{"id":"https://openalex.org/W7204147496","doi":"https://doi.org/10.5281/zenodo.22085771","title":"NF-Gym: an offline reinforcement learning benchmark for EEG neurofeedback protocol selection","display_name":"NF-Gym: an offline reinforcement learning benchmark for EEG neurofeedback protocol selection","publication_year":2026,"publication_date":"2026-08-24","ids":{"openalex":"https://openalex.org/W7204147496","doi":"https://doi.org/10.5281/zenodo.22085771"},"language":"en","primary_location":{"id":"doi:10.5281/zenodo.22085771","doi":"https://doi.org/10.5281/zenodo.22085771","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.22085771","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Software"},"volume":null,"type":"software","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.5281/zenodo.22085771","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5052872866","display_name":"G\u00fcnet Ero\u011flu","orcid":"https://orcid.org/0000-0001-8382-8417","observed_orcids":["https://orcid.org/0000-0001-8382-8417"]},"institutions":[{"id":"https://openalex.org/I128277893","display_name":"Bah\u00e7e\u015fehir University","ror":"https://ror.org/00yze4d93","country_code":"TR","type":"education","lineage":["https://openalex.org/I128277893"]}],"countries":["TR"],"is_corresponding":true,"raw_author_name":"Eroglu, Gunet","raw_affiliation_strings":["Department of Computer Engineering, Bahcesehir University, Istanbul, Turkiye"],"raw_orcid":"https://orcid.org/0000-0001-8382-8417","affiliations":[{"raw_affiliation_string":"Department of Computer Engineering, Bahcesehir University, Istanbul, Turkiye","institution_ids":["https://openalex.org/I128277893"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5052872866"],"corresponding_institution_ids":["https://openalex.org/I128277893"],"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":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10462","display_name":"Reinforcement Learning in Robotics","score":0.8330000042915344,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.8330000042915344,"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":0.1429000049829483,"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/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.004999999888241291,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/neurofeedback","display_name":"neurofeedback","score":0.9739999771118164},{"id":"https://openalex.org/keywords/offline-reinforcement-learning","display_name":"offline reinforcement learning","score":0.953000009059906},{"id":"https://openalex.org/keywords/benchmark","display_name":"benchmark","score":0.6309999823570251},{"id":"https://openalex.org/keywords/protocol-selection","display_name":"protocol selection","score":0.8360000252723694},{"id":"https://openalex.org/keywords/closed-loop-neurofeedback","display_name":"closed-loop neurofeedback","score":0.8109999895095825},{"id":"https://openalex.org/keywords/regret","display_name":"regret","score":0.531000018119812},{"id":"https://openalex.org/keywords/potential-based-reward-shaping","display_name":"potential-based reward shaping","score":0.9150000214576721},{"id":"https://openalex.org/keywords/policy-invariance","display_name":"policy invariance","score":0.7900000214576721}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.771399974822998},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7337999939918518},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7113999724388123},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6021999716758728},{"id":"https://openalex.org/C50817715","wikidata":"https://www.wikidata.org/wiki/Q79895177","display_name":"Regret","level":2,"score":0.5717999935150146},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5582000017166138},{"id":"https://openalex.org/C2164484","wikidata":"https://www.wikidata.org/wiki/Q5170150","display_name":"Core (optical fiber)","level":2,"score":0.4927000105381012},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.48649999499320984},{"id":"https://openalex.org/C2780385302","wikidata":"https://www.wikidata.org/wiki/Q367158","display_name":"Protocol (science)","level":3,"score":0.45190000534057617},{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.42910000681877136},{"id":"https://openalex.org/C2434490","wikidata":"https://www.wikidata.org/wiki/Q1306920","display_name":"Neurofeedback","level":3,"score":0.38670000433921814},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.32429999113082886},{"id":"https://openalex.org/C67203356","wikidata":"https://www.wikidata.org/wiki/Q1321905","display_name":"Reinforcement","level":2,"score":0.32089999318122864},{"id":"https://openalex.org/C40506919","wikidata":"https://www.wikidata.org/wiki/Q7452469","display_name":"Sequence learning","level":2,"score":0.3147999942302704},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.3133000135421753},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.27730000019073486},{"id":"https://openalex.org/C2780102126","wikidata":"https://www.wikidata.org/wiki/Q10928179","display_name":"Online and offline","level":2,"score":0.27730000019073486},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.27000001072883606},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.2685999870300293},{"id":"https://openalex.org/C2778565505","wikidata":"https://www.wikidata.org/wiki/Q2207566","display_name":"Spec#","level":2,"score":0.26440000534057617},{"id":"https://openalex.org/C2780938662","wikidata":"https://www.wikidata.org/wiki/Q973710","display_name":"Tying","level":2,"score":0.26409998536109924},{"id":"https://openalex.org/C2780490138","wikidata":"https://www.wikidata.org/wiki/Q7079636","display_name":"Offline learning","level":3,"score":0.25279998779296875}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.5281/zenodo.22085771","doi":"https://doi.org/10.5281/zenodo.22085771","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.22085771","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Software"},{"id":"doi:10.5281/zenodo.22085772","doi":"https://doi.org/10.5281/zenodo.22085772","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.22085772","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Software"}],"best_oa_location":{"id":"doi:10.5281/zenodo.22085771","doi":"https://doi.org/10.5281/zenodo.22085771","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.22085771","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Software"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W6925440466","https://openalex.org/W4229449597","https://openalex.org/W6944294776","https://openalex.org/W6943950030","https://openalex.org/W2777111584","https://openalex.org/W6903874869","https://openalex.org/W7205196862","https://openalex.org/W2885059787","https://openalex.org/W7212136739","https://openalex.org/W7196922813"],"abstract_inverted_index":{"NF-Gym":[0],"is":[1,15,28],"a":[2,16,39],"benchmark":[3],"for":[4,61,69],"offline":[5],"reinforcement":[6],"learning":[7],"in":[8],"closed-loop":[9],"EEG":[10],"neurofeedback.":[11],"Its":[12],"latent":[13],"core":[14],"small":[17],"tabular":[18],"MDP":[19],"solved":[20],"exactly":[21],"by":[22],"backward":[23],"induction,":[24],"so":[25],"every":[26],"result":[27],"reported":[29],"as":[30,38],"regret":[31],"against":[32],"an":[33,49],"external":[34],"optimum":[35],"rather":[36],"than":[37],"within-simulator":[40],"comparison.":[41],"The":[42],"package":[43],"implements":[44],"Factored":[45],"Return":[46],"Decomposition":[47],"(FRD),":[48],"unstructured":[50],"return-decomposition":[51],"baseline,":[52],"potential-based":[53],"reward":[54],"shaping":[55],"with":[56],"the":[57,66,77],"terminal-potential":[58],"convention":[59],"required":[60],"episodic":[62],"policy":[63],"invariance,":[64],"and":[65,74],"numerical":[67],"checks":[68],"Propositions":[70],"1,":[71],"3,":[72],"4":[73],"5":[75],"of":[76],"accompanying":[78],"paper.":[79]},"counts_by_year":[],"updated_date":"2026-10-28T10:33:24.094632","created_date":"2026-08-25T00:00:00"}
