{"id":"https://openalex.org/W2777250476","doi":"https://doi.org/10.1109/dicta.2017.8227402","title":"Affective Video Events Summarization Using EMD Decomposed EEG Signals (EDES)","display_name":"Affective Video Events Summarization Using EMD Decomposed EEG Signals (EDES)","publication_year":2017,"publication_date":"2017-11-01","ids":{"openalex":"https://openalex.org/W2777250476","doi":"https://doi.org/10.1109/dicta.2017.8227402","mag":"2777250476"},"language":"en","primary_location":{"id":"doi:10.1109/dicta.2017.8227402","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dicta.2017.8227402","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"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/A5008370864","display_name":"Md. Musfequs Salehin","orcid":"https://orcid.org/0000-0003-0937-9750"},"institutions":[{"id":"https://openalex.org/I153230381","display_name":"Charles Sturt University","ror":"https://ror.org/00wfvh315","country_code":"AU","type":"education","lineage":["https://openalex.org/I153230381"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"Md. Musfequs Salehin","raw_affiliation_strings":["CSU Machine Learning (CML) Research Unit, Charles Sturt University, Bathurst, Australia"],"affiliations":[{"raw_affiliation_string":"CSU Machine Learning (CML) Research Unit, Charles Sturt University, Bathurst, Australia","institution_ids":["https://openalex.org/I153230381"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002818315","display_name":"Manoranjan Paul","orcid":"https://orcid.org/0000-0001-6870-5056"},"institutions":[{"id":"https://openalex.org/I153230381","display_name":"Charles Sturt University","ror":"https://ror.org/00wfvh315","country_code":"AU","type":"education","lineage":["https://openalex.org/I153230381"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Manoranjan Paul","raw_affiliation_strings":["CSU Machine Learning (CML) Research Unit, Charles Sturt University, Bathurst, Australia"],"affiliations":[{"raw_affiliation_string":"CSU Machine Learning (CML) Research Unit, Charles Sturt University, Bathurst, Australia","institution_ids":["https://openalex.org/I153230381"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5008370864"],"corresponding_institution_ids":["https://openalex.org/I153230381"],"apc_list":null,"apc_paid":null,"fwci":0.5548,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.67370455,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"174","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9922000169754028,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9922000169754028,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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":0.9915000200271606,"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":0.9894999861717224,"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/automatic-summarization","display_name":"Automatic summarization","score":0.8929758071899414},{"id":"https://openalex.org/keywords/electroencephalography","display_name":"Electroencephalography","score":0.8024758696556091},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7583162784576416},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6566939353942871},{"id":"https://openalex.org/keywords/hilbert\u2013huang-transform","display_name":"Hilbert\u2013Huang transform","score":0.5921069979667664},{"id":"https://openalex.org/keywords/discrete-fourier-transform","display_name":"Discrete Fourier transform (general)","score":0.5172763466835022},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4989776611328125},{"id":"https://openalex.org/keywords/time\u2013frequency-analysis","display_name":"Time\u2013frequency analysis","score":0.4449611306190491},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.4134003818035126},{"id":"https://openalex.org/keywords/fourier-transform","display_name":"Fourier transform","score":0.40376016497612},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3146824240684509},{"id":"https://openalex.org/keywords/short-time-fourier-transform","display_name":"Short-time Fourier transform","score":0.2702774107456207},{"id":"https://openalex.org/keywords/fourier-analysis","display_name":"Fourier analysis","score":0.19070300459861755},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1251564621925354}],"concepts":[{"id":"https://openalex.org/C170858558","wikidata":"https://www.wikidata.org/wiki/Q1394144","display_name":"Automatic summarization","level":2,"score":0.8929758071899414},{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.8024758696556091},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7583162784576416},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6566939353942871},{"id":"https://openalex.org/C25570617","wikidata":"https://www.wikidata.org/wiki/Q1006462","display_name":"Hilbert\u2013Huang transform","level":3,"score":0.5921069979667664},{"id":"https://openalex.org/C57733114","wikidata":"https://www.wikidata.org/wiki/Q1006032","display_name":"Discrete Fourier transform (general)","level":5,"score":0.5172763466835022},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4989776611328125},{"id":"https://openalex.org/C142433447","wikidata":"https://www.wikidata.org/wiki/Q7806653","display_name":"Time\u2013frequency analysis","level":3,"score":0.4449611306190491},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.4134003818035126},{"id":"https://openalex.org/C102519508","wikidata":"https://www.wikidata.org/wiki/Q6520159","display_name":"Fourier transform","level":2,"score":0.40376016497612},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3146824240684509},{"id":"https://openalex.org/C166386157","wikidata":"https://www.wikidata.org/wiki/Q1477735","display_name":"Short-time Fourier transform","level":4,"score":0.2702774107456207},{"id":"https://openalex.org/C203024314","wikidata":"https://www.wikidata.org/wiki/Q1365258","display_name":"Fourier analysis","level":3,"score":0.19070300459861755},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1251564621925354},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C118552586","wikidata":"https://www.wikidata.org/wiki/Q7867","display_name":"Psychiatry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/dicta.2017.8227402","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dicta.2017.8227402","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1528177060","https://openalex.org/W1750953599","https://openalex.org/W1900913856","https://openalex.org/W1981898797","https://openalex.org/W1990441178","https://openalex.org/W2002055708","https://openalex.org/W2002645931","https://openalex.org/W2013941432","https://openalex.org/W2032342062","https://openalex.org/W2038896286","https://openalex.org/W2049448443","https://openalex.org/W2050266027","https://openalex.org/W2072551889","https://openalex.org/W2084680213","https://openalex.org/W2109606373","https://openalex.org/W2123751508","https://openalex.org/W2153134014","https://openalex.org/W2532883456","https://openalex.org/W6637546335"],"related_works":["https://openalex.org/W2384010565","https://openalex.org/W2399116914","https://openalex.org/W2582090188","https://openalex.org/W2353169136","https://openalex.org/W2056180080","https://openalex.org/W2320736787","https://openalex.org/W4313203779","https://openalex.org/W2001890362","https://openalex.org/W1971866022","https://openalex.org/W4205371211"],"abstract_inverted_index":{"Video":[0],"Summarization":[1],"is":[2,65,81,172],"a":[3,8,39,47,131,161,166,169],"computer-based":[4],"technique":[5,111],"to":[6,89,119,142],"generate":[7],"shorter":[9],"version":[10],"of":[11,32,38,94],"the":[12,91,99,103,113,144,156,176,181,190,196],"original":[13],"long":[14],"video":[15,26,133,170,178],"for":[16,25,52,68,84,165],"memory":[17],"management":[18],"and":[19,35,76,123,159],"information":[20],"retrieval.":[21],"The":[22,49,147,185],"existing":[23,197],"method":[24,135],"summarization":[27,134],"applies":[28],"affective":[29,177],"(the":[30],"state":[31],"excitement,":[33],"interestingness,":[34],"panic)":[36],"level":[37],"viewer":[40],"captured":[41],"by":[42,174],"Electroencephalography":[43],"(EEG)":[44],"while":[45],"watching":[46],"video.":[48,167],"traditional":[50],"methods":[51],"extracting":[53],"features":[54],"from":[55,117,121,155,180],"EEG":[56,72,85,95,139],"signals":[57,73,96],"employ":[58],"Discrete":[59],"Fourier":[60],"Transform":[61],"(DFT).":[62],"However,":[63],"DFT":[64,80],"mainly":[66],"suitable":[67],"stationary":[69],"signals.":[70,86],"As":[71],"are":[74],"non-linear":[75,122],"non-stationary":[77,124],"in":[78],"nature,":[79],"not":[82],"appropriate":[83],"In":[87],"addition":[88],"this,":[90],"high-frequency":[92,145,157],"components":[93,116,158],"usually":[97],"contain":[98],"distinct":[100],"properties":[101],"than":[102,195],"other":[104],"frequency":[105,115],"components.":[106,146],"Empirical":[107],"Mode":[108],"Decomposition":[109],"(EMD)":[110],"extracts":[112],"different":[114],"high":[118],"low":[120],"signals,":[125],"such":[126],"EEG.":[127],"Therefore,":[128],"we":[129],"propose":[130],"new":[132],"applying":[136],"EMD":[137],"Decomposed":[138],"Signals":[140],"(EDES)":[141],"extract":[143],"proposed":[148,191],"approach":[149,192],"calculates":[150],"Power":[151],"Spectral":[152],"Density":[153],"(PSD)":[154],"generates":[160],"neuronal":[162,182],"attention":[163,183],"curve":[164],"Finally,":[168],"summary":[171],"produced":[173],"selecting":[175],"events":[179],"curve.":[184],"experimental":[186],"results":[187],"reveal":[188],"that":[189],"performs":[193],"better":[194],"state-":[198],"of-the-art":[199],"method.":[200]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}