Temporal Fusion Dynamically Separable Graph Convolutional Network for EEG Motion Intention Decoding Based on Source Information Extraction-Reference-Cited by-同舟云学术

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Temporal Fusion Dynamically Separable Graph Convolutional Network for EEG Motion Intention Decoding Based on Source Information Extraction

Published:2024 Issue: Volume:73 Page:1-13
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Tang Xianlun¹^ORCID,Wang Tianzhu¹^ORCID,Li Xingchen¹^ORCID,Zhu Wenbin¹^ORCID,Hong Xinyi²^ORCID,Gao Xinbo³^ORCID

Affiliation:

1. Chongqing Key Laboratory of Complex Systems and Bionic Control, Chongqing University of Posts and Telecommunications, Chongqing, China

2. Key Laboratory of Data Engineering and Visual Computing, Chongqing University of Posts and Telecommunications, Chongqing, China

3. Guangyang Bay Laboratory, Chongqing Institute for Brain and Intelligence, Chongqing, China

Funder

Natural Science Foundation Project of Chongqing

Science and Technology Research Program of Chongqing Municipal Education Commission

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/19/10367905/10568960.pdf?arnumber=10568960

Reference46 articles.

1. EEG-Based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications

2. A comparative analysis of signal processing and classification methods for different applications based on EEG signals

3. Model-Based Fitting of EEG Signals With Uncertainty Quantification for Robust Alpha Wave Identification

4. Source connectivity analysis with MEG and EEG

5. Effect of BCI-Controlled Pedaling Training System With Multiple Modalities of Feedback on Motor and Cognitive Function Rehabilitation of Early Subacute Stroke Patients

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