In this research, emotional states in arousal/valence dimensions have been classified using minimum number of channels and frequency bands of EEG signal. Using the discrete wavelet transforms, EEG signals have been decomposed to corresponding frequency bands and then several features have been extracted. The support vector machine and K-nearest neighbor classifiers have been used to detect the emotional states from the extracted features. For the recorded 10-channel EEG signal, results illustrate the classification accuracy of 86.75 % for arousal level and 84.05 % for valence level. Moreover, using the high-frequency bands, specifically gamma band, yields higher accuracy compared to using low-frequency bands of EEG signal. All of these support to the development of a real-time emotion classification system.
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