Identificador persistente para citar o vincular este elemento:
http://hdl.handle.net/10662/20662
Títulos: | EEG-based brain-computer interfaces using motor-imagery: Techniques and challenges |
Autores/as: | Padfield, Natasha Zabalza, Jaime Zhao, Huimin Masero Vargas, Valentín Ren, Jinchang |
Palabras clave: | Brain-computer interface (BCI);Interfaz cerebro-ordenador;Electroencephalography (EEG);Electroencephalografía (EEG);Motor-imagery (MI);Imagen de motricidad (IM) |
Fecha de publicación: | 2019 |
Editor/a: | MDPI |
Resumen: | Electroencephalography (EEG)-based brain-computer interfaces (BCIs), particularly those using motor-imagery (MI) data, have the potential to become groundbreaking technologies in both clinical and entertainment settings. MI data is generated when a subject imagines the movement of a limb. This paper reviews state-of-the-art signal processing techniques for MI EEG-based BCIs, with a particular focus on the feature extraction, feature selection and classification techniques used. It also summarizes the main applications of EEG-based BCIs, particularly those based on MI data, and finally presents a detailed discussion of the most prevalent challenges impeding the development and commercialization of EEG-based BCIs. |
ISSN: | 14248220 |
DOI: | 10.3390/s19061423 |
Colección: | DISIT - Artículos |
Archivos
Archivo | Descripción | Tamaño | Formato | |
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s19061423.pdf | 1,96 MB | Adobe PDF | Descargar |
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