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 planned to calculate 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 using internationally authorized DEAP dataset. Our proposed method is helpful in
recognizing emotions efficiently.