Results 1 to 10 of about 97,052 (301)

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

Journal of NeuroEngineering and Rehabilitation
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.
Laura Ferrero, Paula Soriano-Segura, Jacobo Navarro, Oscar Jones, Mario Ortiz, Eduardo Iáñez, José M. Azorín, José L. Contreras-Vidal   +7 more
doaj   +2 more sources

Brain-machine interface based on transfer-learning for detecting the appearance of obstacles during exoskeleton-assisted walking [PDF]

Frontiers in Neuroscience, 2023
IntroductionBrain-machine interfaces (BMIs) attempt to establish communication between the user and the device to be controlled. BMIs have great challenges to face in order to design a robust control in the real field of application.
Vicente Quiles, Vicente Quiles, Laura Ferrero, Laura Ferrero, Laura Ferrero, Eduardo Iáñez, Eduardo Iáñez, Mario Ortiz, Mario Ortiz, Mario Ortiz, Ángel Gil-Agudo, José M. Azorín, José M. Azorín, José M. Azorín, José M. Azorín   +14 more
doaj   +2 more sources

Brain-Machine Interface (BMI) in paralysis

Annals of Physical and Rehabilitation Medicine, 2015
Brain-machine interfaces (BMIs) use brain activity to control external devices, facilitating paralyzed patients to interact with the environment. In this review, we focus on the current advances of non-invasive BMIs for communication in patients with amyotrophic lateral sclerosis (ALS) and for restoration of motor impairment after severe stroke.BMI ...
Niels Birbaumer
exaly   +3 more sources

Real-time linear prediction of simultaneous and independent movements of two finger groups using an intracortical brain-machine interface. [PDF]

Neuron, 2021
Nason SR, Mender MJ, Vaskov AK, Willsey MS, Ganesh Kumar N, Kung TA, Patil PG, Chestek CA.   +7 more
europepmc   +2 more sources

A new full closed-loop brain-machine interface approach based on neural activity: A study based on modeling and experimental studies. [PDF]

Heliyon, 2023
Amiri M, Nazari S, Jafari AH, Makkiabadi B.   +3 more
europepmc   +2 more sources

Parameter optimization of 3D convolutional neural network for dry-EEG motor imagery brain-machine interface. [PDF]

Front Neurosci
Kobayashi N, Ino M.
europepmc   +2 more sources

Review of tDCS Configurations for Stimulation of the Lower-Limb Area of Motor Cortex and Cerebellum

Brain Sciences, 2022
This article presents an exhaustive analysis of the works present in the literature pertaining to transcranial direct current stimulation(tDCS) applications.
Vicente Quiles, Laura Ferrero, Eduardo Iáñez, Mario Ortiz, José M. Azorín   +4 more
doaj   +1 more source

A BMI Based on Motor Imagery and Attention for Commanding a Lower-Limb Robotic Exoskeleton: A Case Study

Applied Sciences, 2021
Lower-limb robotic exoskeletons are wearable devices that can be beneficial for people with lower-extremity motor impairment because they can be valuable in rehabilitation or assistance.
Laura Ferrero, Vicente Quiles, Mario Ortiz, Eduardo Iáñez, José M. Azorín   +4 more
doaj   +1 more source

Decoding of Turning Intention during Walking Based on EEG Biomarkers

Biosensors, 2022
In the EEG literature, there is a lack of asynchronous intention models that realistically propose interfaces for applications that must operate in real time.
Vicente Quiles, Laura Ferrero, Eduardo Iáñez, Mario Ortiz, José M. Azorín   +4 more
doaj   +1 more source

Transitioning from global to local computational strategies during brain-machine interface learning. [PDF]

Front Neurosci
Bridges NR, Stickle M, Moxon KA.
europepmc   +2 more sources