EEG Based Classification of Human Emotions Using Discrete Wavelet Transform

Electroencephalography is widely used to study the dynamics of neural information processing in the brain and to diagnose brain disorder and cognitive processes. In this paper, we proposed EEG based emotion recognition system using Discrete Wavelet Transformation. A set of highly significant features based on wavelets coefficients has been extracted which also includes modified wavelet energy features. In order to minimize redundancy and maximize relevancy among features, mRMR algorithm is significantly applied for feature selection. Multi class Support Vector Machine is used to perform classification of four classes of human emotions. EEG recordings of "DEAP" database are used in this experiment. The proposed approach shows significant performance compared to existing algorithms.