FAformer: parallel Fourier-attention architectures benefits EEG-based affective computing with enhanced spatial information

• Published in: Neural computing & applications Vol. 36; no. 8; pp. 3903 - 3919
• Main Authors: Gao, Ziheng, Huang, Jiajin, Chen, Jianhui, Zhou, Haiyan
• Format: Journal Article
• Language: English
• Published: London Springer London 01.03.2024; Springer Nature B.V
• Subjects: Affective computing; Artificial Intelligence; Channels; Cognition; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Deep learning; Electroencephalography; Fourier transforms; Image Processing and Computer Vision; Probability and Statistics in Computer Science; Review; Self-supervised learning; Spatial data; Subsystems; Topology; Deep learning; Affective computing; Vision transformer (ViT); Self-supervised learning
• DOI: 10.1007/s00521-023-09289-z
• ISSN: 0941-0643

The balance of brain functional segregation (i.e., the process in specialized local subsystems) and integration (i.e., the process in global cooperation of the subsystems) is crucial for cognition in human beings, and many deep learning models have been used to evaluate the spatial information during EEG-based affective computing. However, acquiring the intrinsic spatial representation in the topology of EEG channels is still challenging. To further address the issue, we propose the FAformer to enhance spatial information in EEG signals with parallel-branch architectures based on a vision transformer (ViT). In the encoder, there is a branch that utilizes Adaptive Neural Fourier Operators (AFNO) to model global spatial patterns using the Fourier transform in the electrode channel dimension. The other branch utilizes multi-head self-attention (MSA) to explore the dependence of emotion on different channels, which is conducive to building key local networks. Additionally, a self-supervised learning (SSL) task of adaptive feature dissociation (AdaptiveFD) is developed to improve the distinctiveness of spatial features generated from the parallel branches and guarantee robustness in different subjects. FAformer achieves superior performance over the competitive models on the DREAMER and DEAP. Moreover, the rationality and hyperparameters analysis are conducted to demonstrate the effectiveness of the FAformer. Finally, the visualization of features reveals the spatial global connections and key local patterns during the deep learning process in FAformer, which benefits EEG-based affective computing.