Brain–machine interface based on deep learning to control asynchronously a lower-limb robotic exoskeleton: a case-of-study [PDF]
Journal of NeuroEngineering and RehabilitationBackground 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 +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 +14 more
doaj +2 more sources
Brain–machine interface for eye movements [PDF]
Proceedings of the National Academy of Sciences of the United States of America, 2014 Significance We developed a brain–machine interface (BMI) that records single cell activity from populations of neurons for decoding planned eye movements (i.e., saccades) without the animals executing them. The recordings were made from the lateral intraparietal area, an important cortical node in the primate saccade system.
Richard Andersen
exaly +4 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 +4 more
doaj +1 more source
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 +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 +4 more
doaj +1 more source
Real-time linear prediction of simultaneous and independent movements of two finger groups using an intracortical brain-machine interface. [PDF]
Neuron, 2021 Nason SR +7 more
europepmc +2 more sources
IEEE Access, 2021 Motor imagery (MI) is one of the most common paradigms used in brain-computer interfaces (BCIs). This mental process is defined as the imagination of movement without any motion.
L. Ferrero +4 more
doaj +1 more source
Parameter optimization of 3D convolutional neural network for dry-EEG motor imagery brain-machine interface. [PDF]
Front NeurosciKobayashi N, Ino M.
europepmc +2 more sources
Intention Concepts and Brain-Machine Interfacing [PDF]
Frontiers in Psychology, 2012 Intentions, including their temporal properties and semantic content, are receiving increased attention, and neuroscientific studies in humans vary with respect to the topography of intention-related neural responses. This may reflect the fact that the kind of intentions investigated in one study may not be exactly the same kind investigated in the ...
Franziska eThinnes-Elker +12 more
openaire +3 more sources