  In this research, an emotion recognition system is developed based on
valence/arousal model using electroencephalography (EEG) signals. EEG signals
are decomposed into the gamma, beta, alpha and theta frequency bands using
discrete wavelet transform (DWT), and spectral features are extracted from each
frequency band. Principle component analysis (PCA) is applied to the extracted
features by preserving the same dimensionality, as a transform, to make the
features mutually uncorrelated. Support vector machine (SVM), K-nearest
neighbor (KNN) and artificial neural network (ANN) are used to classify
emotional states. The cross-validated SVM with radial basis function (RBF)
kernel using extracted features of 10 EEG channels, performs with 91.3%
accuracy for arousal and 91.1% accuracy for valence, both in the beta frequency
band. Our approach shows better performance compared to existing algorithms
applied to the "DEAP" dataset.
