{"id":"https://openalex.org/W7160264302","doi":"https://doi.org/10.5753/sbsi.2026.248345","title":"Evaluating Robustness and Detection of Adversarial Attacks in EEG-Based Brain-Computer Interfaces","display_name":"Evaluating Robustness and Detection of Adversarial Attacks in EEG-Based Brain-Computer Interfaces","publication_year":2026,"publication_date":"2026-05-05","ids":{"openalex":"https://openalex.org/W7160264302","doi":"https://doi.org/10.5753/sbsi.2026.248345"},"language":null,"primary_location":{"id":"doi:10.5753/sbsi.2026.248345","is_oa":true,"landing_page_url":"https://doi.org/10.5753/sbsi.2026.248345","pdf_url":"https://sol.sbc.org.br/index.php/sbsi/article/download/41328/41098","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Anais do XXII Simp\u00f3sio Brasileiro de Sistemas de Informa\u00e7\u00e3o (SBSI 2026)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://sol.sbc.org.br/index.php/sbsi/article/download/41328/41098","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5126763144","display_name":"Beatriz C. da Costa","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Beatriz C. da Costa","raw_affiliation_strings":["FURG"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"FURG","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048839201","display_name":"Andr\u00e9 Riker","orcid":"https://orcid.org/0000-0002-6594-8893"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Andr\u00e9 Riker","raw_affiliation_strings":["UFPA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"UFPA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135301142","display_name":"Roger Immich","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Roger Immich","raw_affiliation_strings":["UFRN"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"UFRN","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5135344333","display_name":"Bruno L. Dalmazo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bruno L. Dalmazo","raw_affiliation_strings":["FURG"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"FURG","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.4533195,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"292","last_page":"309"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.6952999830245972,"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"}},"topics":[{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.6952999830245972,"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/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.186599999666214,"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/T11800","display_name":"User Authentication and Security Systems","score":0.01549999974668026,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/adversarial-system","display_name":"Adversarial system","score":0.8608999848365784},{"id":"https://openalex.org/keywords/adversarial-machine-learning","display_name":"Adversarial machine learning","score":0.6687999963760376},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6489999890327454},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6316999793052673},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5449000000953674},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.5228000283241272}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.8608999848365784},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7211999893188477},{"id":"https://openalex.org/C2778403875","wikidata":"https://www.wikidata.org/wiki/Q20312394","display_name":"Adversarial machine learning","level":3,"score":0.6687999963760376},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6489999890327454},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6316999793052673},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5999000072479248},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5519000291824341},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5449000000953674},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5228000283241272},{"id":"https://openalex.org/C173201364","wikidata":"https://www.wikidata.org/wiki/Q897410","display_name":"Brain\u2013computer interface","level":3,"score":0.45989999175071716},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.3395000100135803},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.28850001096725464},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.28279998898506165},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.26579999923706055},{"id":"https://openalex.org/C115051666","wikidata":"https://www.wikidata.org/wiki/Q6522493","display_name":"Ranging","level":2,"score":0.25619998574256897}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.5753/sbsi.2026.248345","is_oa":true,"landing_page_url":"https://doi.org/10.5753/sbsi.2026.248345","pdf_url":"https://sol.sbc.org.br/index.php/sbsi/article/download/41328/41098","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Anais do XXII Simp\u00f3sio Brasileiro de Sistemas de Informa\u00e7\u00e3o (SBSI 2026)","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.5753/sbsi.2026.248345","is_oa":true,"landing_page_url":"https://doi.org/10.5753/sbsi.2026.248345","pdf_url":"https://sol.sbc.org.br/index.php/sbsi/article/download/41328/41098","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Anais do XXII Simp\u00f3sio Brasileiro de Sistemas de Informa\u00e7\u00e3o (SBSI 2026)","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7160264302.pdf","grobid_xml":"https://content.openalex.org/works/W7160264302.grobid-xml"},"referenced_works_count":25,"referenced_works":["https://openalex.org/W1998510843","https://openalex.org/W2030611346","https://openalex.org/W2125244310","https://openalex.org/W2151298633","https://openalex.org/W2243397390","https://openalex.org/W2519532976","https://openalex.org/W2559463885","https://openalex.org/W2623427976","https://openalex.org/W2791319131","https://openalex.org/W2808392682","https://openalex.org/W2963857521","https://openalex.org/W3008533131","https://openalex.org/W3088909400","https://openalex.org/W3120110115","https://openalex.org/W3162031981","https://openalex.org/W4312318241","https://openalex.org/W4380033540","https://openalex.org/W4385695888","https://openalex.org/W4390187667","https://openalex.org/W4390497390","https://openalex.org/W4391306511","https://openalex.org/W4396574809","https://openalex.org/W4402983418","https://openalex.org/W4402984724","https://openalex.org/W4403595956"],"related_works":[],"abstract_inverted_index":{"Research":[0,161],"Context:":[1],"Brain-computer":[2],"interfaces":[3],"(BCI)":[4],"are":[5,38,77],"systems":[6,280],"that":[7],"capture":[8],"brain":[9],"signals":[10,19],"through":[11],"techniques":[12],"such":[13,133],"as":[14,134],"electroencephalography":[15],"(EEG),":[16],"processing":[17,159],"these":[18,65,96],"for":[20,29,225,233,260,278],"various":[21],"applications,":[22],"especially":[23],"in":[24,75,101,117],"the":[25,35,48,55,62,125,143,167,193,236,251,265],"control":[26],"of":[27,50,64,92,95,119,146,195,199,238,253,267],"devices":[28,53],"people":[30],"with":[31,173,207,220,231,282],"motor":[32],"limitations.":[33],"Despite":[34],"benefits,":[36],"there":[37],"security":[39,63,269],"concerns,":[40],"including":[41],"adversarial":[42,80,115,131,147,261],"and":[43,87,113,138,185,189,223,243,263],"cybersecurity":[44],"attacks.":[45],"Due":[46],"to":[47,60,79,111,128,191,213,245],"emergence":[49],"brain-computer":[51],"interaction":[52],"on":[54,149,166,181],"market,":[56],"it":[57],"is":[58],"necessary":[59],"analyze":[61,114],"devices.":[66,121],"Scientific":[67],"and/or":[68,106],"Practical":[69],"Problem:":[70],"Machine":[71],"learning":[72],"classifiers":[73,118],"used":[74,124],"BCIs":[76],"vulnerable":[78],"attacks,":[81,227],"which":[82],"can":[83],"compromise":[84],"accuracy,":[85],"safety,":[86],"user":[88],"privacy.":[89],"The":[90,248],"lack":[91],"systematic":[93],"evaluation":[94,141,258],"vulnerabilities":[97,252],"represents":[98],"a":[99],"gap":[100],"current":[102],"research.":[103],"Proposed":[104],"Solution":[105],"Analysis:":[107],"This":[108],"work":[109,249],"aims":[110],"emulate":[112],"attacks":[116,148,174],"BCI":[120,168,254,272],"Our":[122,140],"experiments":[123],"Foolbox":[126],"tool":[127],"evaluate":[129],"different":[130],"techniques,":[132],"DeepFool,":[135,234],"FGSM,":[136],"PGD,":[137],"Carlini-Wagner.":[139],"identifies":[142],"negative":[144],"effects":[145],"data":[150],"classification.":[151],"Related":[152],"IS":[153,246],"Theory:":[154],"Technology":[155],"acceptance":[156],"model;":[157],"Information":[158],"theory.":[160],"Method:":[162],"Experiments":[163],"were":[164,187],"conducted":[165],"Competition":[169],"2008":[170],"Graz":[171],"dataset,":[172],"emulated":[175],"during":[176],"inference.":[177],"Detection":[178,215],"mechanisms":[179],"based":[180],"Random":[182,221],"Forest,":[183],"SVM,":[184],"KNN":[186,232],"trained":[188],"evaluated":[190],"assess":[192],"feasibility":[194],"automatic":[196],"defense.":[197],"Summary":[198],"Results:":[200],"Classifier":[201],"accuracy":[202,219],"decreased":[203],"sharply":[204],"under":[205],"attack,":[206],"success":[208],"rates":[209],"ranging":[210],"from":[211],"75.2%":[212],"100%.":[214],"models":[216],"achieved":[217],"83%":[218],"Forest":[222],"SVM":[224],"FGSM":[226],"but":[228],"only":[229],"5%":[230],"highlighting":[235],"challenge":[237],"detecting":[239],"subtle":[240],"perturbations.":[241],"Contributions":[242],"Impact":[244],"area:":[247],"demonstrates":[250],"classifiers,":[255],"proposes":[256],"an":[257],"pipeline":[259],"robustness,":[262],"emphasizes":[264],"importance":[266],"integrating":[268],"assessment":[270],"into":[271],"development.":[273],"Results":[274],"have":[275],"direct":[276],"implications":[277],"information":[279],"dealing":[281],"sensitive":[283],"biomedical":[284],"data.":[285]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2026-05-06T00:00:00"}
