GLFANet: A global to local feature aggregation network for EEG emotion recognition

Recently, emotion recognition technology based on electroencephalogram (EEG) signals is widely used in areas such as human–computer interaction and disease diagnosis. Traditional deep learning models rarely focus on the topological features of EEG electrodes, and often focus only on the local features of EEG signals, which makes it difficult to enhance the effectiveness of emotion recognition. In order to improve the accuracy and robustness of EEG-based emotion recognition algorithms, we propose an EEG emotion recognition algorithm based on a global to local feature aggregation network (GLFANet). This algorithm firstly uses the spatial location of the channels of EEG signals and the frequency domain features of each channel to construct an undirected topological graph to represent the spatial connection relationship between channels. Then, the GLFANet can learn deeper features of the undirected topology graph for emotion recognition. GLFANet mainly consists of a global learner composed of multiple graph convolution blocks and a local learner composed of multiple convolution blocks, which can learn both global and local features of EEG signals. The experiment results show that the proposed algorithm achieves higher accuracy on DEAP, SEED and DREAMER contrasted to other advanced algorithms.