{"id":"https://openalex.org/W4416707112","doi":"https://doi.org/10.1109/access.2025.3637315","title":"NeuroCognitor: Unified EEG-Language Framework for Cognitive Load Analysis via Instruction-Tuned Multi-Task Learning","display_name":"NeuroCognitor: Unified EEG-Language Framework for Cognitive Load Analysis via Instruction-Tuned Multi-Task Learning","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4416707112","doi":"https://doi.org/10.1109/access.2025.3637315"},"language":"en","primary_location":{"id":"doi:10.1109/access.2025.3637315","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3637315","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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.2025.3637315","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5120667112","display_name":"Miao Cong","orcid":"https://orcid.org/0009-0004-1021-8000"},"institutions":[{"id":"https://openalex.org/I184983240","display_name":"Northeast Normal University","ror":"https://ror.org/02rkvz144","country_code":"CN","type":"education","lineage":["https://openalex.org/I184983240"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Miao Cong","raw_affiliation_strings":["Academy of Fine Arts, Northeast Normal University, Changchun, Jilin, China"],"raw_orcid":"https://orcid.org/0009-0004-1021-8000","affiliations":[{"raw_affiliation_string":"Academy of Fine Arts, Northeast Normal University, Changchun, Jilin, China","institution_ids":["https://openalex.org/I184983240"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5120667112"],"corresponding_institution_ids":["https://openalex.org/I184983240"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":1.2359,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.84408287,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":98},"biblio":{"volume":"13","issue":null,"first_page":"201645","last_page":"201665"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9527999758720398,"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":0.9527999758720398,"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/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","score":0.004900000058114529,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T10667","display_name":"Emotion and Mood Recognition","score":0.0035000001080334187,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/electroencephalography","display_name":"Electroencephalography","score":0.5982000231742859},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5439000129699707},{"id":"https://openalex.org/keywords/cognitive-load","display_name":"Cognitive load","score":0.42149999737739563},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.3813000023365021},{"id":"https://openalex.org/keywords/security-token","display_name":"Security token","score":0.35499998927116394},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.3246999979019165},{"id":"https://openalex.org/keywords/cognition","display_name":"Cognition","score":0.32269999384880066},{"id":"https://openalex.org/keywords/multi-task-learning","display_name":"Multi-task learning","score":0.31520000100135803}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8159000277519226},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6248999834060669},{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.5982000231742859},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5439000129699707},{"id":"https://openalex.org/C61641136","wikidata":"https://www.wikidata.org/wiki/Q1107019","display_name":"Cognitive load","level":3,"score":0.42149999737739563},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41780000925064087},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.3813000023365021},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.35499998927116394},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.3246999979019165},{"id":"https://openalex.org/C169900460","wikidata":"https://www.wikidata.org/wiki/Q2200417","display_name":"Cognition","level":2,"score":0.32269999384880066},{"id":"https://openalex.org/C28006648","wikidata":"https://www.wikidata.org/wiki/Q6934509","display_name":"Multi-task learning","level":3,"score":0.31520000100135803},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.3098999857902527},{"id":"https://openalex.org/C50965678","wikidata":"https://www.wikidata.org/wiki/Q2724302","display_name":"Abnormality","level":2,"score":0.3093000054359436},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.2962000072002411},{"id":"https://openalex.org/C2777601683","wikidata":"https://www.wikidata.org/wiki/Q6499736","display_name":"Vocabulary","level":2,"score":0.29330000281333923},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.2874000072479248},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.2870999872684479},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.2777999937534332},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2766000032424927},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.25429999828338623},{"id":"https://openalex.org/C2777067715","wikidata":"https://www.wikidata.org/wiki/Q3327726","display_name":"Multitaper","level":2,"score":0.2535000145435333}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2025.3637315","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3637315","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:ee7e08ec0c6643c7ab049a85ae7e052e","is_oa":true,"landing_page_url":"https://doaj.org/article/ee7e08ec0c6643c7ab049a85ae7e052e","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 201645-201665 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2025.3637315","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3637315","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Cognitive":[0],"load":[1],"estimation":[2],"from":[3],"electroencephalography":[4],"(EEG)":[5],"is":[6,15,70],"pivotal":[7],"to":[8,39,45,58,227],"safety-critical":[9],"domains":[10],"where":[11],"mental":[12],"state":[13],"monitoring":[14,281],"essential,":[16],"such":[17],"as":[18,213],"healthcare,":[19],"education,":[20],"aviation,":[21],"and":[22,32,42,48,91,132,160,173,186,206,233,240,248,256,266,279],"human\u2013computer":[23],"interaction,":[24],"yet":[25],"prevailing":[26],"deep":[27],"models":[28,53],"remain":[29],"(i)":[30],"task-specific":[31],"brittle":[33],"across":[34,96,264],"datasets,":[35,155],"(ii)":[36],"weakly":[37],"aligned":[38,64],"language":[40],"semantics,":[41],"(iii)":[43],"sensitive":[44],"inter-/intra-subject":[46],"variability":[47],"noise.":[49],"Existing":[50],"EEG":[51,77,98,119,212],"foundation":[52],"also":[54],"lack":[55],"a":[56,106,135,140,149,217,223],"pathway":[57],"instruction-tuned":[59,145],"multi-task":[60,146],"reasoning":[61],"because":[62],"semantically":[63],"EEG\u2013text":[65,243],"pairs":[66],"are":[67],"scarce.":[68],"There":[69],"no":[71],"unified":[72,107],"framework":[73,110],"that":[74,111],"(a)":[75],"discretises":[76],"into":[78],"language-compatible":[79],"tokens,":[80],"(b)":[81],"aligns":[82],"their":[83],"latent":[84],"distributions":[85],"with":[86,129,139,220,258],"text":[87,221],"without":[88],"paired":[89],"supervision,":[90],"(c)":[92],"supports":[93],"instruction-driven":[94],"generalisation":[95],"heterogeneous":[97],"tasks.":[99],"To":[100],"address":[101],"these":[102],"challenges,":[103],"we":[104],"propose":[105],"EEG\u2013language":[108],"(NeuroCognitor)":[109],"(1)":[112],"performs":[113],"modality-aware":[114],"vector-quantised":[115],"tokenisation":[116],"of":[117,194],"time\u2013frequency":[118],"segments,":[120],"(2)":[121],"enforces":[122],"distributional":[123,199],"cross-modal":[124],"alignment":[125,200,244],"via":[126],"adversarial":[127],"learning":[128],"gradient":[130],"reversal,":[131],"(3)":[133],"pretrains":[134],"multi-stream":[136],"autoregressive":[137],"prior":[138],"stair-stepping":[141],"causal":[142,150,209],"mask":[143],"before":[144],"adaptation":[147],"on":[148,164,176],"LLM.":[151],"On":[152],"three":[153],"diverse":[154],"NeuroCognitor":[156],"attains":[157],"competitive":[158],"performance":[159],"strong":[161],"cross-task":[162],"generalisability:":[163],"TUAB":[165],"abnormality":[166,229],"detection,":[167,232],"NeuroCognitor-L":[168],"achieves":[169],"balanced":[170],"accuracy":[171],"0.83":[172],"AUROC":[174],"0.92;":[175],"TUEV":[177],"event":[178,231],"classification,":[179],"it":[180],"reaches":[181],"Cohen\u2019s":[182],"\u03ba":[183],"=":[184],"0.71":[185],"weighted":[187],"F1":[188],"0.88.":[189],"Ablations":[190],"confirm":[191],"the":[192,269],"benefits":[193],"codebook":[195],"size":[196],"(\u223c8,192":[197],"entries),":[198],"over":[201],"fragment-level":[202],"pairing,":[203],"tokenization":[204],"granularity,":[205],"strict":[207],"temporal":[208],"masking.":[210],"Casting":[211],"discrete":[214],"tokens":[215],"in":[216,251],"shared":[218],"vocabulary":[219],"enables":[222],"single":[224],"instruction-following":[225],"model":[226],"support":[228,278],"screening,":[230],"sleep":[234],"staging,":[235],"thereby":[236],"reducing":[237],"per-task":[238],"engineering":[239],"maintenance.":[241],"Pair-free":[242],"lowers":[245],"annotation":[246],"burden":[247],"facilitates":[249],"deployment":[250],"data-sparse":[252],"settings.":[253],"Cross-dataset":[254],"robustness":[255],"compatibility":[257],"standard":[259],"10\u201320/10\u201310":[260],"montages":[261],"improve":[262],"portability":[263],"sites":[265],"devices,":[267],"while":[268],"tokenized":[270],"interface":[271],"provides":[272],"auditable,":[273],"integration-ready":[274],"outputs":[275],"for":[276],"decision":[277],"human\u2013machine":[280],"workflows.":[282]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-11-27T00:00:00"}
