Emotion recognition of EEG signals based on contrastive learning graph convolutional model

• Published in: Journal of neural engineering Vol. 21; no. 4; pp. 46060 - 46072
• Main Authors: Zhang, Yiling, Liao, Yuan, Chen, Wei, Zhang, Xiruo, Huang, Liya
• Format: Journal Article
• Language: English
• Published: England IOP Publishing 01.08.2024
• Subjects: Brain - physiology; contrastive learning; differential entropy; Electroencephalography - methods; emotion recognition; Emotions - physiology; graph convolutional neural network; Humans; Machine Learning; Neural Networks, Computer; temporal and spatial feature; contrastive learning; differential entropy; temporal and spatial feature; graph convolutional neural network; emotion recognition
• ISSN: 1741-2560; 1741-2552; 1741-2552
• DOI: 10.1088/1741-2552/ad7060

Objective. Electroencephalogram (EEG) signals offer invaluable insights into the complexities of emotion generation within the brain. Yet, the variability in EEG signals across individuals presents a formidable obstacle for empirical implementations. Our research addresses these challenges innovatively, focusing on the commonalities within distinct subjects’ EEG data. Approach. We introduce a novel approach named Contrastive Learning Graph Convolutional Network (CLGCN). This method captures the distinctive features and crucial channel nodes related to individuals’ emotional states. Specifically, CLGCN merges the dual benefits of CL’s synchronous multisubject data learning and the GCN’s proficiency in deciphering brain connectivity matrices. Understanding multifaceted brain functions and their information interchange processes is realized as CLGCN generates a standardized brain network learning matrix during a dataset’s learning process. Main results. Our model underwent rigorous testing on the Database for Emotion Analysis using Physiological Signals (DEAP) and SEED datasets. In the five-fold cross-validation used for dependent subject experimental setting, it achieved an accuracy of 97.13% on the DEAP dataset and surpassed 99% on the SEED and SEED_IV datasets. In the incremental learning experiments with the SEED dataset, merely 5% of the data was sufficient to fine-tune the model, resulting in an accuracy of 92.8% for the new subject. These findings validate the model’s efficacy. Significance. This work combines CL with GCN, improving the accuracy of decoding emotional states from EEG signals and offering valuable insights into uncovering the underlying mechanisms of emotional processes in the brain.