{"id":"https://openalex.org/W350363685","doi":null,"title":"Feature extraction for EEG classification: representing electrode outputs as a Markov stochastic process","display_name":"Feature extraction for EEG classification: representing electrode outputs as a Markov stochastic process","publication_year":2007,"publication_date":"2007-01-01","ids":{"openalex":"https://openalex.org/W350363685","mag":"350363685"},"language":"en","primary_location":{"id":"mag:350363685","doi":null,"is_oa":false,"landing_page_url":"https://dblp.uni-trier.de/db/conf/esann/esann2007.html#WuN07","pdf_url":null,"source":{"id":"https://openalex.org/S4306421047","display_name":"The European Symposium on Artificial Neural Networks","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"The European Symposium on Artificial Neural Networks","raw_type":null},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101525811","display_name":"Liang Wu","orcid":"https://orcid.org/0000-0002-2336-7695","observed_orcids":["https://orcid.org/0000-0002-2336-7695","https://orcid.org/0009-0001-3087-2403"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liang Wu","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-2336-7695","affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5070935552","display_name":"Predrag Neskovic","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Predrag Neskovic","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.6823,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.62253289,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"567","last_page":"572"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9984999895095825,"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":0.9984999895095825,"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":0.000699999975040555,"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/T10581","display_name":"Neural dynamics and brain function","score":9.999999747378752e-05,"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/eeg-classification","display_name":"EEG classification","score":0.9240000247955322},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"feature extraction","score":0.9869999885559082},{"id":"https://openalex.org/keywords/markov-processes","display_name":"Markov processes","score":0.8489999771118164},{"id":"https://openalex.org/keywords/conditional-entropy","display_name":"conditional entropy","score":0.8600000143051147},{"id":"https://openalex.org/keywords/conditional-mutual-information","display_name":"conditional mutual information","score":0.953000009059906},{"id":"https://openalex.org/keywords/discriminative-features","display_name":"discriminative features","score":0.7020000219345093},{"id":"https://openalex.org/keywords/time-dependence","display_name":"time dependence","score":0.5870000123977661},{"id":"https://openalex.org/keywords/single-trial-eeg","display_name":"single-trial EEG","score":0.8109999895095825}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.877422571182251},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6891853213310242},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.5927561521530151},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5907233953475952},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.561740517616272},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.5356634855270386},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.5279154777526855},{"id":"https://openalex.org/C101721835","wikidata":"https://www.wikidata.org/wiki/Q813908","display_name":"Conditional entropy","level":3,"score":0.5242041349411011},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5130287408828735},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.4828372597694397},{"id":"https://openalex.org/C8272713","wikidata":"https://www.wikidata.org/wiki/Q176737","display_name":"Stochastic process","level":2,"score":0.43885496258735657},{"id":"https://openalex.org/C196956702","wikidata":"https://www.wikidata.org/wiki/Q6795829","display_name":"Maximum-entropy Markov model","level":5,"score":0.4271526038646698},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.30886244773864746},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3064729571342468},{"id":"https://openalex.org/C163836022","wikidata":"https://www.wikidata.org/wiki/Q6771326","display_name":"Markov model","level":3,"score":0.28661012649536133},{"id":"https://openalex.org/C54907487","wikidata":"https://www.wikidata.org/wiki/Q7915688","display_name":"Variable-order Markov model","level":4,"score":0.226995587348938},{"id":"https://openalex.org/C9679016","wikidata":"https://www.wikidata.org/wiki/Q1417473","display_name":"Principle of maximum entropy","level":2,"score":0.1891438364982605},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1622300148010254},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:350363685","doi":null,"is_oa":false,"landing_page_url":"https://dblp.uni-trier.de/db/conf/esann/esann2007.html#WuN07","pdf_url":null,"source":{"id":"https://openalex.org/S4306421047","display_name":"The European Symposium on Artificial Neural Networks","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"The European Symposium on Artificial Neural Networks","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W135205812","https://openalex.org/W1509205144","https://openalex.org/W1606551439","https://openalex.org/W2003689160","https://openalex.org/W2049971040","https://openalex.org/W2065919594","https://openalex.org/W2099111195","https://openalex.org/W2109100179"],"related_works":["https://openalex.org/W135205812","https://openalex.org/W1592362975","https://openalex.org/W7096172067","https://openalex.org/W4408584353","https://openalex.org/W2759681378","https://openalex.org/W2010652710","https://openalex.org/W3136172800","https://openalex.org/W3192255147","https://openalex.org/W2883827556","https://openalex.org/W3164514384"],"abstract_inverted_index":{"In":[0],"this":[1],"work":[2],"we":[3,49,60],"introduce":[4],"a":[5,24,35,76,86],"new":[6],"model":[7],"for":[8,82],"representing":[9],"EEG":[10,104],"signals":[11],"and":[12,27,39,53],"extracting":[13],"discriminative":[14,92],"features.":[15],"We":[16,70,94],"treat":[17],"the":[18,30,96,103],"outputs":[19],"of":[20,32,67,79,98],"each":[21],"electrode":[22],"as":[23],"stochastic":[25],"process":[26,36],"assume":[28],"that":[29,72],"sequence":[31],"variables":[33],"forming":[34],"is":[37],"stationary":[38],"Markov.":[40],"To":[41],"capture":[42,55],"tem-":[43],"poral":[44],"dependences":[45,56],"within":[46],"an":[47],"electrode,":[48],"use":[50,61],"conditional":[51,62],"entropy":[52],"to":[54],"between":[57],"dierent":[58],"electrodes":[59],"mu-":[63],"tual":[64],"information":[65],"features":[66,90,100],"increasing":[68],"complexities.":[69],"show":[71],"even":[73],"when":[74],"using":[75],"small":[77],"number":[78],"sampling":[80],"points":[81],"their":[83],"estimation":[84],"(e.g.":[85],"single":[87],"trial)":[88],"these":[89,99],"carry":[91],"information.":[93],"test":[95],"usefulness":[97],"by":[101],"classifying":[102],"data":[105],"from":[106],"n-back":[107],"memory":[108],"tasks.":[109]},"counts_by_year":[{"year":2019,"cited_by_count":1},{"year":2014,"cited_by_count":1},{"year":2013,"cited_by_count":2},{"year":2012,"cited_by_count":1}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2016-06-24T00:00:00"}