2023
DOI: 10.3390/s23187694
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Decoding Algorithm of Motor Imagery Electroencephalogram Signal Based on CLRNet Network Model

Chaozhu Zhang,
Hongxing Chu,
Mingyuan Ma

Abstract: EEG decoding based on motor imagery is an important part of brain–computer interface technology and is an important indicator that determines the overall performance of the brain–computer interface. Due to the complexity of motor imagery EEG feature analysis, traditional classification models rely heavily on the signal preprocessing and feature design stages. End-to-end neural networks in deep learning have been applied to the classification task processing of motor imagery EEG and have shown good results. Thi… Show more

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Cited by 3 publications
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