Application of Empirical Mode Decomposition and Teager energy operator to EEG signals for mental task classification
This paper presents a novel method for mental task classification from EEG signals using Empirical Mode Decomposition and Teager energy operator techniques on EEG data. The efficacy of these techniques for non-stationary and non-linear data has already been demonstrated, which therefore lend themsel...
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description | This paper presents a novel method for mental task classification from EEG signals using Empirical Mode Decomposition and Teager energy operator techniques on EEG data. The efficacy of these techniques for non-stationary and non-linear data has already been demonstrated, which therefore lend themselves well to EEG signals, which are also non-stationary and non-linear in nature. The method described in this paper decomposed the EEG signals (6 EEG signals per task per subject, for a total of 5 tasks over multiple trials) into their constituent oscillatory modes, called intrinsic mode functions, and separated out the trend from the signal. Teager energy operator was used to calculate the average energy of both the detrended signal and the trend. The average energy was used to construct separate feature vectors with a small number of parameters for the detrended signal and the trend. Based on these parameters, one-versus-one classification of mental tasks was performed using Linear Discriminant Analysis. Using both feature vectors, an average correct classification rate of more than 85% was achieved, demonstrating the effectiveness of the method used. Furthermore, this method used all the intrinsic mode functions, as opposed to similar studies, demonstrating that the trend of the EEG signal also contains important discriminatory information. |
doi_str_mv | 10.1109/IEMBS.2010.5626501 |
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The efficacy of these techniques for non-stationary and non-linear data has already been demonstrated, which therefore lend themselves well to EEG signals, which are also non-stationary and non-linear in nature. The method described in this paper decomposed the EEG signals (6 EEG signals per task per subject, for a total of 5 tasks over multiple trials) into their constituent oscillatory modes, called intrinsic mode functions, and separated out the trend from the signal. Teager energy operator was used to calculate the average energy of both the detrended signal and the trend. The average energy was used to construct separate feature vectors with a small number of parameters for the detrended signal and the trend. Based on these parameters, one-versus-one classification of mental tasks was performed using Linear Discriminant Analysis. Using both feature vectors, an average correct classification rate of more than 85% was achieved, demonstrating the effectiveness of the method used. 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The efficacy of these techniques for non-stationary and non-linear data has already been demonstrated, which therefore lend themselves well to EEG signals, which are also non-stationary and non-linear in nature. The method described in this paper decomposed the EEG signals (6 EEG signals per task per subject, for a total of 5 tasks over multiple trials) into their constituent oscillatory modes, called intrinsic mode functions, and separated out the trend from the signal. Teager energy operator was used to calculate the average energy of both the detrended signal and the trend. The average energy was used to construct separate feature vectors with a small number of parameters for the detrended signal and the trend. Based on these parameters, one-versus-one classification of mental tasks was performed using Linear Discriminant Analysis. Using both feature vectors, an average correct classification rate of more than 85% was achieved, demonstrating the effectiveness of the method used. Furthermore, this method used all the intrinsic mode functions, as opposed to similar studies, demonstrating that the trend of the EEG signal also contains important discriminatory information.</description><subject>Algorithms</subject><subject>Brain - physiopathology</subject><subject>Brain Mapping - methods</subject><subject>Cognition - physiology</subject><subject>Conferences</subject><subject>Electroencephalography</subject><subject>Electroencephalography - methods</subject><subject>Feature extraction</subject><subject>Humans</subject><subject>Indexes</subject><subject>Signal analysis</subject><subject>Task Performance and Analysis</subject><subject>USA Councils</subject><subject>Vectors</subject><issn>1094-687X</issn><issn>1557-170X</issn><issn>1558-4615</issn><isbn>1424441234</isbn><isbn>9781424441235</isbn><isbn>1424441242</isbn><isbn>9781424441242</isbn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2010</creationdate><recordtype>article</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><sourceid>EIF</sourceid><recordid>eNpFkMlOAzEQRM0mssAPgIT8AxO8xz6GMIRIiTgQJG6RZ6YdGWbTeDjk77FIgFOr-pWqVY3QDSUTSom5X6brh9cJI1FLxZQk9ASNqGBCCMoEO0VDKqVOhKLy7B9wcR4BMSJRevo-QKMQPghhhEh6iQYsEsWYGKJ-1ralz23vmxo3DqdV67uoS7xuCsCPkDdV2wT_w21d4A3YHXQYauh2e9y00Nm-6XDf4DRd4OB3tS0DdnFVQd3HnN6GT5yXNgTvjoeu0IWLLrg-zjF6e0o38-dk9bJYzmerxHPO-0QZIZ2RijtwGWiuwQitlNKZNVNTAJFEa2sK4sAC1VJkqsggN0xPDWMu52N0d8htv7IKim3b-cp2--1v_Wi4PRg8APzh45v5N9Z-a-0</recordid><startdate>20100101</startdate><enddate>20100101</enddate><creator>Kaleem, M F</creator><creator>Sugavaneswaran, L</creator><creator>Guergachi, A</creator><creator>Krishnan, S</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope></search><sort><creationdate>20100101</creationdate><title>Application of Empirical Mode Decomposition and Teager energy operator to EEG signals for mental task classification</title><author>Kaleem, M F ; Sugavaneswaran, L ; Guergachi, A ; Krishnan, S</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i333t-6945f9563fefbe838e9486668ba979de05088a9d0feae1854b6dbec9287922fc3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Algorithms</topic><topic>Brain - physiopathology</topic><topic>Brain Mapping - methods</topic><topic>Cognition - physiology</topic><topic>Conferences</topic><topic>Electroencephalography</topic><topic>Electroencephalography - methods</topic><topic>Feature extraction</topic><topic>Humans</topic><topic>Indexes</topic><topic>Signal analysis</topic><topic>Task Performance and Analysis</topic><topic>USA Councils</topic><topic>Vectors</topic><toplevel>online_resources</toplevel><creatorcontrib>Kaleem, M F</creatorcontrib><creatorcontrib>Sugavaneswaran, L</creatorcontrib><creatorcontrib>Guergachi, A</creatorcontrib><creatorcontrib>Krishnan, S</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><jtitle>2010 Annual International Conference of the IEEE Engineering in Medicine and Biology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Kaleem, M F</au><au>Sugavaneswaran, L</au><au>Guergachi, A</au><au>Krishnan, S</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Application of Empirical Mode Decomposition and Teager energy operator to EEG signals for mental task classification</atitle><jtitle>2010 Annual International Conference of the IEEE Engineering in Medicine and Biology</jtitle><stitle>IEMBS</stitle><addtitle>Conf Proc IEEE Eng Med Biol Soc</addtitle><date>2010-01-01</date><risdate>2010</risdate><volume>2010</volume><spage>4590</spage><epage>4593</epage><pages>4590-4593</pages><issn>1094-687X</issn><issn>1557-170X</issn><eissn>1558-4615</eissn><isbn>1424441234</isbn><isbn>9781424441235</isbn><eisbn>1424441242</eisbn><eisbn>9781424441242</eisbn><abstract>This paper presents a novel method for mental task classification from EEG signals using Empirical Mode Decomposition and Teager energy operator techniques on EEG data. The efficacy of these techniques for non-stationary and non-linear data has already been demonstrated, which therefore lend themselves well to EEG signals, which are also non-stationary and non-linear in nature. The method described in this paper decomposed the EEG signals (6 EEG signals per task per subject, for a total of 5 tasks over multiple trials) into their constituent oscillatory modes, called intrinsic mode functions, and separated out the trend from the signal. Teager energy operator was used to calculate the average energy of both the detrended signal and the trend. The average energy was used to construct separate feature vectors with a small number of parameters for the detrended signal and the trend. Based on these parameters, one-versus-one classification of mental tasks was performed using Linear Discriminant Analysis. Using both feature vectors, an average correct classification rate of more than 85% was achieved, demonstrating the effectiveness of the method used. Furthermore, this method used all the intrinsic mode functions, as opposed to similar studies, demonstrating that the trend of the EEG signal also contains important discriminatory information.</abstract><cop>United States</cop><pub>IEEE</pub><pmid>21096224</pmid><doi>10.1109/IEMBS.2010.5626501</doi><tpages>4</tpages></addata></record> |
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subjects | Algorithms Brain - physiopathology Brain Mapping - methods Cognition - physiology Conferences Electroencephalography Electroencephalography - methods Feature extraction Humans Indexes Signal analysis Task Performance and Analysis USA Councils Vectors |
title | Application of Empirical Mode Decomposition and Teager energy operator to EEG signals for mental task classification |
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