Virtual Image from EEG to Recognize Appropriate Emotion using Convolutional Neural Network

Abstract

Electroencephalogram (EEG) that measures the electrical activity of the brain has been used extensively to recognize emotion. Normally feature based emotion recognition requires a strong effort to design the perfect feature or feature set related to the classification of emotion. To curtail the manual human effort we designed a model by using a virtual image from EEG with Convolutional Neural Network (CNN). Initially, we calculated Pearson’s correlation coefficients form different sub-bands of EEG to formulate a virtual image. Later, this virtual image was fed into a CNN architecture to classify emotion. We made two distinct protocols; between these, protocol-1 was to classify positive and negative emotion and protocol-2 was to classify four distinct emotions. An overall maximum accuracy of 81.51% on valence and 79.42% on arousal was obtained by using internationally authorized DEAP dataset. Our proposed method is helpful in recognizing emotions efficiently.