Subject-Dependent Artifact Removal for Enhancing Motor Imagery Classifier Performance under Poor Skills

The Electroencephalography (EEG)-based motor imagery (MI) paradigm is one of the most studied technologies for Brain-Computer Interface (BCI) development. Still, the low Signal-to-Noise Ratio (SNR) poses a challenge when constructing EEG-based BCI systems. Moreover, the non-stationary and nonlinear...

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Detalles Bibliográficos
Autores principales: Tobón-Henao, Mateo, Álvarez-Meza, Andrés, Castellanos-Domínguez, Germán
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9371054/
https://www.ncbi.nlm.nih.gov/pubmed/35957329
http://dx.doi.org/10.3390/s22155771

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9371054/
https://www.ncbi.nlm.nih.gov/pubmed/35957329
http://dx.doi.org/10.3390/s22155771

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