134","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:60b3e6b0872749968123286abf46c37f","is_oa":true,"landing_page_url":"https://doaj.org/article/60b3e6b0872749968123286abf46c37f","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 13, Pp 85342-85362 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2025.3570134","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3570134","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":56,"referenced_works":["https://openalex.org/W2002816599","https://openalex.org/W2037725700","https://openalex.org/W2046107991","https://openalex.org/W2060472216","https://openalex.org/W2095526364","https://openalex.org/W2154526105","https://openalex.org/W2559463885","https://openalex.org/W2602768412","https://openalex.org/W2809316095","https://openalex.org/W2902034646","https://openalex.org/W2924703003","https://openalex.org/W2944382864","https://openalex.org/W2963355311","https://openalex.org/W2963647337","https://openalex.org/W2971850532","https://openalex.org/W2979626876","https://openalex.org/W2982640928","https://openalex.org/W2996790460","https://openalex.org/W3027379093","https://openalex.org/W3112695512","https://openalex.org/W3138511122","https://openalex.org/W3164103035","https://openalex.org/W3173689850","https://openalex.org/W3182491428","https://openalex.org/W3194020089","https://openalex.org/W4211174509","https://openalex.org/W4224284782","https://openalex.org/W4285282726","https://openalex.org/W4286587846","https://openalex.org/W4304758569","https://openalex.org/W4307498816","https://openalex.org/W4310263662","https://openalex.org/W4362580518","https://openalex.org/W4377197393","https://openalex.org/W4378879456","https://openalex.org/W4381336006","https://openalex.org/W4387681954","https://openalex.org/W4388087198","https://openalex.org/W4388505404","https://openalex.org/W4389610258","https://openalex.org/W4391423117","https://openalex.org/W4391617522","https://openalex.org/W4393941987","https://openalex.org/W4399849687","https://openalex.org/W4400114720","https://openalex.org/W4403784862","https://openalex.org/W4404238471","https://openalex.org/W4404313184","https://openalex.org/W4405488674","https://openalex.org/W4407839009","https://openalex.org/W6730042731","https://openalex.org/W6797028469","https://openalex.org/W6842672040","https://openalex.org/W6843595992","https://openalex.org/W6882528334","https://openalex.org/W6963588625"],"related_works":["https://openalex.org/W2922348724","https://openalex.org/W200322357","https://openalex.org/W2130428257","https://openalex.org/W4308951944","https://openalex.org/W2057366091","https://openalex.org/W4312960290","https://openalex.org/W2049513647","https://openalex.org/W2988848585","https://openalex.org/W4233722919","https://openalex.org/W4323929055"],"abstract_inverted_index":{"Electroencephalography":[0],"(EEG)":[1],"data":[2],"acquisition":[3,88],"process":[4],"in":[5,17,96,103,151,160,195,245],"Brain-Computer":[6],"Interfaces":[7],"(BCIs)":[8],"is":[9,167],"inevitably":[10],"affected":[11],"by":[12,202,208],"uncertainty":[13,45],"which":[14,133],"introduces":[15],"variability":[16,235],"the":[18,25,37,51,55,65,112,117,156,161,173,196,231],"data.":[19],"This":[20,34],"variability,":[21,171],"often":[22],"over-looked,":[23],"affects":[24],"training":[26],"and":[27,99,130,149,207,243],"testing":[28],"of":[29,39,54,114,120,139,158,163,233],"neural":[30],"network":[31],"(NN)":[32],"models.":[33],"study":[35],"evaluates":[36],"impact":[38,232],"systematic":[40,221],"bias":[41],"(\u00b12%)":[42],"plus":[43],"aleatoric":[44,215],"(2\u20135%":[46],"random":[47],"Gaussian":[48],"perturbations)":[49],"on":[50,236],"classification":[52,104],"confidence":[53,102,148,177,200],"EEGNet":[56,159,188],"model":[57,101,140,174,199],"for":[58,178,204,210,229],"four":[59],"class-Motor":[60],"Imagery":[61],"(MI)":[62],"tasks":[63],"using":[64],"BCI":[66,238,247],"Competition":[67],"IV":[68],"2a":[69],"dataset.":[70],"Through":[71],"two":[72],"Monte":[73],"Carlo":[74],"simulations":[75],"with":[76,172,198],"100":[77],"iterations":[78],"each,":[79],"perturbed":[80],"datasets":[81],"were":[82],"generated":[83],"to":[84,93,169,184,191],"mimic":[85],"real-world":[86],"EEG":[87],"uncertainties.":[89],"Softmax":[90],"outputs":[91],"served":[92],"analyze":[94],"overlap":[95,128],"predicted":[97],"probabilities":[98],"quantify":[100],"decisions.":[105],"We":[106],"introduce":[107],"robust":[108],"evaluation":[109],"metrics,":[110],"including":[111],"proportion":[113],"area":[115],"under":[116],"curve":[118],"(AUC)":[119],"probability":[121],"density":[122],"functions":[123],"(PDFs)":[124],"\u2265":[125],"70%":[126],"accuracy,":[127],"coefficients,":[129],"percentile-based":[131],"thresholding,":[132],"provide":[134],"a":[135,226],"more":[136],"comprehensive":[137],"assessment":[138],"performance,":[141],"capturing":[142],"not":[143],"only":[144],"accuracy":[145],"but":[146],"also":[147],"ambiguity":[150],"predictions.":[152],"Results":[153],"show":[154],"that":[155,214],"robustness":[157],"face":[162],"realistic":[164],"measurement":[165,234],"uncertainties":[166],"prone":[168],"inter-subject":[170],"achieving":[175],"higher":[176],"Subject":[179,185,205,211],"1":[180,206],"(average":[181],"90.52%)":[182],"compared":[183],"2":[186],"(62.96%).":[187],"demonstrates":[189],"resilience":[190],"directional":[192],"calibration":[193],"shifts":[194],"data,":[197],"varying":[201],"0.22%":[203],"6.24%":[209],"2,":[212],"showing":[213],"precision":[216],"errors":[217],"dominate":[218],"over":[219],"small":[220],"shifts.":[222],"Our":[223],"approach":[224],"provides":[225],"rigorous":[227],"framework":[228],"quantifying":[230],"EEG-based":[237],"classification,":[239],"thereby":[240],"enhancing":[241],"reliability":[242],"generalizability":[244],"practical":[246],"deployments.":[248]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3}],"updated_date":"2026-06-13T07:54:00.901334","created_date":"2025-10-10T00:00:00"}
