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

• Published in: Computer methods and programs in biomedicine Vol. 243; p. 107927
• Main Authors: Xu, FeiFan, Pan, Deng, Zheng, Haohao, Ouyang, Yu, Jia, Zhe, Zeng, Hong
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.01.2024
• Subjects: Convolutional neural network (CNN); EEG emotion recognition; Neuromorphic; Recurrent neural network (RNN); Spiking neural network (SNN); EEG emotion recognition; Convolutional neural network (CNN); Neuromorphic; Spiking neural network (SNN); Recurrent neural network (RNN)
• DOI: 10.1016/j.cmpb.2023.107927
• ISSN: 0169-2607

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. 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. 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. EESCN has shown excellent performance and efficiency in EEG-based emotion recognition with potential for practical applications requiring portability and resource constraints. •An efficient EEG neuromorphic data generation module for Spiking Neural Network.•A NeuroSpiking framework for feature extraction and emotion classification.•The advantages of faster running speed and less memory footprint.