EEG emotion recognition using improved graph neural network with channel selection. (April 2023)

• Record Type: Journal Article
• Authors: Lin, Xuefen; Chen, Jielin; Ma, Weifeng; Tang, Wei; Wang, Yuchen
• Is Part Of: Computer methods and programs in biomedicine. Volume 231(2023)
• Publication Date: 2023-04
• Subjects: EEG classification -- Graph neural network -- Convolutional neural network -- Attention mechanism; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2023.107380
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: The average accuracy of the proposed model is above 90% on several datasets. The proposed model captures both intra- and inter-channel features of EEG. Graphs combining functional connectivity have stronger representation capabilities. The channel retention ratio of the proposed method is dynamically adjustable. The accuracy of the model exceeded 80% with 20% of the EEG channels retained. Abstract: Background and objective: Emotion classification tasks based on electroencephalography (EEG) are an essential part of artificial intelligence, with promising applications in healthcare areas such as autism research and emotion detection in pregnant women. However, the complex data acquisition environment provides a variable number of EEG channels, which interferes with the model to simulate the process of information transfer in the human brain. Therefore, this paper proposes an improved graph convolution model with dynamic channel selection. Methods: The proposed model combines the advantages of 1D convolution and graph convolution to capture the intra- and inter-channel EEG features, respectively. We add functional connectivity in the graph structure that helps to simulate the relationship between brain regions further. In addition, an adjustable scale of channel selection can be performed based on the attention distribution in the graph structure. Results: We conducted various experiments on the DEAP-Twente, DEAP-Geneva, and SEED datasets and achieved average accuracies of 90.74%, 91%, and 90.22%, respectively, which exceeded most existing models. Meanwhile, with only 20% of the EEG channels retained, the models achieved average accuracies of 82.78%, 84%, and 83.93% on the above three datasets, respectively. Conclusions: The experimental results show that the proposed model can achieve effective emotion classification in complex dataset environments. Also, the proposed channel selection method is informative for reducing the cost of affective computing.