EEG Channel Correlation Based Model for Emotion Recognition

Emotion recognition using Artificial Intelligence (AI) is a fundamental prerequisite to improve Human-Computer Interaction (HCI). Recognizing emotion from Electroencephalogram (EEG) has been globally accepted in many applications such as intelligent thinking, decision-making, social communication, feeling detection, affective computing, etc. Nevertheless, due to having too low amplitude variation related to time on EEG signal, the proper recognition of emotion from this signal has become too challenging. Usually, considerable effort is required to identify the proper feature or feature set for an effective feature-based emotion recognition system. To extenuate the manual human effort of feature extraction, we proposed a deep machine-learning-based model with Convolutional Neural Network (CNN). At first, the one-dimensional EEG data were converted to Pearson's Correlation Coefficient (PCC) featured images…