EEG-Based Emotion Recognition via Efficient Convolutional Neural Network and Contrastive Learning

Convolutional neural networks (CNNs) have achieved better performance than traditional algorithms in electroencephalogram (EEG)-based emotion recognition tasks in recent years. However, as the number of convolution layers increases, the number of network parameters increases sharply. Furthermore, emotional labels are not fully utilized by most supervised learning methods. To achieve a simple and effective model with supervised learning, we propose an efficient CNN and contrastive learning (ECNN-C) method for EEG-based emotion recognition. We utilize a novel convolutional block to replace the standard convolution to reduce the computational burden of the model. In addition, we adopt supervised contrastive learning to make full use of emotion labels, which allows us to pull EEG samples with the same emotional state together, push samples of the different emotional states apart from each other at the same time. Experimental results prove the effectiveness of the ECNN-C method for EEG-based emotion recognition.