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Artificial Neural Networks Based Emotion Classification System through Relative Wavelet Energy of EEG Signal

Authors:

Prima Dewi Purnamasari,

Anak Agung Putri Ratna,

Benyamin KusumoputroAuthors Info & Claims

ICNCC '16: Proceedings of the Fifth International Conference on Network, Communication and Computing

Pages 135 - 139

https://doi.org/10.1145/3033288.3033298

Published: 17 December 2016 Publication History

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Abstract

Emotion classification from EEG brain signal has been a widely research topic recently, because of the complexity of processing the multi channels of the brain signal and also the problem on mapping the human emotion itself. This paper discusses the technique to determine the emotion classification from EEG brain signal using a relative wavelet energy (RWE) as a feature vector and an artificial neural networks (ANN) as a classifier. In this research, two types of ANN classifier was utilized and analyzed, namely, Back-propagation Neural Networks (BPNN) and Probabilistic Neural Networks (PNN). Also reducing the number of the EEG channel to be processed is investigated, in order to decrease the computational cost of the classification system. Results showed that the recognition rate of the reduced utilized channels up to 4 channel are incomparable with that of the full utilization of 32 channels. However, 8 and 14 channels still produced sufficient recognition rate. It is also confirmed from experiments that the BPNN shown as a more reliable classifier compare with the PNN method.

References

[1]

S. Koelstra, J. Lee, A. Yazdani, T. Ebrahimi, T. Pun, A. Nijholt, and I. Patras, "DEAP: A Database for Emotion Analysis using Physiological Signals," IEEE Trans. Affect. Comput., pp. 1--15, 2012.

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[2]

P. D. Purnamasari, A. A. P. Ratna, and B. Kusumoputro, "EEG Based Patient Emotion Monitoring using Relative Wavelet Energy Feature and Back Propagation Neural Network," in Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE, 2015, pp. 2820--2823.

Google Scholar

[3]

M. Unser and A. Aldroubi, "A review of wavelets in biomedical applications," Proc. IEEE, vol. 84, no. 4, pp. 626--638, 1996.

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Google Scholar

[4]

S. G. Mallat, "Theory for multiresolution signal decomposition: the wavelet representation," IEEE Trans. Pattern Anal. Mach. Intell., vol. 11, no. 7, pp. 674--693, 1989.

Digital Library

Google Scholar

[5]

P. C. Petrantonakis and L. J. Hadjileontiadis, "A novel emotion elicitation index using frontal brain asymmetry for enhanced EEG-based emotion recognition," IEEE Trans. Inf. Technol. Biomed., vol. 15, no. 5, pp. 737--746, 2011.

Digital Library

Google Scholar

[6]

L. Guo, D. Rivero, J. a. Seoane, and A. Pazos, "Classification of EEG signals using relative wavelet energy and artificial neural networks," in Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation - GEC '09, 2009, p. 177.

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Shaila SAnirudh BNair AMonish LMurala PSanjana A(2023)EEG Signal-Based Human Emotion Recognition Using Power Spectrum Density and Discrete Wavelet TransformAdvances in Computing and Information10.1007/978-981-99-7622-5_39(557-567)Online publication date: 16-Dec-2023
https://doi.org/10.1007/978-981-99-7622-5_39
Purnamasari PRatna AKusumoputro B(2017)Development of Filtered Bispectrum for EEG Signal Feature Extraction in Automatic Emotion Recognition Using Artificial Neural NetworksAlgorithms10.3390/a1002006310:2(63)Online publication date: 30-May-2017
https://doi.org/10.3390/a10020063
Dewi Purnamasari PRatna AKusumoputro B(2017)Relative wavelet bispectrum feature for alcoholic EEG signal classification using artificial neural network2017 15th International Conference on Quality in Research (QiR) : International Symposium on Electrical and Computer Engineering10.1109/QIR.2017.8168473(154-158)Online publication date: Jul-2017
https://doi.org/10.1109/QIR.2017.8168473

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ICNCC '16: Proceedings of the Fifth International Conference on Network, Communication and Computing

December 2016

343 pages

ISBN:9781450347938

DOI:10.1145/3033288

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 17 December 2016

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ICNCC '16: Fifth International Conference on Network, Communication and Computing

December 17 - 21, 2016

Kyoto, Japan

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Shaila SAnirudh BNair AMonish LMurala PSanjana A(2023)EEG Signal-Based Human Emotion Recognition Using Power Spectrum Density and Discrete Wavelet TransformAdvances in Computing and Information10.1007/978-981-99-7622-5_39(557-567)Online publication date: 16-Dec-2023
https://doi.org/10.1007/978-981-99-7622-5_39
Purnamasari PRatna AKusumoputro B(2017)Development of Filtered Bispectrum for EEG Signal Feature Extraction in Automatic Emotion Recognition Using Artificial Neural NetworksAlgorithms10.3390/a1002006310:2(63)Online publication date: 30-May-2017
https://doi.org/10.3390/a10020063
Dewi Purnamasari PRatna AKusumoputro B(2017)Relative wavelet bispectrum feature for alcoholic EEG signal classification using artificial neural network2017 15th International Conference on Quality in Research (QiR) : International Symposium on Electrical and Computer Engineering10.1109/QIR.2017.8168473(154-158)Online publication date: Jul-2017
https://doi.org/10.1109/QIR.2017.8168473

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Classification of EEG signals using relative wavelet energy and artificial neural networks

Automatic recognition of vigilance state by using a wavelet-based artificial neural network

The application of neural networks in classification of epilepsy using EEG signals

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