Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/98800
Title: Discrete wavelet transform coefficients for emotion recognition from EEG signals
Authors: Ser, Wee
Huang, Guang-Bin
Yohanes, Rendi E. J.
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2012
Abstract: 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.
URI: https://hdl.handle.net/10356/98800
http://hdl.handle.net/10220/12625
DOI: http://dx.doi.org/10.1109/EMBC.2012.6346410
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Conference Papers

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