{"id":"https://openalex.org/W4416249349","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228971","title":"Frequency-Aware Contrastive Loss for Self-Supervised EEG Seizure Detection","display_name":"Frequency-Aware Contrastive Loss for Self-Supervised EEG Seizure Detection","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416249349","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228971"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11228971","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228971","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5030922992","display_name":"Kristina Stemikovskaya","orcid":"https://orcid.org/0000-0003-1985-7066"},"institutions":[{"id":"https://openalex.org/I4210091170","display_name":"Euskadiko Parke Teknologikoa","ror":"https://ror.org/00caq9197","country_code":"ES","type":"archive","lineage":["https://openalex.org/I4210091170"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Kristina Stemikovskaya","raw_affiliation_strings":["TECNALIA, Member of the Basque Research & Technology Alliance (BRTA),Derio, Bizkaia,Spain"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"TECNALIA, Member of the Basque Research & Technology Alliance (BRTA),Derio, Bizkaia,Spain","institution_ids":["https://openalex.org/I4210091170"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038856384","display_name":"Diana Manjarr\u00e9s","orcid":"https://orcid.org/0000-0001-5656-9042"},"institutions":[{"id":"https://openalex.org/I4210091170","display_name":"Euskadiko Parke Teknologikoa","ror":"https://ror.org/00caq9197","country_code":"ES","type":"archive","lineage":["https://openalex.org/I4210091170"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Diana Manjarr\u00e9s","raw_affiliation_strings":["TECNALIA, Member of the Basque Research & Technology Alliance (BRTA),Derio, Bizkaia,Spain"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"TECNALIA, Member of the Basque Research & Technology Alliance (BRTA),Derio, Bizkaia,Spain","institution_ids":["https://openalex.org/I4210091170"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5075541426","display_name":"Isaac Triguero","orcid":"https://orcid.org/0000-0002-0150-0651"},"institutions":[{"id":"https://openalex.org/I173304897","display_name":"Universidad de Granada","ror":"https://ror.org/04njjy449","country_code":"ES","type":"education","lineage":["https://openalex.org/I173304897"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Isaac Triguero","raw_affiliation_strings":["University of Granada,Department of Computer Science and Artificial Intelligence,Granada,Spain"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Granada,Department of Computer Science and Artificial Intelligence,Granada,Spain","institution_ids":["https://openalex.org/I173304897"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9887999892234802,"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":0.9887999892234802,"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/T10241","display_name":"Functional Brain Connectivity Studies","score":0.0019000000320374966,"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/T10094","display_name":"Epilepsy research and treatment","score":0.0010999999940395355,"subfield":{"id":"https://openalex.org/subfields/2738","display_name":"Psychiatry and Mental health"},"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/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5649999976158142},{"id":"https://openalex.org/keywords/electroencephalography","display_name":"Electroencephalography","score":0.5062000155448914},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.46860000491142273},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.42590001225471497},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.3700999915599823},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.36489999294281006},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.3386000096797943},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.33169999718666077},{"id":"https://openalex.org/keywords/frequency-domain","display_name":"Frequency domain","score":0.320499986410141}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6793000102043152},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6176999807357788},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5649999976158142},{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.5062000155448914},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.46860000491142273},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.4332999885082245},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.42590001225471497},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.3700999915599823},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.36489999294281006},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3386000096797943},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.33169999718666077},{"id":"https://openalex.org/C19118579","wikidata":"https://www.wikidata.org/wiki/Q786423","display_name":"Frequency domain","level":2,"score":0.320499986410141},{"id":"https://openalex.org/C168110828","wikidata":"https://www.wikidata.org/wiki/Q1331626","display_name":"Spectral density","level":2,"score":0.3192000091075897},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.31459999084472656},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.30559998750686646},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.3003000020980835},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2996000051498413},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.29789999127388},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.29440000653266907},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.28929999470710754},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.2782000005245209},{"id":"https://openalex.org/C2781181686","wikidata":"https://www.wikidata.org/wiki/Q4226068","display_name":"Coherence (philosophical gambling strategy)","level":2,"score":0.2712000012397766},{"id":"https://openalex.org/C103824480","wikidata":"https://www.wikidata.org/wiki/Q185889","display_name":"Time domain","level":2,"score":0.2669999897480011},{"id":"https://openalex.org/C152478114","wikidata":"https://www.wikidata.org/wiki/Q660910","display_name":"Neurophysiology","level":2,"score":0.266400009393692},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.258899986743927},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.25600001215934753},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.2556999921798706},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.2508000135421753}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11228971","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228971","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W2027927824","https://openalex.org/W2081349923","https://openalex.org/W2098659091","https://openalex.org/W2150374621","https://openalex.org/W2994411342","https://openalex.org/W3035725276","https://openalex.org/W3096655658","https://openalex.org/W3114632476","https://openalex.org/W3141849305","https://openalex.org/W3177342940","https://openalex.org/W3190152617","https://openalex.org/W3193057479","https://openalex.org/W4239510810","https://openalex.org/W4286579687","https://openalex.org/W4381327766","https://openalex.org/W4386879760","https://openalex.org/W4387129869","https://openalex.org/W4387385719","https://openalex.org/W4393095888","https://openalex.org/W4406261132"],"related_works":[],"abstract_inverted_index":{"Contrastive":[0],"Representation":[1],"Learning":[2],"(CRL),":[3],"a":[4,75,92,137,166],"sub-field":[5],"of":[6,17,30,118,187,198],"self-supervised":[7],"learning,":[8],"models":[9],"relationships":[10],"between":[11,95,153],"data":[12,37],"points":[13],"by":[14,82,136],"comparing":[15],"pairs":[16],"samples":[18,97,119],"and":[19,140,179,195],"utilizing":[20],"specifically":[21],"formulated":[22],"contrastive":[23,40,144],"pretext":[24],"tasks":[25],"in":[26,45],"which":[27,56],"multiple":[28],"views":[29],"each":[31],"input":[32],"example":[33],"are":[34,134],"generated":[35],"via":[36],"augmentation.":[38],"While":[39],"learning":[41],"has":[42],"shown":[43],"promise":[44],"EEG":[46,87],"analysis,":[47],"current":[48],"methods":[49],"typically":[50],"rely":[51],"on":[52,99,161],"random":[53],"negative":[54,80,132],"sampling,":[55],"may":[57],"not":[58],"capture":[59],"the":[60,116,143,148,162,175,188,196],"subtle":[61],"distinctions":[62],"crucial":[63],"for":[64],"seizure":[65,200],"detection.":[66],"Our":[67],"proposed":[68],"method,":[69],"Frequency-aware":[70],"NT-Xent":[71,177],"(FA-NT-Xent)":[72],"loss,":[73],"introduces":[74],"principled":[76],"approach":[77,206],"to":[78,127,151],"hard":[79,131],"selection":[81],"leveraging":[83],"domain":[84],"knowledge":[85],"from":[86],"analysis.":[88],"Specifically,":[89],"we":[90],"construct":[91],"distance":[93],"matrix":[94],"batch":[96],"based":[98],"their":[100],"spectral":[101],"band":[102],"power":[103],"profiles":[104],"across":[105],"five":[106],"canonical":[107],"frequency":[108,123],"bands":[109],"(delta,":[110],"theta,":[111],"alpha,":[112],"beta,":[113],"gamma),":[114],"enabling":[115],"identification":[117],"that":[120,203],"share":[121],"similar":[122,155],"characteristics":[124],"but":[125,156],"belong":[126],"different":[128],"classes.":[129],"These":[130],"examples":[133],"weighted":[135],"learnable":[138],"parameter":[139],"integrated":[141],"into":[142],"loss":[145,178],"computation,":[146],"enhancing":[147],"model\u2019s":[149],"ability":[150],"distinguish":[152],"physiologically":[154],"clinically":[157,209],"distinct":[158],"patterns.":[159],"Evaluated":[160],"CHB-MIT":[163],"dataset":[164],"using":[165],"cross-patient":[167,215],"validation":[168],"scheme,":[169],"our":[170,204],"method":[171],"demonstrates":[172],"improvements":[173],"over":[174],"standard":[176],"achieves":[180],"competitive":[181],"performance":[182],"against":[183],"state-of-the-art":[184],"approaches.":[185],"Visualization":[186],"learned":[189],"representations":[190],"reveals":[191],"improved":[192],"class":[193],"separation":[194],"emergence":[197],"patient-independent":[199],"characteristics,":[201],"indicating":[202],"frequency-aware":[205],"successfully":[207],"captures":[208],"relevant":[210],"features":[211],"while":[212],"maintaining":[213],"robust":[214],"generalization.":[216]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-11-14T00:00:00"}