{"id":"https://openalex.org/W7163988688","doi":"https://doi.org/10.1145/3748522.3779935","title":"Subject-Invariant EEG Embeddings via Mixup and Adversarial Learning for Semantic Retrieval","display_name":"Subject-Invariant EEG Embeddings via Mixup and Adversarial Learning for Semantic Retrieval","publication_year":2026,"publication_date":"2026-03-23","ids":{"openalex":"https://openalex.org/W7163988688","doi":"https://doi.org/10.1145/3748522.3779935"},"language":null,"primary_location":{"id":"doi:10.1145/3748522.3779935","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3748522.3779935","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 41st ACM/SIGAPP Symposium on Applied Computing","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3748522.3779935","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5084741729","display_name":"WooHyeok Choi","orcid":"https://orcid.org/0009-0002-1888-4281"},"institutions":[{"id":"https://openalex.org/I197347611","display_name":"Korea University","ror":"https://ror.org/047dqcg40","country_code":"KR","type":"education","lineage":["https://openalex.org/I197347611"]},{"id":"https://openalex.org/I4210161052","display_name":"Korea University","ror":"https://ror.org/05m1gnk07","country_code":"JP","type":"education","lineage":["https://openalex.org/I4210161052"]}],"countries":["JP","KR"],"is_corresponding":false,"raw_author_name":"Woohyeok Choi","raw_affiliation_strings":["korea university, Seoul, Republic of Korea"],"raw_orcid":"https://orcid.org/0009-0002-1888-4281","affiliations":[{"raw_affiliation_string":"korea university, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I197347611","https://openalex.org/I4210161052"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100606264","display_name":"Jun-Mo Kim","orcid":"https://orcid.org/0000-0002-2238-3255"},"institutions":[{"id":"https://openalex.org/I197347611","display_name":"Korea University","ror":"https://ror.org/047dqcg40","country_code":"KR","type":"education","lineage":["https://openalex.org/I197347611"]},{"id":"https://openalex.org/I4210161052","display_name":"Korea University","ror":"https://ror.org/05m1gnk07","country_code":"JP","type":"education","lineage":["https://openalex.org/I4210161052"]}],"countries":["JP","KR"],"is_corresponding":false,"raw_author_name":"Jun-Mo Kim","raw_affiliation_strings":["korea university, Seoul, Republic of Korea"],"raw_orcid":"https://orcid.org/0000-0002-2238-3255","affiliations":[{"raw_affiliation_string":"korea university, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I197347611","https://openalex.org/I4210161052"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138202574","display_name":"Songha Kim","orcid":"https://orcid.org/0009-0001-8868-9337"},"institutions":[{"id":"https://openalex.org/I197347611","display_name":"Korea University","ror":"https://ror.org/047dqcg40","country_code":"KR","type":"education","lineage":["https://openalex.org/I197347611"]},{"id":"https://openalex.org/I4210161052","display_name":"Korea University","ror":"https://ror.org/05m1gnk07","country_code":"JP","type":"education","lineage":["https://openalex.org/I4210161052"]}],"countries":["JP","KR"],"is_corresponding":false,"raw_author_name":"Songha Kim","raw_affiliation_strings":["korea university, Seoul, Republic of Korea"],"raw_orcid":"https://orcid.org/0009-0001-8868-9337","affiliations":[{"raw_affiliation_string":"korea university, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I197347611","https://openalex.org/I4210161052"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129795732","display_name":"Yebin Choi","orcid":null},"institutions":[{"id":"https://openalex.org/I197347611","display_name":"Korea University","ror":"https://ror.org/047dqcg40","country_code":"KR","type":"education","lineage":["https://openalex.org/I197347611"]},{"id":"https://openalex.org/I4210161052","display_name":"Korea University","ror":"https://ror.org/05m1gnk07","country_code":"JP","type":"education","lineage":["https://openalex.org/I4210161052"]}],"countries":["JP","KR"],"is_corresponding":false,"raw_author_name":"Yebin Choi","raw_affiliation_strings":["korea university, Seoul, Republic of Korea"],"raw_orcid":"https://orcid.org/0009-0006-3869-6957","affiliations":[{"raw_affiliation_string":"korea university, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I197347611","https://openalex.org/I4210161052"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5138271551","display_name":"Tae-Eui Kam","orcid":"https://orcid.org/0000-0002-6677-7176"},"institutions":[{"id":"https://openalex.org/I197347611","display_name":"Korea University","ror":"https://ror.org/047dqcg40","country_code":"KR","type":"education","lineage":["https://openalex.org/I197347611"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Tae-Eui Kam","raw_affiliation_strings":["Korea university, Seoul, Republic of Korea"],"raw_orcid":"https://orcid.org/0000-0002-6677-7176","affiliations":[{"raw_affiliation_string":"Korea university, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I197347611"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.94640828,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1960","last_page":"1961"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.41850000619888306,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.41850000619888306,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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":0.27570000290870667,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.035100001841783524,"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/electroencephalography","display_name":"Electroencephalography","score":0.6790000200271606},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.6456000208854675},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6104999780654907},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.5404999852180481},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.5088000297546387},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.38760000467300415},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.2971000075340271}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.757099986076355},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6965000033378601},{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.6790000200271606},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.6456000208854675},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6104999780654907},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.5404999852180481},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.5088000297546387},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4255000054836273},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.38760000467300415},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3458000123500824},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3248000144958496},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.2971000075340271},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.2935999929904938},{"id":"https://openalex.org/C1667742","wikidata":"https://www.wikidata.org/wiki/Q10927554","display_name":"Image retrieval","level":3,"score":0.2825999855995178},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.27459999918937683},{"id":"https://openalex.org/C189391414","wikidata":"https://www.wikidata.org/wiki/Q7936579","display_name":"Visual Word","level":4,"score":0.27410000562667847},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.2653999924659729},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2529999911785126}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3748522.3779935","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3748522.3779935","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 41st ACM/SIGAPP Symposium on Applied Computing","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3748522.3779935","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3748522.3779935","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 41st ACM/SIGAPP Symposium on Applied Computing","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.6101765036582947,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":2,"referenced_works":["https://openalex.org/W4309409002","https://openalex.org/W4412868759"],"related_works":[],"abstract_inverted_index":{"Decoding":[0],"visual":[1],"information":[2],"from":[3],"electroencephalography":[4],"(EEG)":[5],"has":[6],"advanced":[7],"with":[8],"contrastive":[9],"learning,":[10],"but":[11],"cross-subject":[12,24,62],"generalization":[13,83],"remains":[14],"difficult":[15],"due":[16],"to":[17,84],"strong":[18],"inter-subject":[19],"variability.":[20],"We":[21],"propose":[22],"a":[23],"EEG-to-image":[25],"retrieval":[26,63],"framework":[27],"that":[28,57],"eliminates":[29],"the":[30,53,72],"need":[31],"for":[32,80],"subject-specific":[33],"calibration":[34],"by":[35],"combining":[36],"feature-level":[37],"mixup":[38,76],"augmentation":[39],"and":[40,68,77,82],"subject-adversarial":[41,78],"learning.":[42],"This":[43],"encourages":[44],"subject-invariant":[45],"yet":[46],"semantically":[47],"meaningful":[48],"EEG":[49],"representations.":[50],"Experiments":[51],"on":[52],"THINGS-EEG":[54],"dataset":[55],"show":[56],"our":[58],"method":[59],"substantially":[60],"improves":[61],"accuracy":[64],"over":[65],"prior":[66],"approaches,":[67],"ablation":[69],"studies":[70],"confirm":[71],"complementary":[73],"benefits":[74],"of":[75],"learning":[79],"robustness":[81],"unseen":[85],"subjects.":[86]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-06-10T00:00:00"}