Knowledge-guided Network Pruning for EEG-based Emotion Recognition

With the development of deep learning in EEG-related tasks, the complexity of learning models has gradually increased. These complex models often result in long inference times, high energy consumption, and an increased risk of overfitting. Therefore, model compression has become an important consideration. Although some EEG models have used lightweight techniques, such as separable convolution, no existing work has directly attempted to compress EEG models to reduce their complexity. In this paper, we integrate neuroscience knowledge into EEG model pruning recovery, and innovatively propose two loss functions in the learning process, the knowledge-guided region-wise loss that enforces the classification evidence consistent with the importance of the prefrontal lobe, and the knowledge-guided sample-wise loss that constrains the learning process by distinguishing the importance of different samples.