MAFNet: Multi-domain Features Attention-Based Fusion Network for Cross-Subject Motor Imagery Classification-Reference-Cited by-同舟云学术

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MAFNet: Multi-domain Features Attention-Based Fusion Network for Cross-Subject Motor Imagery Classification

Published:2024-06-30 Issue: Volume:16 Page:1-7
ISSN:
Container-title:2024 International Joint Conference on Neural Networks (IJCNN)
language:
Short-container-title:

Author:

Hong Yu¹,Zeng Xinhua¹,Wu Feng²,Wang Jingjing²

Affiliation:

1. Fudan University,Academy for Engineering and Technology,Shanghai,China,200433

2. Shanghai University of Traditional Chinese Medicine,Yueyang Hospital of Integrated Traditional Chinese and Western Medicine,Department of Cardiology,Shanghai,China,200437

Funder

Science and Technology Commission of Shanghai Municipality

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10649807/10649898/10651271.pdf?arnumber=10651271

Reference28 articles.

1. A Critical Survey of EEG-Based BCI Systems for Applications in Industrial Internet of Things

2. EEG-Based Strategies to Detect Motor Imagery for Control and Rehabilitation

3. Brain-computer interface boosts motor imagery practice during stroke recovery

4. Decoding motor imagery from the posterior parietal cortex of a tetraplegic human

5. An EEG database for the cognitive assessment of motor imagery during walking with a lower-limb exoskeleton

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