The role of empirical mode decomposition on emotion classification using stimulated eeg signals

Anwesha Khasnobish; Saugat Bhattacharyya; Garima Singh; Arindam Jati; Amit Konar; D. N. Tibarewala; R. Janarthanan

doi:10.1007/978-3-642-31600-5_6

The role of empirical mode decomposition on emotion classification using stimulated eeg signals

Anwesha Khasnobish, Saugat Bhattacharyya, Garima Singh, Arindam Jati, Amit Konar, D. N. Tibarewala, R. Janarthanan

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

7 Citations (Scopus)

Abstract

An efficient scheme of emotion recognition using EEG signals is an initiation to our quest for developing emotionally intelligent systems and devices, in order to enhance the performance quality of the same. Classification of emotions, both euphoric and negative, using stimulated EEG signals acquired from subjects whose different emotional states were elicited using audio-visual stimuli. The underlying strategy involved the extraction of Power spectral density(PSD) and empirical mode decomposition (EMD) features from the raw EEG data and their classification using linear discriminant analysis (LDA) and linear support vector machine (SVM) thereby classifying the emotions into their respective emotion classes: neutral, happy and sad, with an average classification accuracy of 76.46%,where the neutral state has been classified most efficiently, with an average classification accuracy of 80.86%. The classification accuracy increases with EMD features with reduction in time and computational complexity. LDA is found to perform better than LSVM with EMD features.

Original language	English
Title of host publication	Advances in Computing and Information Technology- Proceedings of the 2nd International Conference on Advances in Computing and Information Technology, ACITY 2012- Volume 3
Editors	Natarajan Meghanathan, Nabendu Chaki, Dhinaharan Nagamalai
Publisher	Springer Verlag
Pages	55-62
Number of pages	8
ISBN (Electronic)	978-3-642-31600-5
ISBN (Print)	9783642315992
DOIs	https://doi.org/10.1007/978-3-642-31600-5_6
Publication status	Published (in print/issue) - 1 Jan 2013
Event	2nd International Conference on Advances in Computing and Information Technology, ACITY 2012 - Chennai, India Duration: 13 Jul 2012 → 15 Jul 2012

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	178
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Conference

Conference	2nd International Conference on Advances in Computing and Information Technology, ACITY 2012
Country/Territory	India
City	Chennai
Period	13/07/12 → 15/07/12

Keywords

EEG
EMD
Emotion recognition
LDA
PSD
SVM

Access to Document

10.1007/978-3-642-31600-5_6

Cite this

Khasnobish, A., Bhattacharyya, S., Singh, G., Jati, A., Konar, A., Tibarewala, D. N., & Janarthanan, R. (2013). The role of empirical mode decomposition on emotion classification using stimulated eeg signals. In N. Meghanathan, N. Chaki, & D. Nagamalai (Eds.), Advances in Computing and Information Technology- Proceedings of the 2nd International Conference on Advances in Computing and Information Technology, ACITY 2012- Volume 3 (pp. 55-62). (Advances in Intelligent Systems and Computing; Vol. 178). Springer Verlag. https://doi.org/10.1007/978-3-642-31600-5_6

Khasnobish, Anwesha ; Bhattacharyya, Saugat ; Singh, Garima et al. / The role of empirical mode decomposition on emotion classification using stimulated eeg signals. Advances in Computing and Information Technology- Proceedings of the 2nd International Conference on Advances in Computing and Information Technology, ACITY 2012- Volume 3. editor / Natarajan Meghanathan ; Nabendu Chaki ; Dhinaharan Nagamalai. Springer Verlag, 2013. pp. 55-62 (Advances in Intelligent Systems and Computing).

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title = "The role of empirical mode decomposition on emotion classification using stimulated eeg signals",

abstract = "An efficient scheme of emotion recognition using EEG signals is an initiation to our quest for developing emotionally intelligent systems and devices, in order to enhance the performance quality of the same. Classification of emotions, both euphoric and negative, using stimulated EEG signals acquired from subjects whose different emotional states were elicited using audio-visual stimuli. The underlying strategy involved the extraction of Power spectral density(PSD) and empirical mode decomposition (EMD) features from the raw EEG data and their classification using linear discriminant analysis (LDA) and linear support vector machine (SVM) thereby classifying the emotions into their respective emotion classes: neutral, happy and sad, with an average classification accuracy of 76.46\%,where the neutral state has been classified most efficiently, with an average classification accuracy of 80.86\%. The classification accuracy increases with EMD features with reduction in time and computational complexity. LDA is found to perform better than LSVM with EMD features.",

keywords = "EEG, EMD, Emotion recognition, LDA, PSD, SVM",

author = "Anwesha Khasnobish and Saugat Bhattacharyya and Garima Singh and Arindam Jati and Amit Konar and Tibarewala, \{D. N.\} and R. Janarthanan",

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language = "English",

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booktitle = "Advances in Computing and Information Technology- Proceedings of the 2nd International Conference on Advances in Computing and Information Technology, ACITY 2012- Volume 3",

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Khasnobish, A, Bhattacharyya, S, Singh, G, Jati, A, Konar, A, Tibarewala, DN & Janarthanan, R 2013, The role of empirical mode decomposition on emotion classification using stimulated eeg signals. in N Meghanathan, N Chaki & D Nagamalai (eds), Advances in Computing and Information Technology- Proceedings of the 2nd International Conference on Advances in Computing and Information Technology, ACITY 2012- Volume 3. Advances in Intelligent Systems and Computing, vol. 178, Springer Verlag, pp. 55-62, 2nd International Conference on Advances in Computing and Information Technology, ACITY 2012, Chennai, India, 13/07/12. https://doi.org/10.1007/978-3-642-31600-5_6

The role of empirical mode decomposition on emotion classification using stimulated eeg signals. / Khasnobish, Anwesha; Bhattacharyya, Saugat; Singh, Garima et al.
Advances in Computing and Information Technology- Proceedings of the 2nd International Conference on Advances in Computing and Information Technology, ACITY 2012- Volume 3. ed. / Natarajan Meghanathan; Nabendu Chaki; Dhinaharan Nagamalai. Springer Verlag, 2013. p. 55-62 (Advances in Intelligent Systems and Computing; Vol. 178).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - The role of empirical mode decomposition on emotion classification using stimulated eeg signals

AU - Khasnobish, Anwesha

AU - Bhattacharyya, Saugat

AU - Singh, Garima

AU - Jati, Arindam

AU - Konar, Amit

AU - Tibarewala, D. N.

AU - Janarthanan, R.

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N2 - An efficient scheme of emotion recognition using EEG signals is an initiation to our quest for developing emotionally intelligent systems and devices, in order to enhance the performance quality of the same. Classification of emotions, both euphoric and negative, using stimulated EEG signals acquired from subjects whose different emotional states were elicited using audio-visual stimuli. The underlying strategy involved the extraction of Power spectral density(PSD) and empirical mode decomposition (EMD) features from the raw EEG data and their classification using linear discriminant analysis (LDA) and linear support vector machine (SVM) thereby classifying the emotions into their respective emotion classes: neutral, happy and sad, with an average classification accuracy of 76.46%,where the neutral state has been classified most efficiently, with an average classification accuracy of 80.86%. The classification accuracy increases with EMD features with reduction in time and computational complexity. LDA is found to perform better than LSVM with EMD features.

AB - An efficient scheme of emotion recognition using EEG signals is an initiation to our quest for developing emotionally intelligent systems and devices, in order to enhance the performance quality of the same. Classification of emotions, both euphoric and negative, using stimulated EEG signals acquired from subjects whose different emotional states were elicited using audio-visual stimuli. The underlying strategy involved the extraction of Power spectral density(PSD) and empirical mode decomposition (EMD) features from the raw EEG data and their classification using linear discriminant analysis (LDA) and linear support vector machine (SVM) thereby classifying the emotions into their respective emotion classes: neutral, happy and sad, with an average classification accuracy of 76.46%,where the neutral state has been classified most efficiently, with an average classification accuracy of 80.86%. The classification accuracy increases with EMD features with reduction in time and computational complexity. LDA is found to perform better than LSVM with EMD features.

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Khasnobish A, Bhattacharyya S, Singh G, Jati A, Konar A, Tibarewala DN et al. The role of empirical mode decomposition on emotion classification using stimulated eeg signals. In Meghanathan N, Chaki N, Nagamalai D, editors, Advances in Computing and Information Technology- Proceedings of the 2nd International Conference on Advances in Computing and Information Technology, ACITY 2012- Volume 3. Springer Verlag. 2013. p. 55-62. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-642-31600-5_6

The role of empirical mode decomposition on emotion classification using stimulated eeg signals

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