Estimation of Neuromuscular Primitives from EEG Slow Cortical Potentials in Incomplete Spinal Cord Injury Individuals for a New Class of Brain-Machine Interfaces
Por favor, use este identificador para citar o enlazar este ítem:
http://hdl.handle.net/10045/72789
Título: | Estimation of Neuromuscular Primitives from EEG Slow Cortical Potentials in Incomplete Spinal Cord Injury Individuals for a New Class of Brain-Machine Interfaces |
---|---|
Autor/es: | Úbeda, Andrés | Azorín, José M. | Farina, Dario | Sartori, Massimo |
Grupo/s de investigación o GITE: | Automática, Robótica y Visión Artificial |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Física, Ingeniería de Sistemas y Teoría de la Señal |
Palabras clave: | Brain-machine interface | Muscle primitives | Corticospinal mapping | Linear decoders | Gait rehabilitation | Lower-limb exoskeletons |
Área/s de conocimiento: | Ingeniería de Sistemas y Automática |
Fecha de publicación: | 25-ene-2018 |
Editor: | Frontiers Media |
Cita bibliográfica: | Úbeda A, Azorín JM, Farina D and Sartori M (2018) Estimation of Neuromuscular Primitives from EEG Slow Cortical Potentials in Incomplete Spinal Cord Injury Individuals for a New Class of Brain-Machine Interfaces. Front. Comput. Neurosci. 12:3. doi: 10.3389/fncom.2018.00003 |
Resumen: | One of the current challenges in human motor rehabilitation is the robust application of Brain-Machine Interfaces to assistive technologies such as powered lower limb exoskeletons. Reliable decoding of motor intentions and accurate timing of the robotic device actuation is fundamental to optimally enhance the patient's functional improvement. Several studies show that it may be possible to extract motor intentions from electroencephalographic (EEG) signals. These findings, although notable, suggests that current techniques are still far from being systematically applied to an accurate real-time control of rehabilitation or assistive devices. Here we propose the estimation of spinal primitives of multi-muscle control from EEG, using electromyography (EMG) dimensionality reduction as a solution to increase the robustness of the method. We successfully apply this methodology, both to healthy and incomplete spinal cord injury (SCI) patients, to identify muscle contraction during periodical knee extension from the EEG. We then introduce a novel performance metric, which accurately evaluates muscle primitive activations. |
Patrocinador/es: | This research has been supported by Conselleria d'Educació, Cultura i Esport of Generalitat Valenciana of Spain through grant APOSTD/2015/104, by the European Research Council Advanced Grant DEMOVE (grant agreement 267888), by the European Commission as part of the project BioMot (FP7-ICT-2013-10, Grant Agreement 611695) and by the Spanish Ministry of Economy and Competitiveness and the European Union through the European Regional Development Fund (ERDF) “A way to build Europe” as part of the project Associate–Decoding and stimulation of motor and sensory brain activity to support long term potentiation through Hebbian and paired associative stimulation during rehabilitation of gait (DPI2014-58431-C4-2-R). |
URI: | http://hdl.handle.net/10045/72789 |
ISSN: | 1662-5188 |
DOI: | 10.3389/fncom.2018.00003 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © 2018 Úbeda, Azorín, Farina and Sartori. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
Revisión científica: | si |
Versión del editor: | http://dx.doi.org/10.3389/fncom.2018.00003 |
Aparece en las colecciones: | INV - AUROVA - Artículos de Revistas INV - HURO - Artículos de Revistas |
Archivos en este ítem:
Archivo | Descripción | Tamaño | Formato | |
---|---|---|---|---|
2018_Ubeda_etal_FrontComputNeurosci.pdf | 9,16 MB | Adobe PDF | Abrir Vista previa | |
Este ítem está licenciado bajo Licencia Creative Commons