WSEAS Transactions on Signal Processing
Print ISSN: 1790-5052, E-ISSN: 2224-3488
Volume 10, 2014
Combining Spectral and Fractal Features for Emotion Recognition on Electroencephalographic Signals
Authors: ,
Abstract: Recent studies have attempted to recognize emotions by extracting spectral and fractal features from electroencephalographic signals; however, up to now none of them have combined these two features to recognize emotions. This paper aims at providing a comparison between an accuracy rate of an approach that recognizes emotions by extracting both spectral and fractal features with that of those that extract only one of these features. To this end, we designed and implemented a procedure that recognizes positive and negative emotions by extracting spectral, fractal, or both features. Next, using this procedure, we built three different approaches to recognize positive and negative emotions; the first one extracted both spectral and fractal features, whereas the other two extracted each type of feature separately. Then, the accuracy rate of the approaches was calculated and compared among them. The comparison showed that the spectral-fractal approach recognizes emotions more accurately than the spectral and fractal approaches in 96% and 79% of the time, respectively. This suggests that it is possible to develop a more effective emotion recognition method by extracting both spectral and fractal features than extracting only one type of them.
Search Articles
Keywords: Affective Computing, Discrete Wavelet Transform, Electroencephalogram, Emotion recognition, Multifractal Analysis, Support Vector Machine, Pattern recognition
Pages: 481-496
WSEAS Transactions on Signal Processing, ISSN / E-ISSN: 1790-5052 / 2224-3488, Volume 10, 2014, Art. #50