Classification of emotion from EEG using hybrid radial basis function networks with elitist PSO

Emotion play an important role at several activities in the present world. Human decision making, cognitive process and interaction between human & machine all the activities depends on human emotions. Facial expression, musical activities and several approaches used to find the human emotions. In this paper EEG is used to find the accurate emotion. Emotion classification is the huge task. Classification of the human emotion is a process that merges the feature selection and provides the class labels for the data. The proposed work has four stages which include preprocessing, feature extraction, feature selection and classification. This paper uses a Radial Basis Function Network with trained by Evolution algorithm and particle Swarm Optimization is used to select the particular features in the feature selection process. The result of the network will classify the human emotions into arousal and valence emotion. Based on the classification, different emotion level accuracy has to be validated.