A GPSO-optimized convolutional neural networks for EEG-based emotion recognition

Abstract

An urgent problem in the field of deep learning is the optimization of model construction, which frequently hinders its performance and often needs to be designed by experts. Optimizing the hyper-parameters remains a substantial obstacle in designing deep learning models, such as CNNs, in practice. In this paper, we propose an automatical optimization framework using binary coding system and GPSO with gradient penalties to select the structure. Such swarm intelligence optimization approaches have been used but not extensively exploited, and the existing work focuses on models with a fixed depth of networks. We design an experiment to arouse three types of emotion states for each subject, and simultaneously collect EEG signals corresponding to each emotion category. The GPSO-based method efficiently explores the solution space, allowing CNNs to obtain competitive classification performance over the dataset. Results indicate that our method based on the GPSO-optimized CNN model enables us to achieve a prominent classification accuracy, and the proposed method provides an effective automatic optimization framework for CNNs of the emotion recognition task with an uncertain depth of networks.