A 3D Convolutional Neural Network for Emotion Recognition based on EEG Signals

As an important field of research in Human-Machine Interactions, emotion recognition based on the electroencephalography (EEG) signals has become common research. The traditional machine learning approaches use well-designed classifiers with hand-crafted features which may be limited to domain knowledge. Motivated by the outstanding performance of deep learning approaches in recognition tasks, we proposed a 3D convolutional neural network model to extract the spatial-temporal features automatically in the EEG signals. By the pre-processing method with baseline signals and the electrode topological structure relocated, the proposed model achieves a high accuracy rate of 96.61%, 96.43% in the Two class classification task (low/high arousal, low/high valence) and 93.53% in the Four class classification task (low arousal and low valence/high arousal and low valence/low arousal and high valence/high arousal and high valence) in the DEAP dataset, and 97.52%, 96.96% in the Two class classification task and 95.86% in the Four class classification task in the AMIGOS dataset.