Structure of multi-branch separable convolution.

<div><p>Motor imagery (MI) is a brain-computer interface (BCI) technology with the potential to change human life in the future. MI signals have been widely applied in various BCI applications, including neurorehabilitation, smart home control, and prosthetic control. However, the limite...

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Bibliographic Details
Main Author:	DianGuo Cao (22486584) (author)
Other Authors:	ZhenYuan Yu (22486587) (author), Jinqiang Wang (1561045) (author), Yuqiang Wu (12471975) (author)
Published:	2025
Subjects:	Cell Biology Genetics Physiology Science Policy Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified subject classification accuracies combines spatiotemporal convolution change human life branch separable convolution subject classification accuracy smart home control multimodal feature fusion level temporal features sc ), multi various bci applications msc ), multi capture temporal features feature fusion hybrid multi spatial features sc module prosthetic control limited accuracy features output bci applications decision fusion tcn ), widely applied term dependence strong foundation significant barrier results showed results indicate multiple scales mff consists mff ). kappa values kappa value including neurorehabilitation improve robustness head self computer interface capture higher broader growth 2a dataset 21 %,
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Structure of multi-branch separable convolution.

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