Brain–machine interface based on deep learning to control asynchronously a lower-limb robotic exoskeleton: a case-of-study

Brain–machine interface based on deep learning to control asynchronously a lower-limb robotic exoskeleton: a case-of-study

Abstract Background This research focused on the development of a motor imagery (MI) based brain–machine interface (BMI) using deep learning algorithms to control a lower-limb robotic exoskeleton. The study aimed to overcome the limitations of traditional BMI approaches by leveraging the advantages...

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Bibliographic Details
Main Authors: Laura Ferrero, Paula Soriano-Segura, Jacobo Navarro, Oscar Jones, Mario Ortiz, Eduardo Iáñez, José M. Azorín, José L. Contreras-Vidal
Format: Article
Language:English
Published: BMC 2024-04-01
Series:Journal of NeuroEngineering and Rehabilitation
Subjects:
Brain–machine interface
EEG
Exoskeleton
Deep learning
Transfer learning
Online Access:https://doi.org/10.1186/s12984-024-01342-9

Internet

https://doi.org/10.1186/s12984-024-01342-9

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