On the efficiency of long short-term memory in classifying musical impressions from EEG recordings

On the efficiency of long short-term memory in classifying musical impressions from EEG recordings

The objective of this study is the classification of musical impressions with long short-term memory (LSTM) approach using EEG recordings of 20 subjects, while listening to different music genres. For this purpose, a deep learning model was developed, where relevant features extracted from intrinsic...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Kaya, Burak, Habiboglu, M. Gokhan, Moghaddamnia, Sanam
Format: Tagungsbericht
Sprache:eng
Schlagworte:
Electroencephalography
Signal classification
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue 1
container_start_page
container_title
container_volume 2879
creator Kaya, Burak
Habiboglu, M. Gokhan
Moghaddamnia, Sanam
description The objective of this study is the classification of musical impressions with long short-term memory (LSTM) approach using EEG recordings of 20 subjects, while listening to different music genres. For this purpose, a deep learning model was developed, where relevant features extracted from intrinsic mode functions (IMF) of the clean EEG data are used as the input signals. The classification accuracy of the proposed model is evaluated with various feature sets. The highest classification accuracy is 73.33%, which is achieved by combining higher-order statistics and the first difference of IMF features.
doi_str_mv 10.1063/12.0023973
format Conference Proceeding
fullrecord <record><control><sourceid>proquest_scita</sourceid><recordid>TN_cdi_proquest_journals_2899114245</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2899114245</sourcerecordid><originalsourceid>FETCH-LOGICAL-p134t-be11ed8622e89b7ca00540e06e33a8f5737e947301af67f6fc3414940deb95c13</originalsourceid><addsrcrecordid>eNot0EFLwzAUB_AgCs7pxU8Q8CideUnaNEcZcwqDXRS8lTR9cRltU5Pu0G9vZTs9ePz4v8efkEdgK2CFeAG-YowLrcQVWUCeQ6YKKK7JgjEtMy7F9y25S-k4I61UuSD1vqfjASk6563H3k40ONqG_oemQ4hjNmLsaIddiBP1PbWtScm7yc-gOyVvTUt9N0Sct6FP1MXQ0c1mSyPaEJuZpXty40yb8OEyl-TrbfO5fs92--3H-nWXDSDkmNUIgE1ZcI6lrpU1jOWSIStQCFO6XAmFWirBwLhCucJZIUFqyRqsdW5BLMnTOXeI4feEaayO4RT7-WTFS60BJJf5rJ7PKlk_mnF-uhqi70ycKmDVf4kV8OpSovgD7K5kDA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype><pqid>2899114245</pqid></control><display><type>conference_proceeding</type><title>On the efficiency of long short-term memory in classifying musical impressions from EEG recordings</title><source>AIP Journals Complete</source><creator>Kaya, Burak ; Habiboglu, M. Gokhan ; Moghaddamnia, Sanam</creator><contributor>Cakalli, Huseyin ; Canak, Ibrahim ; Dik, Mehmet ; Kandemir, Hacer Sengul ; Gurtug, Ozay ; Ashyralyev, Allaberen ; Harte, Robin ; Akay, Kadri Ulas ; Kocinac, Ljubisa D. R. ; Aral, Nazlim Deniz ; Ucgun, Filiz Cagatay ; Savas, Ekrem ; Ashyralyyev, Charyyar ; Tez, Mujgan</contributor><creatorcontrib>Kaya, Burak ; Habiboglu, M. Gokhan ; Moghaddamnia, Sanam ; Cakalli, Huseyin ; Canak, Ibrahim ; Dik, Mehmet ; Kandemir, Hacer Sengul ; Gurtug, Ozay ; Ashyralyev, Allaberen ; Harte, Robin ; Akay, Kadri Ulas ; Kocinac, Ljubisa D. R. ; Aral, Nazlim Deniz ; Ucgun, Filiz Cagatay ; Savas, Ekrem ; Ashyralyyev, Charyyar ; Tez, Mujgan</creatorcontrib><description>The objective of this study is the classification of musical impressions with long short-term memory (LSTM) approach using EEG recordings of 20 subjects, while listening to different music genres. For this purpose, a deep learning model was developed, where relevant features extracted from intrinsic mode functions (IMF) of the clean EEG data are used as the input signals. The classification accuracy of the proposed model is evaluated with various feature sets. The highest classification accuracy is 73.33%, which is achieved by combining higher-order statistics and the first difference of IMF features.</description><identifier>ISSN: 0094-243X</identifier><identifier>EISSN: 1551-7616</identifier><identifier>DOI: 10.1063/12.0023973</identifier><identifier>CODEN: APCPCS</identifier><language>eng</language><publisher>Melville: American Institute of Physics</publisher><subject>Electroencephalography ; Signal classification</subject><ispartof>AIP conference proceedings, 2023, Vol.2879 (1)</ispartof><rights>Author(s)</rights><rights>2023 Author(s). Published by AIP Publishing.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://pubs.aip.org/acp/article-lookup/doi/10.1063/12.0023973$$EHTML$$P50$$Gscitation$$H</linktohtml><link.rule.ids>309,310,314,776,780,785,786,790,4498,23909,23910,25118,27901,27902,76127</link.rule.ids></links><search><contributor>Cakalli, Huseyin</contributor><contributor>Canak, Ibrahim</contributor><contributor>Dik, Mehmet</contributor><contributor>Kandemir, Hacer Sengul</contributor><contributor>Gurtug, Ozay</contributor><contributor>Ashyralyev, Allaberen</contributor><contributor>Harte, Robin</contributor><contributor>Akay, Kadri Ulas</contributor><contributor>Kocinac, Ljubisa D. R.</contributor><contributor>Aral, Nazlim Deniz</contributor><contributor>Ucgun, Filiz Cagatay</contributor><contributor>Savas, Ekrem</contributor><contributor>Ashyralyyev, Charyyar</contributor><contributor>Tez, Mujgan</contributor><creatorcontrib>Kaya, Burak</creatorcontrib><creatorcontrib>Habiboglu, M. Gokhan</creatorcontrib><creatorcontrib>Moghaddamnia, Sanam</creatorcontrib><title>On the efficiency of long short-term memory in classifying musical impressions from EEG recordings</title><title>AIP conference proceedings</title><description>The objective of this study is the classification of musical impressions with long short-term memory (LSTM) approach using EEG recordings of 20 subjects, while listening to different music genres. For this purpose, a deep learning model was developed, where relevant features extracted from intrinsic mode functions (IMF) of the clean EEG data are used as the input signals. The classification accuracy of the proposed model is evaluated with various feature sets. The highest classification accuracy is 73.33%, which is achieved by combining higher-order statistics and the first difference of IMF features.</description><subject>Electroencephalography</subject><subject>Signal classification</subject><issn>0094-243X</issn><issn>1551-7616</issn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2023</creationdate><recordtype>conference_proceeding</recordtype><recordid>eNot0EFLwzAUB_AgCs7pxU8Q8CideUnaNEcZcwqDXRS8lTR9cRltU5Pu0G9vZTs9ePz4v8efkEdgK2CFeAG-YowLrcQVWUCeQ6YKKK7JgjEtMy7F9y25S-k4I61UuSD1vqfjASk6563H3k40ONqG_oemQ4hjNmLsaIddiBP1PbWtScm7yc-gOyVvTUt9N0Sct6FP1MXQ0c1mSyPaEJuZpXty40yb8OEyl-TrbfO5fs92--3H-nWXDSDkmNUIgE1ZcI6lrpU1jOWSIStQCFO6XAmFWirBwLhCucJZIUFqyRqsdW5BLMnTOXeI4feEaayO4RT7-WTFS60BJJf5rJ7PKlk_mnF-uhqi70ycKmDVf4kV8OpSovgD7K5kDA</recordid><startdate>20231009</startdate><enddate>20231009</enddate><creator>Kaya, Burak</creator><creator>Habiboglu, M. Gokhan</creator><creator>Moghaddamnia, Sanam</creator><general>American Institute of Physics</general><scope>8FD</scope><scope>H8D</scope><scope>L7M</scope></search><sort><creationdate>20231009</creationdate><title>On the efficiency of long short-term memory in classifying musical impressions from EEG recordings</title><author>Kaya, Burak ; Habiboglu, M. Gokhan ; Moghaddamnia, Sanam</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p134t-be11ed8622e89b7ca00540e06e33a8f5737e947301af67f6fc3414940deb95c13</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Electroencephalography</topic><topic>Signal classification</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kaya, Burak</creatorcontrib><creatorcontrib>Habiboglu, M. Gokhan</creatorcontrib><creatorcontrib>Moghaddamnia, Sanam</creatorcontrib><collection>Technology Research Database</collection><collection>Aerospace Database</collection><collection>Advanced Technologies Database with Aerospace</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kaya, Burak</au><au>Habiboglu, M. Gokhan</au><au>Moghaddamnia, Sanam</au><au>Cakalli, Huseyin</au><au>Canak, Ibrahim</au><au>Dik, Mehmet</au><au>Kandemir, Hacer Sengul</au><au>Gurtug, Ozay</au><au>Ashyralyev, Allaberen</au><au>Harte, Robin</au><au>Akay, Kadri Ulas</au><au>Kocinac, Ljubisa D. R.</au><au>Aral, Nazlim Deniz</au><au>Ucgun, Filiz Cagatay</au><au>Savas, Ekrem</au><au>Ashyralyyev, Charyyar</au><au>Tez, Mujgan</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>On the efficiency of long short-term memory in classifying musical impressions from EEG recordings</atitle><btitle>AIP conference proceedings</btitle><date>2023-10-09</date><risdate>2023</risdate><volume>2879</volume><issue>1</issue><issn>0094-243X</issn><eissn>1551-7616</eissn><coden>APCPCS</coden><abstract>The objective of this study is the classification of musical impressions with long short-term memory (LSTM) approach using EEG recordings of 20 subjects, while listening to different music genres. For this purpose, a deep learning model was developed, where relevant features extracted from intrinsic mode functions (IMF) of the clean EEG data are used as the input signals. The classification accuracy of the proposed model is evaluated with various feature sets. The highest classification accuracy is 73.33%, which is achieved by combining higher-order statistics and the first difference of IMF features.</abstract><cop>Melville</cop><pub>American Institute of Physics</pub><doi>10.1063/12.0023973</doi><tpages>4</tpages></addata></record>
fulltext fulltext
identifier ISSN: 0094-243X
ispartof AIP conference proceedings, 2023, Vol.2879 (1)
issn 0094-243X
1551-7616
language eng
recordid cdi_proquest_journals_2899114245
source AIP Journals Complete
subjects Electroencephalography
Signal classification
title On the efficiency of long short-term memory in classifying musical impressions from EEG recordings
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-30T15%3A11%3A11IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_scita&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=On%20the%20efficiency%20of%20long%20short-term%20memory%20in%20classifying%20musical%20impressions%20from%20EEG%20recordings&rft.btitle=AIP%20conference%20proceedings&rft.au=Kaya,%20Burak&rft.date=2023-10-09&rft.volume=2879&rft.issue=1&rft.issn=0094-243X&rft.eissn=1551-7616&rft.coden=APCPCS&rft_id=info:doi/10.1063/12.0023973&rft_dat=%3Cproquest_scita%3E2899114245%3C/proquest_scita%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2899114245&rft_id=info:pmid/&rfr_iscdi=true