• CGNet: A Complex-valued Graph...

Treffer: CGNet: A Complex-valued Graph Network for jointly learning amplitude-phase information in EEG-based brain-computer interfaces.

Title:
CGNet: A Complex-valued Graph Network for jointly learning amplitude-phase information in EEG-based brain-computer interfaces.
Authors:
Cai G; School of Information Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, China. Electronic address: cgq982023711@163.com., Chen Y; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; National Center for Neurological Disorders, China; China National Clinical Research Center for Neurological Diseases, Beijing, China. Electronic address: cyeeyeee@163.com., Yang B; School of Information Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, China. Electronic address: 422141205@qq.com., Yang Y; School of Information Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, China; Peng Cheng Laboratory, Shenzhen, China. Electronic address: 20b952019@stu.hit.edu.cn., Ma T; School of Biomedical Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, China; Peng Cheng Laboratory, Shenzhen, China. Electronic address: tma@hit.edu.cn., Wang Y; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; Chinese Institute for Brain Research, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China. Electronic address: yilong528@aliyun.com.
Source:
Neural networks : the official journal of the International Neural Network Society [Neural Netw] 2025 Nov; Vol. 191, pp. 107795. Date of Electronic Publication: 2025 Jul 05.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Pergamon Press Country of Publication: United States NLM ID: 8805018 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-2782 (Electronic) Linking ISSN: 08936080 NLM ISO Abbreviation: Neural Netw Subsets: MEDLINE
Imprint Name(s):
Original Publication: New York : Pergamon Press, [c1988-
MeSH Terms:
Brain-Computer Interfaces* , Electroencephalography*/methods , Neural Networks, Computer* , Brain*/physiology , Deep Learning*, Humans ; Algorithms
Contributed Indexing:
Keywords: Amplitude-phase learning; Brain–computer interfaces; Complex-valued computation; Global dependency; Graph convolutional network
Entry Date(s):
Date Created: 20250711 Date Completed: 20250906 Latest Revision: 20250906
Update Code:
20250906
DOI:
10.1016/j.neunet.2025.107795
PMID:
40644990
Database:
MEDLINE

Weitere Informationen

The synergy between amplitude and phase in electroencephalogram (EEG)-based brain-computer interfaces (BCIs) provides comprehensive and essential insights into neural oscillatory processes. However, constrained by real-valued computation paradigms, most deep learning methods have to process amplitude and phase independently, neglecting their crucial interaction mechanisms. To address this issue, we construct a Complex-valued Graph Network (CGNet) to capture comprehensive information from EEG signals, where both amplitude and phase information are encoded into the complex-valued representation. Specifically, we design a two-scale complex-valued convolutional network to learn local spatio-temporal information, develop a spatial attention module to enhance spatial information learning, and formulate a dynamic graph convolution to capture global temporal dependencies. Furthermore, we extend CGNet to Filter-Band CGNet (FBCGNet), enhancing the model's adaptability to broadband EEG data. Applied to motor imagery and execution BCI tasks, CGNet achieves state-of-the-art classification performance while maintaining computational efficiency comparable to other advanced algorithms. Notably, FBCGNet further improves CGNet's performance. Visualization results show that CGNet can identify the key spatio-temporal information consistent with paradigm principles. In addition, compared with using amplitude or phase alone, CGNet can capture more comprehensive task-related neural activities, thereby showing higher classification performance. CGNet is a promising tool for mining amplitude-phase information and offering more comprehensive neural decoding in EEG-based BCIs.
(Copyright © 2025 Elsevier Ltd. All rights reserved.)

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.