{"id":"https://openalex.org/W7118793864","doi":"https://doi.org/10.1109/access.2026.3651653","title":"An End-to-End Automated Pipeline for EEG Classification on TinyML Platforms: From Signal to On-Device Inference","display_name":"An End-to-End Automated Pipeline for EEG Classification on TinyML Platforms: From Signal to On-Device Inference","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7118793864","doi":"https://doi.org/10.1109/access.2026.3651653"},"language":null,"primary_location":{"id":"doi:10.1109/access.2026.3651653","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3651653","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.2026.3651653","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5068603353","display_name":"C. Popa","orcid":null},"institutions":[{"id":"https://openalex.org/I61641377","display_name":"Universitatea Na\u021bional\u0103 de \u0218tiin\u021b\u0103 \u0219i Tehnologie Politehnica Bucure\u0219ti","ror":"https://ror.org/0558j5q12","country_code":"RO","type":"education","lineage":["https://openalex.org/I61641377"]}],"countries":["RO"],"is_corresponding":true,"raw_author_name":"C\u0103t\u0103lin Aurelian Popa","raw_affiliation_strings":["Department of Applied Electronics and Information Engineering, National University of Science and Technology Politehnica Bucharest, Bucharest, Romania"],"affiliations":[{"raw_affiliation_string":"Department of Applied Electronics and Information Engineering, National University of Science and Technology Politehnica Bucharest, Bucharest, Romania","institution_ids":["https://openalex.org/I61641377"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108522699","display_name":"Ioana Dogaru","orcid":null},"institutions":[{"id":"https://openalex.org/I61641377","display_name":"Universitatea Na\u021bional\u0103 de \u0218tiin\u021b\u0103 \u0219i Tehnologie Politehnica Bucure\u0219ti","ror":"https://ror.org/0558j5q12","country_code":"RO","type":"education","lineage":["https://openalex.org/I61641377"]}],"countries":["RO"],"is_corresponding":false,"raw_author_name":"Ioana Dogaru","raw_affiliation_strings":["Department of Applied Electronics and Information Engineering, National University of Science and Technology Politehnica Bucharest, Bucharest, Romania"],"affiliations":[{"raw_affiliation_string":"Department of Applied Electronics and Information Engineering, National University of Science and Technology Politehnica Bucharest, Bucharest, Romania","institution_ids":["https://openalex.org/I61641377"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5061756664","display_name":"Radu DOGARU","orcid":null},"institutions":[{"id":"https://openalex.org/I61641377","display_name":"Universitatea Na\u021bional\u0103 de \u0218tiin\u021b\u0103 \u0219i Tehnologie Politehnica Bucure\u0219ti","ror":"https://ror.org/0558j5q12","country_code":"RO","type":"education","lineage":["https://openalex.org/I61641377"]}],"countries":["RO"],"is_corresponding":false,"raw_author_name":"Radu Dogaru","raw_affiliation_strings":["Department of Applied Electronics and Information Engineering, National University of Science and Technology Politehnica Bucharest, Bucharest, Romania"],"affiliations":[{"raw_affiliation_string":"Department of Applied Electronics and Information Engineering, National University of Science and Technology Politehnica Bucharest, Bucharest, Romania","institution_ids":["https://openalex.org/I61641377"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5068603353"],"corresponding_institution_ids":["https://openalex.org/I61641377"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.08750231,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"14","issue":null,"first_page":"8918","last_page":"8937"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9728000164031982,"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":0.9728000164031982,"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":0.00800000037997961,"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"}},{"id":"https://openalex.org/T10094","display_name":"Epilepsy research and treatment","score":0.0024999999441206455,"subfield":{"id":"https://openalex.org/subfields/2738","display_name":"Psychiatry and Mental health"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5759000182151794},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.5684000253677368},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.5246999859809875},{"id":"https://openalex.org/keywords/wearable-computer","display_name":"Wearable computer","score":0.492000013589859},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.421099990606308},{"id":"https://openalex.org/keywords/electroencephalography","display_name":"Electroencephalography","score":0.41940000653266907},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.4025999903678894},{"id":"https://openalex.org/keywords/signal-processing","display_name":"Signal processing","score":0.4007999897003174},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.39590001106262207}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8515999913215637},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.588100016117096},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5759000182151794},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.5684000253677368},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.5246999859809875},{"id":"https://openalex.org/C150594956","wikidata":"https://www.wikidata.org/wiki/Q1334829","display_name":"Wearable computer","level":2,"score":0.492000013589859},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43149998784065247},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.421099990606308},{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.41940000653266907},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.4025999903678894},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.4007999897003174},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.39590001106262207},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.36320000886917114},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.35440000891685486},{"id":"https://openalex.org/C10551718","wikidata":"https://www.wikidata.org/wiki/Q5227332","display_name":"Data pre-processing","level":2,"score":0.3303000032901764},{"id":"https://openalex.org/C84462506","wikidata":"https://www.wikidata.org/wiki/Q173142","display_name":"Digital signal processing","level":2,"score":0.3215000033378601},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.31349998712539673},{"id":"https://openalex.org/C175309249","wikidata":"https://www.wikidata.org/wiki/Q725864","display_name":"Pipeline transport","level":2,"score":0.310699999332428},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3093000054359436},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.30880001187324524},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.29899999499320984},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.29420000314712524},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.29170000553131104},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.29159998893737793},{"id":"https://openalex.org/C138236772","wikidata":"https://www.wikidata.org/wiki/Q25098575","display_name":"Edge device","level":3,"score":0.29010000824928284},{"id":"https://openalex.org/C2779334592","wikidata":"https://www.wikidata.org/wiki/Q6279182","display_name":"Epileptic seizure","level":3,"score":0.2865999937057495},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.2838999927043915},{"id":"https://openalex.org/C175079658","wikidata":"https://www.wikidata.org/wiki/Q7312165","display_name":"Remote patient monitoring","level":2,"score":0.2775999903678894},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.27549999952316284}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/access.2026.3651653","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3651653","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"}],"best_oa_location":{"id":"doi:10.1109/access.2026.3651653","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3651653","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":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.7240986824035645}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W2053744708","https://openalex.org/W2055603681","https://openalex.org/W2109453162","https://openalex.org/W2559463885","https://openalex.org/W2599251041","https://openalex.org/W2775622076","https://openalex.org/W2889782437","https://openalex.org/W2962697884","https://openalex.org/W2963355311","https://openalex.org/W2993247352","https://openalex.org/W2993348073","https://openalex.org/W3017231556","https://openalex.org/W3042619474","https://openalex.org/W3080571847","https://openalex.org/W3087755667","https://openalex.org/W3091321442","https://openalex.org/W3093717841","https://openalex.org/W3119118767","https://openalex.org/W3150499614","https://openalex.org/W3161831566","https://openalex.org/W4316659470","https://openalex.org/W4319295260","https://openalex.org/W4377195629","https://openalex.org/W4392562614","https://openalex.org/W4402371937","https://openalex.org/W4413679374","https://openalex.org/W4414032887"],"related_works":[],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"an":[3],"optimized":[4],"methodology":[5],"for":[6,27,83,112,136,143,163,179,189],"processing":[7],"electroencephalogram":[8],"signals":[9],"on":[10,38,86,115,147],"resource-constrained":[11],"TinyML":[12],"platforms,":[13],"targeting":[14],"emotion":[15,113],"recognition":[16,114],"and":[17,35,54,88,171,182,195],"epileptic":[18],"seizure":[19,84],"detection.":[20],"We":[21],"integrate":[22],"the":[23,39,48,55,69,141],"Reaction-Diffusion":[24],"Transform":[25],"(RDT)":[26],"efficient":[28],"preprocessing,":[29],"automated":[30],"hyperparameter":[31],"optimization":[32],"using":[33],"Optuna,":[34],"model":[36],"deployment":[37],"ESP32":[40],"microcontroller":[41],"via":[42],"Edge":[43],"Impulse.":[44],"The":[45],"approach":[46],"leverages":[47],"GAMEEMO":[49],"dataset":[50,57],"(four":[51],"emotional":[52],"states)":[53],"ESR":[56],"(five":[58],"seizure-related":[59],"categories)":[60],"to":[61,101,127],"develop":[62],"compact,":[63],"high-performance":[64],"models.":[65],"Preliminary":[66],"experiments":[67],"validate":[68],"pipeline\u2019s":[70],"feasibility,":[71],"achieving":[72],"92.6%":[73],"accuracy":[74,90],"with":[75,95,108,150],"a":[76,122,160,187],"15":[77],"ms":[78,103],"inference":[79,99],"time":[80,100],"(int8":[81],"quantization)":[82],"detection":[85],"ESR,":[87],"79.9%":[89],"(raw":[91],"data,":[92],"16":[93],"filters)":[94],"RDT":[96,117],"preprocessing":[97],"reducing":[98],"146":[102],"(77.9%":[104],"accuracy,":[105],"85%":[106],"on-device":[107],"20":[109],"samples,":[110],"int8)":[111],"GAMEEMO.":[116],"reduces":[118],"data":[119,129],"dimensionality,":[120],"enabling":[121],"five-fold":[123],"latency":[124],"decrease":[125],"compared":[126],"raw":[128],"processing,":[130],"while":[131],"maintaining":[132],"discriminative":[133],"features":[134],"critical":[135],"classification.":[137],"These":[138],"results":[139],"highlight":[140],"potential":[142],"real-time":[144],"EEG":[145],"analysis":[146],"low-power":[148],"devices":[149],"limited":[151],"resources":[152],"(520":[153],"KiB":[154],"RAM,":[155],"240":[156],"MHz":[157],"CPU),":[158],"offering":[159],"scalable":[161],"solution":[162],"wearable":[164],"biomedical":[165],"systems.":[166],"By":[167],"addressing":[168],"inter-subject":[169],"variability":[170],"computational":[172],"constraints,":[173],"this":[174],"work":[175],"advances":[176],"point-of-care":[177],"diagnostics":[178],"epilepsy":[180],"monitoring":[181],"mental":[183],"health":[184],"applications,":[185],"laying":[186],"foundation":[188],"future":[190],"enhancements":[191],"in":[192],"temporal":[193],"resolution":[194],"generalization.":[196]},"counts_by_year":[],"updated_date":"2026-01-24T23:23:39.755997","created_date":"2026-01-08T00:00:00"}
