A novel methodology based on static visual stimuli and kinesthetic motor imagery for upper limb neurorehabilitation
Author:
Funder
Fundação de Amparo á Pesquisa e Inovação do Espírito Santo
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s42600-024-00372-5.pdf
Reference43 articles.
1. Alazrai R, Abuhijleh M, Alwanni H, et al. A deep learning framework for decoding motor imagery tasks of the same hand using EEG signals. IEEE Access. 2019;7:109612–27. https://doi.org/10.1109/access.2019.2934018.
2. Barachant A, Bonnet S, Congedo M, et al. Riemannian geometry applied to BCI classification. In: Latent Variable Analysis and Signal Separation. Springer Berlin Heidelberg; 2010. pp 629–636. https://doi.org/10.1007/978-3-642-15995-4_78.
3. Barachant A, Bonnet S, Congedo M, et al. Multiclass brain-computer interface classification by Riemannian geometry. IEEE Trans Biomed Eng. 2012;59(4):920–8. https://doi.org/10.1109/tbme.2011.2172210.
4. Barachant A, Bonnet S, Congedo M, et al. Classification of covariance matrices using a Riemannian-based kernel for BCI applications. Neurocomputing. 2013;112:172–8. https://doi.org/10.1016/j.neucom.2012.12.039.
5. Basteris A, Nijenhuis SM, Stienen AH, et al. Training modalities in robot-mediated upper limb rehabilitation in stroke: a framework for classification based on a systematic review. J Neuroeng Rehabil. 2014;11(1):111. https://doi.org/10.1186/1743-0003-11-111.
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