Emotion classification based on forehead biosignals using support vector machines in music listening
The purpose of this study was to investigate the feasibility of using forehead biosignals as informative channels for classification of music-induced emotions. Classification of four emotional states in Arousal-Valence space was performed by employing two parallel support vector machines as arousal...
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| creator | Naji, M. Firoozabadi, M. Azadfallah, P. |
| description | The purpose of this study was to investigate the feasibility of using forehead biosignals as informative channels for classification of music-induced emotions. Classification of four emotional states in Arousal-Valence space was performed by employing two parallel support vector machines as arousal and valence classifiers. Relative powers of EEG sub-bands, spectral entropy, mean power frequency, and higher order crossings were extracted from each of the three forehead data channels: left Temporalis, Frontalis, and right Temporalis. The inputs of the classifiers were obtained by a feature selection algorithm based on a fuzzy-rough model. The averaged subject-independent classification accuracy of 93.80%, 92.43%, and 86.67% for arousal classification, valence classification, and classification of four emotional states in Arousal-Valence space, respectively, is achieved. |
| doi_str_mv | 10.1109/BIBE.2012.6399657 |
| format | Conference Proceeding |
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Classification of four emotional states in Arousal-Valence space was performed by employing two parallel support vector machines as arousal and valence classifiers. Relative powers of EEG sub-bands, spectral entropy, mean power frequency, and higher order crossings were extracted from each of the three forehead data channels: left Temporalis, Frontalis, and right Temporalis. The inputs of the classifiers were obtained by a feature selection algorithm based on a fuzzy-rough model. The averaged subject-independent classification accuracy of 93.80%, 92.43%, and 86.67% for arousal classification, valence classification, and classification of four emotional states in Arousal-Valence space, respectively, is achieved.</description><identifier>ISBN: 9781467343572</identifier><identifier>ISBN: 1467343579</identifier><identifier>EISBN: 1467343587</identifier><identifier>EISBN: 9781467343565</identifier><identifier>EISBN: 1467343560</identifier><identifier>EISBN: 9781467343589</identifier><identifier>DOI: 10.1109/BIBE.2012.6399657</identifier><language>eng</language><publisher>IEEE</publisher><subject>Accuracy ; arousal ; Electrodes ; Electroencephalography ; emotion classification ; Emotion recognition ; Feature extraction ; Forehead ; forehead biosignals ; Support vector machines ; valence</subject><ispartof>2012 IEEE 12th International Conference on Bioinformatics & Bioengineering (BIBE), 2012, p.396-400</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/6399657$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>310,311,781,785,790,791,2059,27927,54922</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6399657$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Naji, M.</creatorcontrib><creatorcontrib>Firoozabadi, M.</creatorcontrib><creatorcontrib>Azadfallah, P.</creatorcontrib><title>Emotion classification based on forehead biosignals using support vector machines in music listening</title><title>2012 IEEE 12th International Conference on Bioinformatics & Bioengineering (BIBE)</title><addtitle>BIBE</addtitle><description>The purpose of this study was to investigate the feasibility of using forehead biosignals as informative channels for classification of music-induced emotions. Classification of four emotional states in Arousal-Valence space was performed by employing two parallel support vector machines as arousal and valence classifiers. Relative powers of EEG sub-bands, spectral entropy, mean power frequency, and higher order crossings were extracted from each of the three forehead data channels: left Temporalis, Frontalis, and right Temporalis. The inputs of the classifiers were obtained by a feature selection algorithm based on a fuzzy-rough model. The averaged subject-independent classification accuracy of 93.80%, 92.43%, and 86.67% for arousal classification, valence classification, and classification of four emotional states in Arousal-Valence space, respectively, is achieved.</description><subject>Accuracy</subject><subject>arousal</subject><subject>Electrodes</subject><subject>Electroencephalography</subject><subject>emotion classification</subject><subject>Emotion recognition</subject><subject>Feature extraction</subject><subject>Forehead</subject><subject>forehead biosignals</subject><subject>Support vector machines</subject><subject>valence</subject><isbn>9781467343572</isbn><isbn>1467343579</isbn><isbn>1467343587</isbn><isbn>9781467343565</isbn><isbn>1467343560</isbn><isbn>9781467343589</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2012</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo1kMtKA0EURFtEUGM-QNz0DyT2Y6YfSxNGDQTc6Drc230naZlHmJ4I_r2DxlVVwalaFGP3UiylFP5xtVlVSyWkWhrtvSntBbuVhbG60KWzl2zurfvPVl2zec6fQggpdGGMv2Gxavsx9R0PDeSc6hTgNyJkinwydT_QgSByTH1O-w6azE85dXueT8djP4z8i8LYD7yFcEgdZZ463k5E4E3KI3UTeseu6qlH87PO2Mdz9b5-XWzfXjbrp-0iqUKOC7S6DIioFCFKBAfoEEoPFA0ZF0N0pvZkQpQRpHOhLrwrnBVYo9Ia9Iw9_O0mItodh9TC8L07H6N_AEHeWng</recordid><startdate>201211</startdate><enddate>201211</enddate><creator>Naji, M.</creator><creator>Firoozabadi, M.</creator><creator>Azadfallah, P.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201211</creationdate><title>Emotion classification based on forehead biosignals using support vector machines in music listening</title><author>Naji, M. ; Firoozabadi, M. ; Azadfallah, P.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i241t-b735cbbb22ebb1ba8ab8ba59aed6e68dcd86f9e6cd1da188cf4984870bfb233a3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Accuracy</topic><topic>arousal</topic><topic>Electrodes</topic><topic>Electroencephalography</topic><topic>emotion classification</topic><topic>Emotion recognition</topic><topic>Feature extraction</topic><topic>Forehead</topic><topic>forehead biosignals</topic><topic>Support vector machines</topic><topic>valence</topic><toplevel>online_resources</toplevel><creatorcontrib>Naji, M.</creatorcontrib><creatorcontrib>Firoozabadi, M.</creatorcontrib><creatorcontrib>Azadfallah, P.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Naji, M.</au><au>Firoozabadi, M.</au><au>Azadfallah, P.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Emotion classification based on forehead biosignals using support vector machines in music listening</atitle><btitle>2012 IEEE 12th International Conference on Bioinformatics & Bioengineering (BIBE)</btitle><stitle>BIBE</stitle><date>2012-11</date><risdate>2012</risdate><spage>396</spage><epage>400</epage><pages>396-400</pages><isbn>9781467343572</isbn><isbn>1467343579</isbn><eisbn>1467343587</eisbn><eisbn>9781467343565</eisbn><eisbn>1467343560</eisbn><eisbn>9781467343589</eisbn><abstract>The purpose of this study was to investigate the feasibility of using forehead biosignals as informative channels for classification of music-induced emotions. Classification of four emotional states in Arousal-Valence space was performed by employing two parallel support vector machines as arousal and valence classifiers. Relative powers of EEG sub-bands, spectral entropy, mean power frequency, and higher order crossings were extracted from each of the three forehead data channels: left Temporalis, Frontalis, and right Temporalis. The inputs of the classifiers were obtained by a feature selection algorithm based on a fuzzy-rough model. The averaged subject-independent classification accuracy of 93.80%, 92.43%, and 86.67% for arousal classification, valence classification, and classification of four emotional states in Arousal-Valence space, respectively, is achieved.</abstract><pub>IEEE</pub><doi>10.1109/BIBE.2012.6399657</doi><tpages>5</tpages></addata></record> |
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| ispartof | 2012 IEEE 12th International Conference on Bioinformatics & Bioengineering (BIBE), 2012, p.396-400 |
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| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Accuracy arousal Electrodes Electroencephalography emotion classification Emotion recognition Feature extraction Forehead forehead biosignals Support vector machines valence |
| title | Emotion classification based on forehead biosignals using support vector machines in music listening |
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