{"id":"https://openalex.org/W7160267332","doi":"https://doi.org/10.48550/arxiv.2605.00865","title":"How Well Can We Decode Vowels from Auditory EEG -- A Rigorous Cross-Subject Benchmark with Honest Assessment","display_name":"How Well Can We Decode Vowels from Auditory EEG -- A Rigorous Cross-Subject Benchmark with Honest Assessment","publication_year":2026,"publication_date":"2026-04-22","ids":{"openalex":"https://openalex.org/W7160267332","doi":"https://doi.org/10.48550/arxiv.2605.00865"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.00865","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.00865","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.00865","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135289282","display_name":"Xiaoyang Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Li, Xiaoyang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5135289282"],"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":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9424999952316284,"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":0.9424999952316284,"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":0.014100000262260437,"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/T10667","display_name":"Emotion and Mood Recognition","score":0.010900000110268593,"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/decoding-methods","display_name":"Decoding methods","score":0.6065999865531921},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.5995000004768372},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5569999814033508},{"id":"https://openalex.org/keywords/vowel","display_name":"Vowel","score":0.5418999791145325},{"id":"https://openalex.org/keywords/normalization","display_name":"Normalization (sociology)","score":0.5116999745368958},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.475600004196167},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.38659998774528503}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6879000067710876},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.6413999795913696},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.6065999865531921},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.5995000004768372},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5569999814033508},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5455999970436096},{"id":"https://openalex.org/C2779581591","wikidata":"https://www.wikidata.org/wiki/Q36244","display_name":"Vowel","level":2,"score":0.5418999791145325},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.5116999745368958},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.475600004196167},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.38659998774528503},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.38580000400543213},{"id":"https://openalex.org/C61423126","wikidata":"https://www.wikidata.org/wiki/Q187432","display_name":"Scripting language","level":2,"score":0.3725999891757965},{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.3725999891757965},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3506999909877777},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.3262999951839447},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.32359999418258667},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.3190000057220459},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.2840000092983246}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.00865","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.00865","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.00865","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.00865","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.4593597948551178}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"EEG":[0,44],"based":[1],"phoneme":[2],"decoding":[3,36],"is":[4,149],"promising":[5],"for":[6,32,167],"brain":[7],"computer":[8],"interfaces,":[9],"but":[10,151],"many":[11],"prior":[12],"studies":[13],"rely":[14],"on":[15],"within":[16,136],"subject":[17,30,58,137],"evaluation,":[18],"small":[19],"cohorts,":[20],"or":[21],"weak":[22,152],"leakage":[23,68],"control.":[24],"We":[25,161],"present":[26],"a":[27],"reproducible":[28],"cross":[29],"benchmark":[31],"five":[33],"class":[34],"vowel":[35,147],"(a,":[37],"e,":[38],"i,":[39],"o,":[40],"u)":[41],"from":[42,74],"auditory":[43,159],"using":[45],"OpenNeuro":[46],"ds006104":[47],"(16":[48],"subjects,":[49],"61":[50],"channels,":[51],"256":[52],"Hz).":[53],"Under":[54],"strict":[55],"leave":[56],"one":[57],"out":[59],"evaluation":[60,165],"with":[61,100,123],"training":[62],"only":[63],"normalization":[64],"and":[65,80,142,153,164],"explicit":[66],"anti":[67],"checks,":[69],"we":[70],"compare":[71],"14":[72],"pipelines":[73],"classical":[75],"machine":[76],"learning,":[77,79],"deep":[78,124],"Riemannian":[81],"methods.":[82],"The":[83],"best":[84],"full":[85,168],"feature":[86,106],"model":[87,115],"(XGBoost)":[88],"reaches":[89],"24.5":[90],"percent":[91,104],"accuracy":[92],"(chance":[93],"20":[94],"percent),":[95],"while":[96],"differential":[97],"entropy":[98],"features":[99],"LightGBM":[101],"reach":[102],"25.5":[103],"in":[105,126],"specific":[107],"analysis.":[108],"After":[109],"multiple":[110],"comparison":[111],"correction,":[112],"strong":[113],"pairwise":[114,134],"advantages":[116],"are":[117,121],"limited.":[118],"Classical":[119],"methods":[120],"competitive":[122],"models":[125],"this":[127],"low":[128],"signal":[129],"regime.":[130],"Additional":[131],"analyses":[132],"(ablation,":[133],"vowels,":[135],"CV,":[138],"ERP,":[139],"temporal":[140],"generalization,":[141],"electrode":[143],"importance)":[144],"indicate":[145],"that":[146],"information":[148],"real":[150],"mainly":[154],"carried":[155],"by":[156],"early":[157],"transient":[158],"responses.":[160],"release":[162],"code":[163],"scripts":[166],"reproducibility.":[169]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-06T00:00:00"}
