CNN-based Approaches For Cross-Subject Classification in Motor Imagery: From the State-of-The-Art to DynamicNet-Reference-Cited by-同舟云学术

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CNN-based Approaches For Cross-Subject Classification in Motor Imagery: From the State-of-The-Art to DynamicNet

Published:2021-10-13 Issue: Volume: Page:
ISSN:
Container-title:2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)
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Author:

Zancanaro Alberto,Cisotto Giulia,Paulo Joao Ruivo,Pires Gabriel,Nunes Urbano J.

Funder

Portuguese Foundation for Science and Technology (FCT)

ISR-UC

University of Padova

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9562742/9562772/09562821.pdf?arnumber=9562821

Cited by 16 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A radial basis deformable residual convolutional neural model embedded with local multi-modal feature knowledge and its application in cross-subject classification;Expert Systems with Applications;2024-12

2. Ten quick tips for clinical electroencephalographic (EEG) data acquisition and signal processing;PeerJ Computer Science;2024-09-03

3. Toward calibration-free motor imagery brain–computer interfaces: a VGG-based convolutional neural network and WGAN approach;Journal of Neural Engineering;2024-07-31

4. A systematic evaluation of Euclidean alignment with deep learning for EEG decoding;Journal of Neural Engineering;2024-06-01

5. Optimal Channel Selection of Multiclass Motor Imagery Classification Based on Fusion Convolutional Neural Network with Attention Blocks;Sensors;2024-05-16

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