EEG-Based Emotion Recognition Using Deep Learning Network with Principal Component Based Covariate Shift Adaptation
Automatic emotion recognition is one of the most challenging tasks. To detect emotion from nonstationary EEG signals, a sophisticated learning algorithm that can represent high-level abstraction is required. This study proposes the utilization of a deep learning network (DLN) to discover unknown fea...
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| Published in: | TheScientificWorld Vol. 2014; no. 2014; pp. 1 - 10 |
|---|---|
| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Cairo, Egypt
Hindawi Publishing Corporation
01.01.2014
John Wiley & Sons, Inc Wiley |
| Subjects: | |
| ISSN: | 2356-6140, 1537-744X, 1537-744X |
| Online Access: | Get full text |
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| Abstract | Automatic emotion recognition is one of the most challenging tasks. To detect emotion from nonstationary EEG signals, a sophisticated learning algorithm that can represent high-level abstraction is required. This study proposes the utilization of a deep learning network (DLN) to discover unknown feature correlation between input signals that is crucial for the learning task. The DLN is implemented with a stacked autoencoder (SAE) using hierarchical feature learning approach. Input features of the network are power spectral densities of 32-channel EEG signals from 32 subjects. To alleviate overfitting problem, principal component analysis (PCA) is applied to extract the most important components of initial input features. Furthermore, covariate shift adaptation of the principal components is implemented to minimize the nonstationary effect of EEG signals. Experimental results show that the DLN is capable of classifying three different levels of valence and arousal with accuracy of 49.52% and 46.03%, respectively. Principal component based covariate shift adaptation enhances the respective classification accuracy by 5.55% and 6.53%. Moreover, DLN provides better performance compared to SVM and naive Bayes classifiers. |
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| AbstractList | Automatic emotion recognition is one of the most challenging tasks. To detect emotion from nonstationary EEG signals, a sophisticated learning algorithm that can represent high-level abstraction is required. This study proposes the utilization of a deep learning network (DLN) to discover unknown feature correlation between input signals that is crucial for the learning task. The DLN is implemented with a stacked autoencoder (SAE) using hierarchical feature learning approach. Input features of the network are power spectral densities of 32-channel EEG signals from 32 subjects. To alleviate overfitting problem, principal component analysis (PCA) is applied to extract the most important components of initial input features. Furthermore, covariate shift adaptation of the principal components is implemented to minimize the nonstationary effect of EEG signals. Experimental results show that the DLN is capable of classifying three different levels of valence and arousal with accuracy of 49.52% and 46.03%, respectively. Principal component based covariate shift adaptation enhances the respective classification accuracy by 5.55% and 6.53%. Moreover, DLN provides better performance compared to SVM and naive Bayes classifiers. Automatic emotion recognition is one of the most challenging tasks. To detect emotion from nonstationary EEG signals, a sophisticated learning algorithm that can represent high-level abstraction is required. This study proposes the utilization of a deep learning network (DLN) to discover unknown feature correlation between input signals that is crucial for the learning task. The DLN is implemented with a stacked autoencoder (SAE) using hierarchical feature learning approach. Input features of the network are power spectral densities of 32-channel EEG signals from 32 subjects. To alleviate overfitting problem, principal component analysis (PCA) is applied to extract the most important components of initial input features. Furthermore, covariate shift adaptation of the principal components is implemented to minimize the nonstationary effect of EEG signals. Experimental results show that the DLN is capable of classifying three different levels of valence and arousal with accuracy of 49.52% and 46.03%, respectively. Principal component based covariate shift adaptation enhances the respective classification accuracy by 5.55% and 6.53%. Moreover, DLN provides better performance compared to SVM and naive Bayes classifiers.Automatic emotion recognition is one of the most challenging tasks. To detect emotion from nonstationary EEG signals, a sophisticated learning algorithm that can represent high-level abstraction is required. This study proposes the utilization of a deep learning network (DLN) to discover unknown feature correlation between input signals that is crucial for the learning task. The DLN is implemented with a stacked autoencoder (SAE) using hierarchical feature learning approach. Input features of the network are power spectral densities of 32-channel EEG signals from 32 subjects. To alleviate overfitting problem, principal component analysis (PCA) is applied to extract the most important components of initial input features. Furthermore, covariate shift adaptation of the principal components is implemented to minimize the nonstationary effect of EEG signals. Experimental results show that the DLN is capable of classifying three different levels of valence and arousal with accuracy of 49.52% and 46.03%, respectively. Principal component based covariate shift adaptation enhances the respective classification accuracy by 5.55% and 6.53%. Moreover, DLN provides better performance compared to SVM and naive Bayes classifiers. Automatic emotion recognition is one of the most challenging tasks. To detect emotion from nonstationary EEG signals, a sophisticated learning algorithm that can represent high-level abstraction is required. This study proposes the utilization of a deep learning network (DLN) to discover unknown feature correlation between input signals that is crucial for the learning task. The DLN is implemented with a stacked autoencoder (SAE) using hierarchical feature learning approach. Input features of the network are power spectral densities of 32-channel EEG signals from 32 subjects. To alleviate overfitting problem, principal component analysis (PCA) is applied to extract the most important components of initial input features. Furthermore, covariate shift adaptation of the principal components is implemented to minimize the nonstationary effect of EEG signals. Experimental results show that the DLN is capable of classifying three different levels of valence and arousal with accuracy of49.52% and 46.03%, respectively. Principal component based covariate shift adaptation enhances the respective classification accuracy by 5.55% and 6.53%. Moreover, DLN provides better performance compared to SVM and naive Bayes classifiers. |
| Audience | Academic |
| Author | Jirayucharoensak, Suwicha Pan-ngum, Setha Israsena, P. |
| AuthorAffiliation | 2 National Electronics and Computer Technology Center, Thailand Science Park, Khlong Luang, Pathum Thani 12120, Thailand 1 Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok 10330, Thailand |
| AuthorAffiliation_xml | – name: 1 Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok 10330, Thailand – name: 2 National Electronics and Computer Technology Center, Thailand Science Park, Khlong Luang, Pathum Thani 12120, Thailand |
| Author_xml | – sequence: 1 fullname: Jirayucharoensak, Suwicha – sequence: 2 fullname: Israsena, P. – sequence: 3 fullname: Pan-ngum, Setha |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/25258728$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1155/2012/107046 10.1145/1961189.1961199 10.1109/TBME.2013.2253608 10.1186/1687-6180-2012-129 10.1007/978-3-642-10439-8_6 10.1109/T-AFFC.2011.15 10.1016/j.ijhcs.2009.03.005 10.1155/2013/618649 10.1097/00004691-199104000-00007 10.1016/0893-6080(89)90014-2 10.1007/978-1-4757-1904-8 10.1109/T-AFFC.2011.25 10.1109/TSMCA.2011.2116000 10.1162/neco.2006.18.7.1527 10.1088/1741-2560/8/3/036015 |
| ContentType | Journal Article |
| Copyright | Copyright © 2014 Suwicha Jirayucharoensak et al. COPYRIGHT 2014 John Wiley & Sons, Inc. Copyright © 2014 Suwicha Jirayucharoensak et al. Suwicha Jirayucharoensak et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Copyright © 2014 Suwicha Jirayucharoensak et al. 2014 |
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| References | (21) 1995; 35 Naveen R. S. Julian A. Brain computing interface for wheel chair control Proceedings of the 4th International Conference on Computing, Communications and Networking Technologies (ICCCNT '13) July 2013 Tiruchengode, India 1 5 10.1109/ICCCNT.2013.6726572 (20) 2012; 3 Li K. Li X. Zhang Y. Affective state recognition from EEG with deep belief networks Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine 2013 (25) 2011; 2 Chanel G. Kronegg J. Grandjean D. Pun T. Gunsel B. Jain A. Tekalp A. M. Sankur B. Emotion assessment: arousal evaluation using EEG's and peripheral physiological signals Multimedia Content Representation, Classification and Security 2006 4105 Berlin, Germany Springer 530 537 (18) 2011; 8 (8) 2009; 67 (14) 2011; 41 (19) 2012; 2012 (28) 2013; 60 (17) 2006; 18 (22) 2012; 2012, article 129 (24) 1989; 2 Koelstra S. Yazdani A. Soleymani M. Single trial classification of EEG and peripheral physiological signals for recognition of emotions induced by music videos Proceedings of the International Conference on Brain Informatics 2010 Toronto, Canada Wijeratne U. Perera U. Intelligent emotion recognition system using electroencephalography and active shape models Proceedings of the 2nd IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES '12) December 2012 636 641 10.1109/IECBES.2012.6498051 2-s2.0-84876781936 (11) 2013; 2013 Wang X.-W. Nie D. Lu B.-L. Lu B.-L. Zhang L. Kwok J. EEG-based emotion recognition using frequency domain features and support vector machines Neural Information Processing 2011 7062 Berlin, Germany Springer 734 743 Jolliffe I. T. Principal Component Analysis 1986 New York, NY. USA Springer 10.1007/978-1-4757-1904-8 MR841268 Akram F. Metwally M. K. Han H. Jeon H. Kim T. A novel P300-based BCI system for words typing Proceedings of the International Winter Workshop on Brain-Computer Interface (BCI '13) February 2013 24 25 10.1109/IWW-BCI.2013.6506617 2-s2.0-84877701088 AlZoubi O. Calvo R. A. Stevens R. H. Nicholson A. Li X. Classification of EEG for affect recognition: an adaptive approach AI 2009: Advances in Artificial Intelligence 2009 5866 Berlin, Germany Springer 52 61 Lecture Notes in Computer Science Chung S. Y. Yoon H. J. Affective classification using Bayesian classifier and supervised learning Proceedings of the 12th International Conference on Control, Automation and Systems (ICCAS '12) October 2012 1768 1771 2-s2.0-84872523562 Lotte F. Guan C. Learning from other subjects helps reducing brain-computer interface calibration time Proceedings of the 35th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '10) March 2010 Dallas, Tex, USA 614 617 10.1109/ICASSP.2010.5495183 2-s2.0-78049377043 Wikipedia Electroencephalography March 2014, http://en.wikipedia.org/wiki/Electroencephalography Nie D. Wang X.-W. Shi L.-C. Lu B.-L. EEG-based emotion recognition during watching movies Proceedings of the 5th International IEEE/EMBS Conference on Neural Engineering (NER '11) May 2011 Cancun, Mexico 667 670 10.1109/NER.2011.5910636 2-s2.0-79960373110 (6) 2012; 3 (3) 1991; 8 Huang D. Guan C. Ang K. K. Zhang H. Pan Y. Asymmetric spatial pattern for EEG-based emotion detection Proceeding of the International Joint Conference on Neural Networks (IJCNN '12) June 2012 Brisbane, Australia 1 7 10.1109/IJCNN.2012.6252390 2-s2.0-84865063644 11 22 23 (13) 2009; 5866 24 14 16 17 28 18 19 (20) 1995; 35 (10) 2011; 7062 6 (12) 2006; 4105 8 21 |
| References_xml | – volume: 2 start-page: 53 issue: 1 year: 1989 end-page: 56 ident: 24 article-title: Neural networks and principal component analysis: learning from examples without local minima – volume: 2 issue: 3 year: 2011 ident: 25 article-title: LIBSVM: a Library for support vector machines – volume: 41 start-page: 1052 issue: 6 year: 2011 end-page: 1063 ident: 14 article-title: Emotion assessment from physiological signals for adaptation of game difficulty – reference: Chanel G. Kronegg J. Grandjean D. Pun T. Gunsel B. Jain A. Tekalp A. M. Sankur B. Emotion assessment: arousal evaluation using EEG's and peripheral physiological signals Multimedia Content Representation, Classification and Security 2006 4105 Berlin, Germany Springer 530 537 – reference: Wang X.-W. Nie D. Lu B.-L. Lu B.-L. Zhang L. Kwok J. EEG-based emotion recognition using frequency domain features and support vector machines Neural Information Processing 2011 7062 Berlin, Germany Springer 734 743 – volume: 3 start-page: 18 issue: 1 year: 2012 end-page: 31 ident: 20 article-title: DEAP: a database for emotion analysis; using physiological signals – volume: 2013 year: 2013 end-page: 12 ident: 11 article-title: Real-time EEG-based happiness detection system – volume: 35 start-page: 63 issue: 8 year: 1995 end-page: 68 ident: 21 article-title: SAM: the self-assessment manikin. An efficient cross-cultural measurement of emotion response – reference: Chung S. Y. Yoon H. J. Affective classification using Bayesian classifier and supervised learning Proceedings of the 12th International Conference on Control, Automation and Systems (ICCAS '12) October 2012 1768 1771 2-s2.0-84872523562 – volume: 3 start-page: 42 issue: 1 year: 2012 end-page: 55 ident: 6 article-title: A multimodal database for affect recognition and implicit tagging – volume: 8 start-page: 200 issue: 2 year: 1991 end-page: 202 ident: 3 article-title: American Electroencephalographic Society guidelines for standard electrode position nomenclature – reference: Koelstra S. Yazdani A. Soleymani M. Single trial classification of EEG and peripheral physiological signals for recognition of emotions induced by music videos Proceedings of the International Conference on Brain Informatics 2010 Toronto, Canada – volume: 18 start-page: 1527 issue: 7 year: 2006 end-page: 1554 ident: 17 article-title: A fast learning algorithm for deep belief nets – reference: Jolliffe I. T. Principal Component Analysis 1986 New York, NY. USA Springer 10.1007/978-1-4757-1904-8 MR841268 – volume: 8 issue: 3 year: 2011 ident: 18 article-title: Modeling electroencephalography waveforms with semi-supervised deep belief nets: fast classification and anomaly measurement – reference: Wikipedia Electroencephalography March 2014, http://en.wikipedia.org/wiki/Electroencephalography – volume: 60 start-page: 2289 issue: 8 year: 2013 end-page: 2298 ident: 28 article-title: Transferring subspaces between subjects in brain—computer interfacing – reference: Li K. Li X. Zhang Y. Affective state recognition from EEG with deep belief networks Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine 2013 – reference: Huang D. Guan C. Ang K. K. Zhang H. Pan Y. Asymmetric spatial pattern for EEG-based emotion detection Proceeding of the International Joint Conference on Neural Networks (IJCNN '12) June 2012 Brisbane, Australia 1 7 10.1109/IJCNN.2012.6252390 2-s2.0-84865063644 – reference: AlZoubi O. Calvo R. A. Stevens R. H. Nicholson A. Li X. Classification of EEG for affect recognition: an adaptive approach AI 2009: Advances in Artificial Intelligence 2009 5866 Berlin, Germany Springer 52 61 Lecture Notes in Computer Science – volume: 2012, article 129 year: 2012 ident: 22 article-title: Principal component based covariate shift adaption to reduce non-stationarity in a MEG-based brain-computer interface – reference: Nie D. Wang X.-W. Shi L.-C. Lu B.-L. EEG-based emotion recognition during watching movies Proceedings of the 5th International IEEE/EMBS Conference on Neural Engineering (NER '11) May 2011 Cancun, Mexico 667 670 10.1109/NER.2011.5910636 2-s2.0-79960373110 – volume: 67 start-page: 607 issue: 8 year: 2009 end-page: 627 ident: 8 article-title: Short-term emotion assessment in a recall paradigm – reference: Lotte F. Guan C. Learning from other subjects helps reducing brain-computer interface calibration time Proceedings of the 35th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '10) March 2010 Dallas, Tex, USA 614 617 10.1109/ICASSP.2010.5495183 2-s2.0-78049377043 – volume: 2012 year: 2012 end-page: 9 ident: 19 article-title: Sleep stage classification using unsupervised feature learning – reference: Naveen R. S. Julian A. Brain computing interface for wheel chair control Proceedings of the 4th International Conference on Computing, Communications and Networking Technologies (ICCCNT '13) July 2013 Tiruchengode, India 1 5 10.1109/ICCCNT.2013.6726572 – reference: Akram F. Metwally M. K. Han H. Jeon H. Kim T. A novel P300-based BCI system for words typing Proceedings of the International Winter Workshop on Brain-Computer Interface (BCI '13) February 2013 24 25 10.1109/IWW-BCI.2013.6506617 2-s2.0-84877701088 – reference: Wijeratne U. Perera U. Intelligent emotion recognition system using electroencephalography and active shape models Proceedings of the 2nd IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES '12) December 2012 636 641 10.1109/IECBES.2012.6498051 2-s2.0-84876781936 – ident: 18 doi: 10.1155/2012/107046 – ident: 24 doi: 10.1145/1961189.1961199 – ident: 28 doi: 10.1109/TBME.2013.2253608 – ident: 21 doi: 10.1186/1687-6180-2012-129 – volume: 5866 start-page: 52 volume-title: Classification of EEG for affect recognition: an adaptive approach year: 2009 ident: 13 doi: 10.1007/978-3-642-10439-8_6 – ident: 19 doi: 10.1109/T-AFFC.2011.15 – ident: 8 doi: 10.1016/j.ijhcs.2009.03.005 – ident: 11 doi: 10.1155/2013/618649 – volume: 8 start-page: 200 issue: 2 year: 1991 ident: 3 publication-title: Journal of Clinical Neurophysiology doi: 10.1097/00004691-199104000-00007 – volume: 35 start-page: 63 issue: 8 year: 1995 ident: 20 publication-title: Jounal of Advertising Research – ident: 23 doi: 10.1016/0893-6080(89)90014-2 – volume: 4105 start-page: 530 volume-title: Emotion assessment: arousal evaluation using EEG's and peripheral physiological signals year: 2006 ident: 12 – ident: 22 doi: 10.1007/978-1-4757-1904-8 – volume: 7062 start-page: 734 volume-title: EEG-based emotion recognition using frequency domain features and support vector machines year: 2011 ident: 10 – ident: 6 doi: 10.1109/T-AFFC.2011.25 – ident: 14 doi: 10.1109/TSMCA.2011.2116000 – ident: 16 doi: 10.1162/neco.2006.18.7.1527 – ident: 17 doi: 10.1088/1741-2560/8/3/036015 |
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| SubjectTerms | Accuracy Algorithms Architectural engineering Arousal - physiology Brain research Classification Electrodes Electroencephalography Electroencephalography - methods Emotions Emotions - physiology Humans Identification and classification Methods Nerve Net Neural Networks, Computer Principal Component Analysis - methods Principal components analysis Reproducibility of Results Signal processing Support Vector Machine Task Performance and Analysis |
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| Title | EEG-Based Emotion Recognition Using Deep Learning Network with Principal Component Based Covariate Shift Adaptation |
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