Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/98800
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dc.contributor.authorSer, Weeen
dc.contributor.authorHuang, Guang-Binen
dc.contributor.authorYohanes, Rendi E. J.en
dc.date.accessioned2013-07-31T06:37:10Zen
dc.date.accessioned2019-12-06T19:59:47Z-
dc.date.available2013-07-31T06:37:10Zen
dc.date.available2019-12-06T19:59:47Z-
dc.date.copyright2012en
dc.date.issued2012en
dc.identifier.urihttps://hdl.handle.net/10356/98800-
dc.identifier.urihttp://hdl.handle.net/10220/12625en
dc.description.abstractIn 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.en
dc.language.isoenen
dc.subjectDRNTU::Engineering::Electrical and electronic engineeringen
dc.titleDiscrete wavelet transform coefficients for emotion recognition from EEG signalsen
dc.typeConference Paperen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen
dc.contributor.conferenceAnnual International Conference of the IEEE Engineering in Medicine and Biology Society (34th : 2012 : San Diego, USA)en
dc.identifier.doihttp://dx.doi.org/10.1109/EMBC.2012.6346410en
item.grantfulltextnone-
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