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- W3045786036 abstract "Stroke is one of the leading causes of disability and incidence. For the treatment of prognosis of stroke patients, Motor imagery (MI) as a novel experimental paradigm, clinically it is effective because MI based Brain-Computer interface system can promote rehabilitation of stroke patients. There is being a hot and challenging topic to recognize multi-class motor imagery action classification accurately based on electroencephalograph (EEG) signals. In this work, we propose a novel framework named MRC-MLP. Multiple Riemannian covariance is used for EEG feature extraction. We make a multi-scale spectral division to filter EEG signals. They consist of different frequency bandwidths name sub-band. We concatenate and vectorize features extracted by Riemannian covariance on each sub-band. We design a fully connected MLP model with an improved loss function for motor imagery EEG classification. Furthermore, our proposed method MRC-MLP outperforms state-of-the-art methods and achieves approximately mean accuracy with 76%." @default.
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- W3045786036 date "2020-01-01" @default.
- W3045786036 modified "2023-09-25" @default.
- W3045786036 title "MLP With Riemannian Covariance for Motor Imagery Based EEG Analysis" @default.
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- W3045786036 doi "https://doi.org/10.1109/access.2020.3011969" @default.
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