Towards Integrating Automatic Emotion Recognition in Education: A Deep Learning Model Based on 5 EEG Channels

Abstract

In a technologically advanced world, artificial intelligence has impacted all fields of activity. The augmentation of online learning by means of emotion recognition systems raises new challenges in terms of obtaining high-performance systems and in interpreting the results. The paper aims to investigate the usage of automated emotion recognition in learning and to develop a deep learning model based on physiological data to recognize emotions often encountered in classrooms. So, an 1D-CNN model based on physiological data is used to recognize seven emotions: boredom, confusion, frustration, curiosity, excitement, concentration, and anxiety. These emotions are described according to the PAD model and the 5 EEG signals, FP1, AF3, F7, T7, FP2, are taken from the DEAP dataset to train and to test the convolutional neural network model. The high accuracy we obtained (i.e. boredom—99.64%, confusion—99.70%, frustration—99.66%, curiosity—99.80%, excitement—99.91%, concentration—99.70%, anxiety—99.21%) proves that the use of signals obtained via only five channels is sufficient to recognize the presence of emotions. Furthermore, an improved method of analysis based on LIME is proposed and used to obtain reliable explanations for the predictions of our model.