Motor Imagery EEG Classification Based on Riemannian Sparse Optimization and Dempster-Shafer Fusion of Multi-Time-Frequency Patterns

Motor Imagery EEG Classification Based on Riemannian Sparse Optimization and Dempster-Shafer Fusion of Multi-Time-Frequency Patterns

Motor imagery-based brain-computer interfaces (MI-BCIs) features are generally extracted from a wide fixed frequency band and time window of EEG signal. The performance suffers from individual differences in corresponding time to MI tasks. In order to solve the problem, in this study, we propose a n...

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Bibliographic Details
Main Authors: Jing Jin, Tingnan Qu, Ren Xu, Xingyu Wang, Andrzej Cichocki
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Transactions on Neural Systems and Rehabilitation Engineering
Subjects:
Brain-computer interface
motor imagery
Riemannian geometry
sparse optimization
Dempster-Shafer theory
Online Access:https://ieeexplore.ieee.org/document/9931161/

Internet

https://ieeexplore.ieee.org/document/9931161/

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