Abstract
Emotions play a crucial role in human interaction and healthcare. This study introduces an automatic emotion recognition system based on deep learning using electroencephalogram signals. A lightweight pyramidal one-dimensional convolutional neural network model is proposed that involves a small number of learnable parameters. Using the model, a two-level ensemble classifier is designed. Each channel is scanned incrementally in the first level to generate predictions, which are fused using the majority vote. The second level fuses the predictions of all the channels of a signal using a majority vote to predict the emotional state. The method was validated using the public domain challenging benchmark DEAP dataset. The electroencephalogram signals over five brain regions were analyzed. The results indicate that the frontal brain region plays a dominant role, achieving accuracies of 98.43% and 97.65% for two emotion recognition problems (distinguishing high valence vs. low valence and high arousal vs. low arousal states).