{"id":"https://openalex.org/W7225464892","doi":"https://doi.org/10.1016/j.bspc.2026.111819","title":"Transfer learning for GAN-based EEG generation: Fine-tuning a pretrained model for mind-wandering with limited data","display_name":"Transfer learning for GAN-based EEG generation: Fine-tuning a pretrained model for mind-wandering with limited data","publication_year":2026,"publication_date":"2026-11-07","ids":{"openalex":"https://openalex.org/W7225464892","doi":"https://doi.org/10.1016/j.bspc.2026.111819"},"language":"en","primary_location":{"id":"doi:10.1016/j.bspc.2026.111819","doi":"https://doi.org/10.1016/j.bspc.2026.111819","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.bspc.2026.111819","pdf_url":null,"source":{"id":"https://openalex.org/S8427965","display_name":"Biomedical Signal Processing and Control","issn_l":"1746-8094","issn":["1746-8094","1746-8108"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","dongbi-c","jufo-1","ki-jl-1","norway-1","poland-140","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Biomedical Signal Processing and Control","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1016/j.bspc.2026.111819","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5034606745","display_name":"Xinyu Li","orcid":"https://orcid.org/0009-0003-9952-1913","observed_orcids":["https://orcid.org/0009-0003-9952-1913"]},"institutions":[{"id":"https://openalex.org/I1334415907","display_name":"University Medical Center Groningen","ror":"https://ror.org/03cv38k47","country_code":"NL","type":"healthcare","lineage":["https://openalex.org/I1334415907"]},{"id":"https://openalex.org/I169381384","display_name":"University of Groningen","ror":"https://ror.org/012p63287","country_code":"NL","type":"education","lineage":["https://openalex.org/I169381384"]}],"countries":["NL"],"is_corresponding":true,"raw_author_name":"Xinyu Li","raw_affiliation_strings":["University of Groningen, University Medical Center Groningen, Department of Neurology, Groningen, the Netherlands"],"raw_orcid":"https://orcid.org/0009-0003-9952-1913","affiliations":[{"raw_affiliation_string":"University of Groningen, University Medical Center Groningen, Department of Neurology, Groningen, the Netherlands","institution_ids":["https://openalex.org/I1334415907","https://openalex.org/I169381384"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034043818","display_name":"Marieke K. van Vugt","orcid":"https://orcid.org/0000-0003-3200-0059","observed_orcids":["https://orcid.org/0000-0003-3200-0059"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Marieke K. van Vugt","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0003-3200-0059","affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5065911879","display_name":"Natasha M. Maurits","orcid":"https://orcid.org/0000-0001-6231-7044","observed_orcids":["https://orcid.org/0000-0001-6231-7044"]},"institutions":[{"id":"https://openalex.org/I1334415907","display_name":"University Medical Center Groningen","ror":"https://ror.org/03cv38k47","country_code":"NL","type":"healthcare","lineage":["https://openalex.org/I1334415907"]},{"id":"https://openalex.org/I169381384","display_name":"University of Groningen","ror":"https://ror.org/012p63287","country_code":"NL","type":"education","lineage":["https://openalex.org/I169381384"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Natasha M. Maurits","raw_affiliation_strings":["University of Groningen, University Medical Center Groningen, Department of Neurology, Groningen, the Netherlands"],"raw_orcid":"https://orcid.org/0000-0001-6231-7044","affiliations":[{"raw_affiliation_string":"University of Groningen, University Medical Center Groningen, Department of Neurology, Groningen, the Netherlands","institution_ids":["https://openalex.org/I1334415907","https://openalex.org/I169381384"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5034606745"],"corresponding_institution_ids":["https://openalex.org/I1334415907","https://openalex.org/I169381384"],"apc_list":{"value":2590,"currency":"USD","value_usd":2590},"apc_paid":{"value":2590,"currency":"USD","value_usd":2590},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"131","issue":null,"first_page":"111819","last_page":"111819"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.8579000234603882,"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":0.8579000234603882,"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/T13219","display_name":"Mind wandering and attention","score":0.08860000222921371,"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/T10581","display_name":"Neural dynamics and brain function","score":0.006200000178068876,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/mind-wandering","display_name":"mind wandering","score":0.9549999833106995},{"id":"https://openalex.org/keywords/generative-adversarial-networks","display_name":"generative adversarial networks","score":0.9509999752044678},{"id":"https://openalex.org/keywords/transfer-learning","display_name":"transfer learning","score":0.8600000143051147},{"id":"https://openalex.org/keywords/fine-tuning","display_name":"fine-tuning","score":0.9380000233650208},{"id":"https://openalex.org/keywords/wasserstein-gan-with-gradient-penalty","display_name":"Wasserstein GAN with gradient penalty","score":0.8330000042915344},{"id":"https://openalex.org/keywords/event-related-potentials","display_name":"event-related potentials","score":0.8690000176429749},{"id":"https://openalex.org/keywords/data-scarcity","display_name":"data scarcity","score":0.6859999895095825},{"id":"https://openalex.org/keywords/eeg-power-spectra","display_name":"EEG power spectra","score":0.5460000038146973}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.781000018119812},{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.7469000220298767},{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.7027000188827515},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5703999996185303},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.49639999866485596},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.4731999933719635},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.4325999915599823},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.40059998631477356},{"id":"https://openalex.org/C51432778","wikidata":"https://www.wikidata.org/wiki/Q1259145","display_name":"Independent component analysis","level":2,"score":0.3846000134944916},{"id":"https://openalex.org/C173201364","wikidata":"https://www.wikidata.org/wiki/Q897410","display_name":"Brain\u2013computer interface","level":3,"score":0.36899998784065247},{"id":"https://openalex.org/C110384440","wikidata":"https://www.wikidata.org/wiki/Q1143270","display_name":"Upsampling","level":3,"score":0.364300012588501},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.3458000123500824},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.3386000096797943},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32420000433921814},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.3215999901294708},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.31459999084472656},{"id":"https://openalex.org/C519536355","wikidata":"https://www.wikidata.org/wiki/Q21021151","display_name":"Repurposing","level":2,"score":0.3061000108718872},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.30329999327659607},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.28949999809265137},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.2833000123500824},{"id":"https://openalex.org/C71134354","wikidata":"https://www.wikidata.org/wiki/Q458825","display_name":"Kernel density estimation","level":3,"score":0.27970001101493835},{"id":"https://openalex.org/C197424946","wikidata":"https://www.wikidata.org/wiki/Q1165717","display_name":"Waveform","level":3,"score":0.2782999873161316},{"id":"https://openalex.org/C206588197","wikidata":"https://www.wikidata.org/wiki/Q846574","display_name":"Reuse","level":2,"score":0.27709999680519104},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.2590000033378601}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1016/j.bspc.2026.111819","doi":"https://doi.org/10.1016/j.bspc.2026.111819","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.bspc.2026.111819","pdf_url":null,"source":{"id":"https://openalex.org/S8427965","display_name":"Biomedical Signal Processing and Control","issn_l":"1746-8094","issn":["1746-8094","1746-8108"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","dongbi-c","jufo-1","ki-jl-1","norway-1","poland-140","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Biomedical Signal Processing and Control","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1016/j.bspc.2026.111819","doi":"https://doi.org/10.1016/j.bspc.2026.111819","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.bspc.2026.111819","pdf_url":null,"source":{"id":"https://openalex.org/S8427965","display_name":"Biomedical Signal Processing and Control","issn_l":"1746-8094","issn":["1746-8094","1746-8108"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","dongbi-c","jufo-1","ki-jl-1","norway-1","poland-140","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Biomedical Signal Processing and Control","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320322725","display_name":"China Scholarship Council","ror":"https://ror.org/04atp4p48"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":74,"referenced_works":["https://openalex.org/W1590047728","https://openalex.org/W2003985214","https://openalex.org/W2009271482","https://openalex.org/W2010159837","https://openalex.org/W2073768640","https://openalex.org/W2104197832","https://openalex.org/W2117194251","https://openalex.org/W2123947647","https://openalex.org/W2165698076","https://openalex.org/W2346062110","https://openalex.org/W2523445223","https://openalex.org/W2605135824","https://openalex.org/W2807841289","https://openalex.org/W2894740066","https://openalex.org/W2901107321","https://openalex.org/W2904367110","https://openalex.org/W2904408089","https://openalex.org/W2921889068","https://openalex.org/W2945839551","https://openalex.org/W2952572991","https://openalex.org/W2955419283","https://openalex.org/W2980113592","https://openalex.org/W3008257486","https://openalex.org/W3034855543","https://openalex.org/W3041133507","https://openalex.org/W3046090006","https://openalex.org/W3080580366","https://openalex.org/W3095986980","https://openalex.org/W3096831136","https://openalex.org/W3119036516","https://openalex.org/W3161810785","https://openalex.org/W4280613082","https://openalex.org/W4308733509","https://openalex.org/W4318464336","https://openalex.org/W4322766882","https://openalex.org/W4328050827","https://openalex.org/W4363679399","https://openalex.org/W4368373574","https://openalex.org/W4382371186","https://openalex.org/W4385880522","https://openalex.org/W4389477728","https://openalex.org/W4389542791","https://openalex.org/W4391188276","https://openalex.org/W4391669283","https://openalex.org/W4391912573","https://openalex.org/W4392160106","https://openalex.org/W4394604724","https://openalex.org/W4395117426","https://openalex.org/W4399858440","https://openalex.org/W4400512529","https://openalex.org/W4402037295","https://openalex.org/W4402379840","https://openalex.org/W4403589794","https://openalex.org/W4404522101","https://openalex.org/W4404811801","https://openalex.org/W4404837314","https://openalex.org/W4405536523","https://openalex.org/W4405653627","https://openalex.org/W4407099082","https://openalex.org/W4408961068","https://openalex.org/W4409581441","https://openalex.org/W4409849624","https://openalex.org/W4410481326","https://openalex.org/W4412392440","https://openalex.org/W4412434884","https://openalex.org/W4412804174","https://openalex.org/W4413112342","https://openalex.org/W4413220815","https://openalex.org/W4414520949","https://openalex.org/W4415047523","https://openalex.org/W4415050877","https://openalex.org/W4415975394","https://openalex.org/W4416435148","https://openalex.org/W7133193464"],"related_works":["https://openalex.org/W2991185935","https://openalex.org/W2806591294","https://openalex.org/W3040218637","https://openalex.org/W4416435148","https://openalex.org/W4413220815","https://openalex.org/W4229009467","https://openalex.org/W4408804125","https://openalex.org/W4408132708","https://openalex.org/W7133216936","https://openalex.org/W4410924670"],"abstract_inverted_index":{"Mind-wandering":[0],"(MW)":[1],"studies":[2],"often":[3],"suffer":[4],"from":[5,240],"extremely":[6],"small":[7],"datasets,":[8],"making":[9],"it":[10],"difficult":[11],"to":[12,28,53,177,213],"model":[13,246],"condition-specific":[14],"electroencephalography":[15],"(EEG)":[16],"and":[17,33,88,106,115,124,134,159,162,165,183,209],"motivating":[18],"data":[19,61],"augmentation.":[20],"In":[21,198],"these":[22],"MW":[23,32,57,179,234],"studies,":[24],"EEG":[25,58,127,235],"is":[26,230],"recorded":[27],"distinguish":[29],"between":[30],"the":[31,65,101,173,187,200,244],"on-task":[34,48],"(OT)":[35],"state.":[36],"We":[37],"examined":[38],"whether":[39],"a":[40,95],"generative":[41],"adversarial":[42],"network":[43],"(GAN)":[44],"pretrained":[45,84],"on":[46,85,144,186],"data-rich":[47],"trials":[49,87,180],"can":[50],"be":[51],"adapted":[52],"generate":[54],"physiologically":[55],"plausible":[56],"under":[59],"severe":[60],"scarcity.":[62],"To":[63],"compare":[64],"effects":[66],"of":[67,70,167],"different":[68],"types":[69],"pretraining,":[71],"we":[72],"used":[73],"an":[74,219],"event-related":[75],"potential":[76],"(ERP)-based":[77],"Wasserstein":[78],"GAN":[79],"with":[80,224],"gradient":[81],"penalty":[82],"(WGAN-GP)":[83],"OT":[86],"compared":[89],"six":[90],"training":[91,163,211,239],"approaches:":[92],"from-scratch":[93,210],"training,":[94],"no-training":[96,201],"baseline":[97,202],"that":[98,111],"directly":[99],"reused":[100],"OT-pretrained":[102,220],"model,":[103,222],"full":[104,225],"fine-tuning,":[105],"three":[107],"partial":[108,192,227],"fine-tuning":[109,171,193,228],"variants":[110],"updated":[112],"selected":[113],"generator":[114],"discriminator":[116],"layers.":[117],"Models":[118],"were":[119],"evaluated":[120],"using":[121],"ERP":[122],"waveforms":[123],"power":[125],"spectra,":[126],"features":[128],"(band":[129],"power,":[130],"entropy,":[131],"P3":[132],"amplitude":[133],"latency,":[135],"Petrosian":[136],"fractal":[137],"dimension,":[138],"Hjorth":[139],"complexity),":[140],"distributional":[141],"visualization":[142],"based":[143],"principal":[145],"component":[146],"analysis":[147],"(PCA),":[148],"t-distributed":[149],"stochastic":[150],"neighbor":[151],"embedding":[152],"(t-SNE),":[153],"kernel":[154],"density":[155],"estimation":[156],"(KDE),":[157],"distance-":[158],"divergence-based":[160],"metrics,":[161],"time":[164],"number":[166],"trainable":[168],"parameters.":[169],"Full":[170],"produced":[172],"overall":[174],"closest":[175],"match":[176],"real":[178],"across":[181],"waveform-,":[182],"distribution-level":[184],"measures":[185],"held-out":[188],"test":[189],"set,":[190],"whereas":[191],"achieved":[194],"competitive":[195],"feature-level":[196],"similarity.":[197],"contrast,":[199],"kept":[203],"OT-like":[204],"rather":[205],"than":[206,238],"MW-specific":[207],"patterns,":[208],"failed":[212],"learn":[214],"MW-like":[215],"characteristics.":[216],"Therefore,":[217],"adapting":[218],"ERP-WGAN-GP":[221],"especially":[223],"or":[226,242],"approaches,":[229],"more":[231],"effective":[232],"for":[233],"trial":[236],"generation":[237],"scratch":[241],"reusing":[243],"source":[245],"without":[247],"adaptation.":[248]},"counts_by_year":[],"updated_date":"2026-11-09T13:22:03.289127","created_date":"2026-11-08T00:00:00"}
