EESCN: A novel spiking neural network method for EEG-based emotion recognition

Background and Objective: Although existing artificial neural networks have achieved good results in electroencephalograph (EEG) emotion recognition, further improvements are needed in terms of bio-interpretability and robustness. In this research, we aim to develop a highly efficient and high-performance method for emotion recognition based on EEG.Methods: We propose an Emo-EEGSpikeConvNet (EESCN), a novel emotion recognition method based on spiking neural network (SNN). It consists of a neuromorphic data generation module and a NeuroSpiking framework. The neuromorphic data generation module converts EEG data into 2D frame format as input to the NeuroSpiking framework, while the NeuroSpiking framework is used to extract spatio-temporal features of EEG for classification.Results: EESCN achieves high emotion recognition accuracies on DEAP and SEED-IV datasets, ranging from 94.56% to 94.81% on DEAP and a mean accuracy of 79.65% on SEED-IV. Compared to existing SNN methods, EESCN significantly improves EEG emotion recognition performance. In addition, it also has the advantages of faster running speed and less memory footprint.Conclusions: EESCN has shown excellent performance and efficiency in EEG-based emotion recognition with potential for practical applications requiring portability and resource constraints.