Emotion recognition based on wavelet analysis of Empirical Mode Decomposed EEG signals responsive to music videos

Human Computer Interface and implicit emotion tagging of multimedia contents require determination of various emotional states. This paper presents a new emotion recognition method based on wavelet analysis of Empirical Mode Decomposed (EMD) Electroencephalogram (EEG) signals responsive to music videos. Discrete Wavelet Transform (DWT) is performed on the selected Intrinsic Mode Functions (IMFs) obtained from EMD operation. Then Higher Order Statistics (HOS), namely, variance, kurtosis, skewness of suitably chosen DWT coefficients are exploited to form feature vector. Furthermore, Principal Component Analysis (PCA) is applied on the feature vector to reduce the feature dimension. The reduced feature set thus obtained is then fed to Support Vector Machine (SVM) to perform two class classification of emotions. Extensive simulations are carried out to test the efficacy of the proposed method using DEAP, an affective computing database. It is found that the proposed method outperforms some state of the art methods of similar emotion recognition using the same database.