Deep Learning Based Emotion Classification through EEG Spectrogram Images

Emotion modeling for social robotics has the great potential to improve the life quality for the elderly and individuals with disabilities by making communication, care, and interactions more effective. It can help individuals with communication difficulties express their emotions. It can also be used to monitor the emotional well-being of elderly persons living alone and alert caregivers or family members if there are signs of distress. More broadly, emotion modeling is necessary to design robots closer and closer to human beings that can naturally interact with them by understanding their behavior and reactions. Here, we propose a deep learning technique for emotion classification using electroencephalogram (EEG) signals. We aim to recognize valence, arousal, dominance, and likability. Our technique uses the spectrogram from each of the 32 electrodes applied in the skull area. Then, we employ a Resnet101 convolutional neural network to learn a model capable of predicting several emotions. We built and tested our model on the DEAP dataset.