{"id":"https://openalex.org/W7155099636","doi":"https://doi.org/10.48550/arxiv.2604.18095","title":"DSAINet: An Efficient Dual-Scale Attentive Interaction Network for General EEG Decoding","display_name":"DSAINet: An Efficient Dual-Scale Attentive Interaction Network for General EEG Decoding","publication_year":2026,"publication_date":"2026-04-20","ids":{"openalex":"https://openalex.org/W7155099636","doi":"https://doi.org/10.48550/arxiv.2604.18095"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.18095","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.18095","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.18095","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134133487","display_name":"Zhiyuan Ma","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Ma, Zhiyuan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134139432","display_name":"Zeyuan Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Zeyuan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134142107","display_name":"Zihao Qiu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qiu, Zihao","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134110978","display_name":"Jinhao Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Jinhao","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134124387","display_name":"Lingqin Meng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Meng, Lingqin","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124353985","display_name":"Xinche Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Xinche","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134135948","display_name":"Yixuan Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Yixuan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038486986","display_name":"Xinke Shen","orcid":"https://orcid.org/0000-0001-8531-5033"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shen, Xinke","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5134212528","display_name":"Sen Song","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Song, Sen","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":9,"corresponding_author_ids":["https://openalex.org/A5134133487"],"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":0.9793999791145325,"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":0.9793999791145325,"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":0.003000000026077032,"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/T10241","display_name":"Functional Brain Connectivity Studies","score":0.002199999988079071,"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/generalizability-theory","display_name":"Generalizability theory","score":0.6151999831199646},{"id":"https://openalex.org/keywords/electroencephalography","display_name":"Electroencephalography","score":0.5914000272750854},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.532800018787384},{"id":"https://openalex.org/keywords/salient","display_name":"Salient","score":0.5307999849319458},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5097000002861023},{"id":"https://openalex.org/keywords/security-token","display_name":"Security token","score":0.4832000136375427},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4239000082015991},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.37880000472068787},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.376800000667572}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7940000295639038},{"id":"https://openalex.org/C27158222","wikidata":"https://www.wikidata.org/wiki/Q5532422","display_name":"Generalizability theory","level":2,"score":0.6151999831199646},{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.5914000272750854},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.532800018787384},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5310999751091003},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.5307999849319458},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5097000002861023},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.4832000136375427},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4239000082015991},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.37880000472068787},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.376800000667572},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3562999963760376},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.3555000126361847},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.35269999504089355},{"id":"https://openalex.org/C2777655017","wikidata":"https://www.wikidata.org/wiki/Q1501161","display_name":"Toolbox","level":2,"score":0.3361000120639801},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3357999920845032},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.32260000705718994},{"id":"https://openalex.org/C152478114","wikidata":"https://www.wikidata.org/wiki/Q660910","display_name":"Neurophysiology","level":2,"score":0.30570000410079956},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.2849999964237213},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.27480000257492065},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.274399995803833},{"id":"https://openalex.org/C145912823","wikidata":"https://www.wikidata.org/wiki/Q113558","display_name":"Dynamics (music)","level":2,"score":0.26739999651908875},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2565999925136566},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.2547999918460846},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.2542000114917755},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.2515999972820282},{"id":"https://openalex.org/C123757187","wikidata":"https://www.wikidata.org/wiki/Q9195957","display_name":"Network dynamics","level":2,"score":0.2515000104904175}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.18095","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.18095","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.18095","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.18095","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"In":[0],"real-world":[1],"applications":[2],"of":[3],"noninvasive":[4],"electroencephalography":[5],"(EEG),":[6],"specialized":[7],"decoders":[8],"often":[9,27],"show":[10,157],"limited":[11],"generalizability":[12],"across":[13,33,59,131,153,179],"diverse":[14,96],"tasks":[15,60,152],"under":[16,165],"subject-independent":[17,167],"settings.":[18],"One":[19],"central":[20],"challenge":[21],"is":[22,172,201],"that":[23,44,158],"task-relevant":[24,129],"EEG":[25,81,92,150],"signals":[26,93],"follow":[28],"different":[29],"temporal":[30,47,57,97],"organization":[31],"patterns":[32,122],"tasks,":[34],"while":[35],"many":[36],"existing":[37],"methods":[38],"rely":[39],"on":[40,147],"task-tailored":[41],"architectural":[42],"designs":[43],"introduce":[45],"task-specific":[46],"inductive":[48],"biases.":[49],"This":[50],"mismatch":[51],"makes":[52],"it":[53],"difficult":[54],"to":[55,118,127,138],"adapt":[56],"modeling":[58],"without":[61],"changing":[62],"the":[63,175],"model":[64],"configuration.":[65],"To":[66],"address":[67],"these":[68],"challenges,":[69],"we":[70],"propose":[71],"DSAINet,":[72],"an":[73],"efficient":[74],"dual-scale":[75],"attentive":[76],"interaction":[77],"network":[78],"for":[79,143],"general":[80],"decoding.":[82],"Specifically,":[83],"DSAINet":[84,159,182],"constructs":[85],"shared":[86],"spatiotemporal":[87],"token":[88,136],"representations":[89,110],"from":[90],"raw":[91],"and":[94,105,123,194],"models":[95],"dynamics":[98],"through":[99],"parallel":[100],"convolutional":[101],"branches":[102],"at":[103,204],"fine":[104],"coarse":[106],"scales.":[107],"The":[108,199],"resulting":[109],"are":[111],"then":[112],"adaptively":[113],"refined":[114],"by":[115,124,134],"intra-branch":[116],"attention":[117,126],"emphasize":[119],"salient":[120],"scale-specific":[121],"inter-branch":[125],"integrate":[128],"features":[130],"scales,":[132],"followed":[133],"adaptive":[135],"aggregation":[137],"yield":[139],"a":[140,184],"compact":[141],"representation":[142],"prediction.":[144],"Extensive":[145],"experiments":[146],"five":[148],"downstream":[149],"decoding":[151],"ten":[154],"public":[155],"datasets":[156],"consistently":[160],"outperforms":[161],"13":[162],"representative":[163],"baselines":[164],"strict":[166],"evaluation.":[168],"Notably,":[169],"this":[170],"performance":[171],"achieved":[173],"using":[174],"same":[176],"architecture":[177],"hyperparameters":[178],"datasets.":[180],"Moreover,":[181],"achieves":[183],"favorable":[185],"accuracy-efficiency":[186],"trade-off":[187],"with":[188],"only":[189],"about":[190],"77K":[191],"trainable":[192],"parameters":[193],"provides":[195],"interpretable":[196],"neurophysiological":[197],"insights.":[198],"code":[200],"publicly":[202],"available":[203],"https://github.com/zy0929/DSAINet.":[205]},"counts_by_year":[],"updated_date":"2026-04-22T06:07:44.442478","created_date":"2026-04-22T00:00:00"}
