EEG based patient emotion monitoring using relative wavelet energy feature and Back Propagation Neural Network

Prima Dewi Purnamasari; Anak Agung Putri Ratna; Benyamin Kusumo Putro

doi:10.1109/EMBC.2015.7318978

EEG based patient emotion monitoring using relative wavelet energy feature and Back Propagation Neural Network

Prima Dewi Purnamasari, Anak Agung Putri Ratna, Benyamin Kusumo Putro

Department of Electrical Engineering

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

14 Citations (Scopus)

Abstract

In EEG-based emotion recognition, feature extraction is as important as the classification algorithm. A good choice of features results in higher recognition rate. However, there is no standard method for feature extraction in EEG-based emotion recognition, especially for real time monitoring, where speed of computation is crucial. In this work, we assess the use of relative wavelet energy as features and Back Propagation Neural Network (BPNN) as classifier for emotion recognition. This method was implemented in simulated real time emotion recognition by using a publicly accessible database. The results showed that relative wavelet energy and BPNN achieved an average recognition rate of 92.03%. The highest average recognition rate was achieved when the time window was 30s.

Original language	English
Title of host publication	2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2820-2823
Number of pages	4
ISBN (Electronic)	9781424492718
DOIs	https://doi.org/10.1109/EMBC.2015.7318978
Publication status	Published - 4 Nov 2015
Event	37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015 - Milan, Italy Duration: 25 Aug 2015 → 29 Aug 2015

Publication series

Name	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Volume	2015-November
ISSN (Print)	1557-170X

Conference

Conference	37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
Country	Italy
City	Milan
Period	25/08/15 → 29/08/15

Keywords

brain wave
EEG
emotion monitoring
emotion recognition
wavelet energy

Access to Document

10.1109/EMBC.2015.7318978

Link to publication in Scopus

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Purnamasari, P. D., Ratna, A. A. P., & Putro, B. K. (2015). EEG based patient emotion monitoring using relative wavelet energy feature and Back Propagation Neural Network. In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015 (pp. 2820-2823). [7318978] (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Vol. 2015-November). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2015.7318978

Purnamasari, Prima Dewi ; Ratna, Anak Agung Putri ; Putro, Benyamin Kusumo. / EEG based patient emotion monitoring using relative wavelet energy feature and Back Propagation Neural Network. 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 2820-2823 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

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abstract = "In EEG-based emotion recognition, feature extraction is as important as the classification algorithm. A good choice of features results in higher recognition rate. However, there is no standard method for feature extraction in EEG-based emotion recognition, especially for real time monitoring, where speed of computation is crucial. In this work, we assess the use of relative wavelet energy as features and Back Propagation Neural Network (BPNN) as classifier for emotion recognition. This method was implemented in simulated real time emotion recognition by using a publicly accessible database. The results showed that relative wavelet energy and BPNN achieved an average recognition rate of 92.03%. The highest average recognition rate was achieved when the time window was 30s.",

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Purnamasari, PD , Ratna, AAP & Putro, BK 2015, EEG based patient emotion monitoring using relative wavelet energy feature and Back Propagation Neural Network. in 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015., 7318978, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, vol. 2015-November, Institute of Electrical and Electronics Engineers Inc., pp. 2820-2823, 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015, Milan, Italy, 25/08/15. https://doi.org/10.1109/EMBC.2015.7318978

EEG based patient emotion monitoring using relative wavelet energy feature and Back Propagation Neural Network. / Purnamasari, Prima Dewi ; Ratna, Anak Agung Putri ; Putro, Benyamin Kusumo.

2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015. Institute of Electrical and Electronics Engineers Inc., 2015. p. 2820-2823 7318978 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Vol. 2015-November).

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

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N2 - In EEG-based emotion recognition, feature extraction is as important as the classification algorithm. A good choice of features results in higher recognition rate. However, there is no standard method for feature extraction in EEG-based emotion recognition, especially for real time monitoring, where speed of computation is crucial. In this work, we assess the use of relative wavelet energy as features and Back Propagation Neural Network (BPNN) as classifier for emotion recognition. This method was implemented in simulated real time emotion recognition by using a publicly accessible database. The results showed that relative wavelet energy and BPNN achieved an average recognition rate of 92.03%. The highest average recognition rate was achieved when the time window was 30s.

AB - In EEG-based emotion recognition, feature extraction is as important as the classification algorithm. A good choice of features results in higher recognition rate. However, there is no standard method for feature extraction in EEG-based emotion recognition, especially for real time monitoring, where speed of computation is crucial. In this work, we assess the use of relative wavelet energy as features and Back Propagation Neural Network (BPNN) as classifier for emotion recognition. This method was implemented in simulated real time emotion recognition by using a publicly accessible database. The results showed that relative wavelet energy and BPNN achieved an average recognition rate of 92.03%. The highest average recognition rate was achieved when the time window was 30s.

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Purnamasari PD , Ratna AAP , Putro BK. EEG based patient emotion monitoring using relative wavelet energy feature and Back Propagation Neural Network. In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015. Institute of Electrical and Electronics Engineers Inc. 2015. p. 2820-2823. 7318978. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). https://doi.org/10.1109/EMBC.2015.7318978