Sparse Bayesian Learning for End-to-End EEG Decoding-Reference-Cited by-同舟云学术

Sparse Bayesian Learning for End-to-End EEG Decoding

Published:2023-12 Issue:12 Volume:45 Page:15632-15649
ISSN:0162-8828
Container-title:IEEE Transactions on Pattern Analysis and Machine Intelligence
language:
Short-container-title:IEEE Trans. Pattern Anal. Mach. Intell.

Author:

Wang Wenlong¹^ORCID,Qi Feifei²^ORCID,Wipf David Paul³^ORCID,Cai Chang⁴^ORCID,Yu Tianyou¹^ORCID,Li Yuanqing¹^ORCID,Zhang Yu⁵^ORCID,Yu Zhuliang¹^ORCID,Wu Wei⁶^ORCID

Affiliation:

1. School of Automation Science and Engineering, South China University of Technology, Guangzhou, Guangdong, China

2. School of Internet Finance and Information Engineering, Guangdong University of Finance, Guangzhou, Guangdong, China

3. Amazon Shanghai AI Lab, Shanghai, China

4. National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, Hubei, China

5. Department of Bioengineering, Lehigh University, Bethlehem, PA, USA

6. Alto Neuroscience Inc., Los Altos, CA, USA

Funder

National Natural Science Foundation of China

Technology Innovation 2030

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Artificial Intelligence,Computational Theory and Mathematics,Computer Vision and Pattern Recognition,Software

Link

http://xplorestaging.ieee.org/ielx7/34/10308548/10197212.pdf?arnumber=10197212

Reference54 articles.

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2. RSTFC: A Novel Algorithm for Spatio-Temporal Filtering and Classification of Single-Trial EEG

3. Classifying Single-Trial EEG During Motor Imagery by Iterative Spatio-Spectral Patterns Learning (ISSPL)

4. Spectrally weighted common spatial pattern algorithm for single trial EEG classification;tomioka,2006

5. Predictive regression modeling with MEG/EEG: from source power to signals and cognitive states

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