Discrete wavelet transform coefficients for emotion recognition from EEG signals

In this paper, we propose to use DWT coefficients as features for emotion recognition from EEG signals. Previous feature extraction methods used power spectra density values dervied from Fourier Transform or sub-band energy and entropy derived from Wavelet Transform. These feature extracion methods...

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Main Authors: Ser, Wee, Huang, Guang-Bin, Yohanes, Rendi E. J.
Other Authors: School of Electrical and Electronic Engineering
Format: Conference or Workshop Item
Language:English
Published: 2013
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Online Access:https://hdl.handle.net/10356/98800
http://hdl.handle.net/10220/12625
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-988002020-03-07T13:24:48Z Discrete wavelet transform coefficients for emotion recognition from EEG signals Ser, Wee Huang, Guang-Bin Yohanes, Rendi E. J. School of Electrical and Electronic Engineering Annual International Conference of the IEEE Engineering in Medicine and Biology Society (34th : 2012 : San Diego, USA) DRNTU::Engineering::Electrical and electronic engineering In this paper, we propose to use DWT coefficients as features for emotion recognition from EEG signals. Previous feature extraction methods used power spectra density values dervied from Fourier Transform or sub-band energy and entropy derived from Wavelet Transform. These feature extracion methods eliminate temporal information which are essential for analyzing EEG signals. The DWT coefficients represent the degree of correlation between the analyzed signal and the wavelet function at different instances of time; therefore, DWT coefficients contain temporal information of the analyzed signal. The proposed feature extraction method fully utilizes the simultaneous time-frequency analysis of DWT by preserving the temporal information in the DWT coefficients. In this paper, we also study the effects of using different wavelet functions (Coiflets, Daubechies and Symlets) on the performance of the emotion recognition system. The input EEG signals were obtained from two electrodes according to 10-20 system: Fp1 and Fp2. Visual stimuli from International Affective Picture System (IAPS) were used to induce two emotions: happy and sad. Two classifiers were used: Extreme Learning Machine (ELM) and Support Vector Machine (SVM). Experimental results confirmed that the proposed DWT coefficients method showed improvement of performance compared to previous methods. 2013-07-31T06:37:10Z 2019-12-06T19:59:47Z 2013-07-31T06:37:10Z 2019-12-06T19:59:47Z 2012 2012 Conference Paper https://hdl.handle.net/10356/98800 http://hdl.handle.net/10220/12625 10.1109/EMBC.2012.6346410 en
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic DRNTU::Engineering::Electrical and electronic engineering
spellingShingle DRNTU::Engineering::Electrical and electronic engineering
Ser, Wee
Huang, Guang-Bin
Yohanes, Rendi E. J.
Discrete wavelet transform coefficients for emotion recognition from EEG signals
description In this paper, we propose to use DWT coefficients as features for emotion recognition from EEG signals. Previous feature extraction methods used power spectra density values dervied from Fourier Transform or sub-band energy and entropy derived from Wavelet Transform. These feature extracion methods eliminate temporal information which are essential for analyzing EEG signals. The DWT coefficients represent the degree of correlation between the analyzed signal and the wavelet function at different instances of time; therefore, DWT coefficients contain temporal information of the analyzed signal. The proposed feature extraction method fully utilizes the simultaneous time-frequency analysis of DWT by preserving the temporal information in the DWT coefficients. In this paper, we also study the effects of using different wavelet functions (Coiflets, Daubechies and Symlets) on the performance of the emotion recognition system. The input EEG signals were obtained from two electrodes according to 10-20 system: Fp1 and Fp2. Visual stimuli from International Affective Picture System (IAPS) were used to induce two emotions: happy and sad. Two classifiers were used: Extreme Learning Machine (ELM) and Support Vector Machine (SVM). Experimental results confirmed that the proposed DWT coefficients method showed improvement of performance compared to previous methods.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Ser, Wee
Huang, Guang-Bin
Yohanes, Rendi E. J.
format Conference or Workshop Item
author Ser, Wee
Huang, Guang-Bin
Yohanes, Rendi E. J.
author_sort Ser, Wee
title Discrete wavelet transform coefficients for emotion recognition from EEG signals
title_short Discrete wavelet transform coefficients for emotion recognition from EEG signals
title_full Discrete wavelet transform coefficients for emotion recognition from EEG signals
title_fullStr Discrete wavelet transform coefficients for emotion recognition from EEG signals
title_full_unstemmed Discrete wavelet transform coefficients for emotion recognition from EEG signals
title_sort discrete wavelet transform coefficients for emotion recognition from eeg signals
publishDate 2013
url https://hdl.handle.net/10356/98800
http://hdl.handle.net/10220/12625
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