Emotion recognition system based on EEG signals using relative wavelet energy features and a modified radial basis function neural networks

Bharasaka Krisnandhika; Akhmad Faqih; Prima Dewi Pumamasari; Benyamin Kusumoputro

doi:10.1109/ICCED.2017.8019990

Emotion recognition system based on EEG signals using relative wavelet energy features and a modified radial basis function neural networks

Bharasaka Krisnandhika, Akhmad Faqih, Prima Dewi Pumamasari, Benyamin Kusumoputro

Department of Electrical Engineering

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

21 Citations (Scopus)

Abstract

Emotion recognition is very important, especially on its application for patient monitoring and in the treatment management system of that patient. In this paper, an EEG-based emotion recognition system is developed that consists of a feature extraction subsystem and a classifier subsystem. In this research, we have studied the utilization of a relative wavelet energy as the feature extraction, and a modified radial basis function neural networks is then implemented as the classifier. Experimental result shows that the relative wavelet energy and the modified radial basis function neural networks achieved an average recognition rate of 76% when using a 50% of the data in the training stage.

Original language	English
Title of host publication	2017 International Conference on Consumer Electronics and Devices, ICCED 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	50-54
Number of pages	5
ISBN (Electronic)	9781538604038
DOIs	https://doi.org/10.1109/ICCED.2017.8019990
Publication status	Published - 29 Aug 2017
Event	1st International Conference on Consumer Electronics and Devices, ICCED 2017 - London, United Kingdom Duration: 14 Jul 2017 → 17 Jul 2017

Publication series

Name	2017 International Conference on Consumer Electronics and Devices, ICCED 2017

Conference

Conference	1st International Conference on Consumer Electronics and Devices, ICCED 2017
Country/Territory	United Kingdom
City	London
Period	14/07/17 → 17/07/17

Keywords

emotion classification
emotion recognition
modified radial basis function neural networks
relative wavelet energy

Access to Document

10.1109/ICCED.2017.8019990

Cite this

Krisnandhika, B., Faqih, A., Dewi Pumamasari, P., & Kusumoputro, B. (2017). Emotion recognition system based on EEG signals using relative wavelet energy features and a modified radial basis function neural networks. In 2017 International Conference on Consumer Electronics and Devices, ICCED 2017 (pp. 50-54). Article 8019990 (2017 International Conference on Consumer Electronics and Devices, ICCED 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCED.2017.8019990

Krisnandhika, Bharasaka ; Faqih, Akhmad ; Dewi Pumamasari, Prima et al. / Emotion recognition system based on EEG signals using relative wavelet energy features and a modified radial basis function neural networks. 2017 International Conference on Consumer Electronics and Devices, ICCED 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 50-54 (2017 International Conference on Consumer Electronics and Devices, ICCED 2017).

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title = "Emotion recognition system based on EEG signals using relative wavelet energy features and a modified radial basis function neural networks",

abstract = "Emotion recognition is very important, especially on its application for patient monitoring and in the treatment management system of that patient. In this paper, an EEG-based emotion recognition system is developed that consists of a feature extraction subsystem and a classifier subsystem. In this research, we have studied the utilization of a relative wavelet energy as the feature extraction, and a modified radial basis function neural networks is then implemented as the classifier. Experimental result shows that the relative wavelet energy and the modified radial basis function neural networks achieved an average recognition rate of 76% when using a 50% of the data in the training stage.",

keywords = "emotion classification, emotion recognition, modified radial basis function neural networks, relative wavelet energy",

author = "Bharasaka Krisnandhika and Akhmad Faqih and {Dewi Pumamasari}, Prima and Benyamin Kusumoputro",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 1st International Conference on Consumer Electronics and Devices, ICCED 2017 ; Conference date: 14-07-2017 Through 17-07-2017",

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Krisnandhika, B, Faqih, A, Dewi Pumamasari, P & Kusumoputro, B 2017, Emotion recognition system based on EEG signals using relative wavelet energy features and a modified radial basis function neural networks. in 2017 International Conference on Consumer Electronics and Devices, ICCED 2017., 8019990, 2017 International Conference on Consumer Electronics and Devices, ICCED 2017, Institute of Electrical and Electronics Engineers Inc., pp. 50-54, 1st International Conference on Consumer Electronics and Devices, ICCED 2017, London, United Kingdom, 14/07/17. https://doi.org/10.1109/ICCED.2017.8019990

Emotion recognition system based on EEG signals using relative wavelet energy features and a modified radial basis function neural networks. / Krisnandhika, Bharasaka; Faqih, Akhmad; Dewi Pumamasari, Prima et al.
2017 International Conference on Consumer Electronics and Devices, ICCED 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 50-54 8019990 (2017 International Conference on Consumer Electronics and Devices, ICCED 2017).

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

TY - GEN

T1 - Emotion recognition system based on EEG signals using relative wavelet energy features and a modified radial basis function neural networks

AU - Krisnandhika, Bharasaka

AU - Faqih, Akhmad

AU - Dewi Pumamasari, Prima

AU - Kusumoputro, Benyamin

PY - 2017/8/29

Y1 - 2017/8/29

N2 - Emotion recognition is very important, especially on its application for patient monitoring and in the treatment management system of that patient. In this paper, an EEG-based emotion recognition system is developed that consists of a feature extraction subsystem and a classifier subsystem. In this research, we have studied the utilization of a relative wavelet energy as the feature extraction, and a modified radial basis function neural networks is then implemented as the classifier. Experimental result shows that the relative wavelet energy and the modified radial basis function neural networks achieved an average recognition rate of 76% when using a 50% of the data in the training stage.

AB - Emotion recognition is very important, especially on its application for patient monitoring and in the treatment management system of that patient. In this paper, an EEG-based emotion recognition system is developed that consists of a feature extraction subsystem and a classifier subsystem. In this research, we have studied the utilization of a relative wavelet energy as the feature extraction, and a modified radial basis function neural networks is then implemented as the classifier. Experimental result shows that the relative wavelet energy and the modified radial basis function neural networks achieved an average recognition rate of 76% when using a 50% of the data in the training stage.

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KW - modified radial basis function neural networks

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Krisnandhika B, Faqih A, Dewi Pumamasari P , Kusumoputro B. Emotion recognition system based on EEG signals using relative wavelet energy features and a modified radial basis function neural networks. In 2017 International Conference on Consumer Electronics and Devices, ICCED 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 50-54. 8019990. (2017 International Conference on Consumer Electronics and Devices, ICCED 2017). doi: 10.1109/ICCED.2017.8019990

Emotion recognition system based on EEG signals using relative wavelet energy features and a modified radial basis function neural networks

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Keywords

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