Decoding Multi-Class Motor Imagery and Motor Execution Tasks Using Riemannian Geometry Algorithms on Large EEG Datasets

The use of Riemannian geometry decoding algorithms in classifying electroencephalography-based motor-imagery brain–computer interfaces (BCIs) trials is relatively new and promises to outperform the current state-of-the-art methods by overcoming the noise and nonstationarity of electroencephalography...

Descripción completa

Detalles Bibliográficos
Autores principales: Shuqfa, Zaid, Belkacem, Abdelkader Nasreddine, Lakas, Abderrahmane
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10255410/
https://www.ncbi.nlm.nih.gov/pubmed/37299779
http://dx.doi.org/10.3390/s23115051

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10255410/
https://www.ncbi.nlm.nih.gov/pubmed/37299779
http://dx.doi.org/10.3390/s23115051

Ejemplares similares