Classification of Human Emotions from Hectroencephalogram using Support Vector Machine

In this paper we propose the support vector machine classifier for the purpose of classifying Human Emotions using Electroencephalogram (EEG). EEG signal consists of different brain waves reflecting brain activity according to the electrode placement and the functioning of the brain. Audio Visual stimuli are given to the human volunteers and emotions in the form of EEG signals are invoked, using the hardware and software set-up. Emotions invoked here are the basic emotions named as Angry, Happy, Neutral and Sad. The signal is then pre-processed, its wavelet decomposition is done using Sub-band decomposition algorithm, and statistical parameters of wavelet coefficients are calculated. Support Vector Machine is used to classify the features of query samples into their class of emotion after training. Output of the Support Vector Machine is the class of Emotion of a Query Sample.