Journal
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
Volume 13, Issue 1, Pages 379-388Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TAFFC.2019.2937768
Keywords
Emotion recognition; graph convolutional neural networks; broad learning system; graph convolutional broad network
Funding
- Key Laboratory of Child Development and Learning Science (Southeast University)
- Brain-like Computing and Machine Intelligence (BCMI) Laboratory
- University of the West of Scotland, School of Engineering and Computing
- National Natural Science Foundation of China [61702195, 61751202, U1813203, 61572540, U1801262]
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In recent years, emotion recognition has become a research focus in the field of artificial intelligence. This study proposes a Graph Convolutional Broad Network (GCB-net) for exploring deeper-level information of graph-structured data, and achieves better accuracy and performance compared to other methods.
In recent years, emotion recognition has become a research focus in the area of artificial intelligence. Due to its irregular structure, EEG data can be analyzed by applying graphical based algorithms or models much more efficiently. In this work, a Graph Convolutional Broad Network (GCB-net) was designed for exploring the deeper-level information of graph-structured data. It used the graph convolutional layer to extract features of graph-structured input and stacks multiple regular convolutional layers to extract relatively abstract features. The final concatenation utilized the broad concept, which preserves the outputs of all hierarchical layers, allowing the model to search features in broad spaces. To improve the performance of the proposed GCB-net, the broad learning system (BLS) was applied to enhance its features. For comparison, two individual experiments were conducted to examine the efficiency of the proposed GCB-net based on the SJTU emotion EEG dataset (SEED) and DREAMER dataset respectively. In SEED, compared with other state-of-art methods, the GCB-net could better promote the accuracy (reaching 94.24 percent) on the DE feature of the all-frequency band. In DREAMER dataset, GCB-net performed better than other models with the same setting. Furthermore, the GCB-net reached high accuracies of 86.99, 89.32 and 89.20 percent on dimensions of Valence, Arousal and Dominance respectively. The experimental results showed the robust classifying ability of the GCB-net and BLS in EEG emotion recognition.
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