Coupling Convolution, Transformer and Graph Embedding for Motor Imagery Brain-Computer Interfaces
Author:
Affiliation:
1. Tianjin University,School of Electrical and Information Engineering,Tianjin,China,300072
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9937201/9937203/09937435.pdf?arnumber=9937435
Reference21 articles.
1. A graph-temporal fused dual-input convolutional neural network for detecting sleep stages from eeg signals;cai;IEEE Transactions on Circuits and Systems II Express Briefs,2020
2. Bispectrum-Based Channel Selection for Motor Imagery Based Brain-Computer Interfacing
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5. Non-Autoregressive Transformer for Speech Recognition
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1. Data Constraints and Performance Optimization for Transformer-Based Models in EEG-Based Brain-Computer Interfaces: A Survey;IEEE Access;2024
2. Deep Learning in EEG-Based BCIs: A Comprehensive Review of Transformer Models, Advantages, Challenges, and Applications;IEEE Access;2023
3. Deep Learning in Motor Imagery Eeg Signal Decoding: A Systematic Review;2023
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