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

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Abstract

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.

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Data availability

The datasets used or analyzed during the current study are available as follows: DREAMER: https://www.embs.org/jbhi?s=DREAMER DEAP: http://www.eecs.qmul.ac.uk/mmv/datasets/deap/.

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Funding

This work is supported by the National Natural Science Foundation of China (62173008, 61602017), Beijing Natural Science Foundation (No. 4222022), and the Education and Teaching Research Project of Beijing University of Technology (ER2022SJB06).

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Authors and Affiliations

  1. Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China

    Ziheng Gao, Jiajin Huang, Jianhui Chen & Haiyan Zhou

  2. Beijing International Collaboration Base on Brain Informatics and Wisdom Services, Beijing, 100124, China

    Jiajin Huang, Jianhui Chen & Haiyan Zhou

  3. Engineering Research Center of Intelligent Perception and Autonomous Control, Ministry of Education, Beijing, 100124, China

    Jiajin Huang, Jianhui Chen & Haiyan Zhou

  4. Engineering Research Center of Digital Community, Ministry of Education, Beijing, 100124, China

    Jiajin Huang, Jianhui Chen & Haiyan Zhou

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  1. Ziheng Gao
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Correspondence to Haiyan Zhou.

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Gao, Z., Huang, J., Chen, J. et al. FAformer: parallel Fourier-attention architectures benefits EEG-based affective computing with enhanced spatial information. Neural Comput & Applic 36, 3903–3919 (2024). https://doi.org/10.1007/s00521-023-09289-z

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