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

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 durin...