Results 41 to 50 of about 388,034 (342)

Analysis of the EEG Rhythms Based on the Empirical Mode Decomposition During Motor Imagery When Using a Lower-Limb Exoskeleton. A Case Study

Frontiers in Neurorobotics, 2020
The use of brain-machine interfaces in combination with robotic exoskeletons is usually based on the analysis of the changes in power that some brain rhythms experience during a motion event.
Mario Ortiz, Mario Ortiz, Eduardo Iáñez, José L. Contreras-Vidal, José M. Azorín   +4 more
doaj   +1 more source

From thought to action: The brain-machine interface in posterior parietal cortex. [PDF]

Proc Natl Acad Sci U S A, 2019
A dramatic example of translational monkey research is the development of neural prosthetics for assisting paralyzed patients. A neuroprosthesis consists of implanted electrodes that can record the intended movement of a paralyzed part of the body, a ...
Andersen RA, Aflalo T, Kellis S.
europepmc   +2 more sources

Brain-computer interface enhanced by virtual reality training for controlling a lower limb exoskeleton

iScience, 2023
Summary: This study explores the use of a brain-computer interface (BCI) based on motor imagery (MI) for the control of a lower limb exoskeleton to aid in motor recovery after a neural injury.
Laura Ferrero, Vicente Quiles, Mario Ortiz, Eduardo Iáñez, Ángel Gil-Agudo, José M. Azorín   +5 more
doaj   +1 more source

Connecting the Brain to Itself through an Emulation. [PDF]

, 2017
Pilot clinical trials of human patients implanted with devices that can chronically record and stimulate ensembles of hundreds to thousands of individual neurons offer the possibility of expanding the substrate of cognition.
Serruya, Mijail D.
core   +3 more sources

Incorporating Feedback from Multiple Sensory Modalities Enhances Brain–Machine Interface Control [PDF]

, 2010
The brain typically uses a rich supply of feedback from multiple sensory modalities to control movement in healthy individuals. In many individuals, these afferent pathways, as well as their efferent counterparts, are compromised by disease or injury ...
Fagg, Andrew H., Hatsopoulos, Nicholas G., Suminski, Aaron J., Tkach, Dennis C.   +3 more
core   +2 more sources

Defining brain–machine interface applications by matching interface performance with device requirements [PDF]

, 2007
Interaction with machines is mediated by human-machine interfaces (HMIs). Brain-machine interfaces (BMIs) are a particular class of HMIs and have so far been studied as a communication means for people who have little or no voluntary control of muscle ...
Aggarwal, Ajiboye, Axelrod, Bailey, Bayliss, Bayliss, Birbaumer, Birbaumer, Birbaumer, Blankertz, Bokil, Boksem, Buttfield, Card, Carmena, Chan, Chapin, Cheng, Cincotti, Citi, Corneil, Coyle, Crampton, Curran, De Silva, Donchin, Donoghue, Dornhege, Englehart, Fabiani, Farwell, Fetz, Fitts, Gao, Geng, Georgopoulos, Giuseppe Megali, Graimann, Graimann, Grauman, Guger, Hendy, Hinz, Hochberg, Humphrey, Hyrskykari, Inverso, Jacob, Kaper, Kauhanen, Kelly, Kelly, Kennedy, Kennedy, Kennedy, Kennedy, Kostov, Krebs, Kronegg, Kübler, Laubach, Lebedev, Leuthardt, Lorist, Luca Citi, Luca Rossini, MacKenzie, MacKenzie, Marg, Martina Marinelli, Mason, Mason, Mason, Maynard, McFarland, Meinicke, Micera, Middendorf, Millán, Millán, Moran, Mussa-Ivaldi, Müller-Putz, Müller-Putz, Navarro, Neumann, Neuper, Obermaier, Oh, Oliver Tonet, Paolo Dario, Paolo Maria Rossini, Perelmouter, Pfurtscheller, Pfurtscheller, Pfurtscheller, Plaisant, Rickert, Rossini, Rossini, Santhanam, Scherer, Sellers, Sellers, Serby, Serruya, Sutter, Tanaka, Taylor, Taylor, Tecchio, Tejima, Thulasidas, Thulasidas, Tomei, Urban, Vaughan, Vuckovic, Wang, Wang, Ward, Weiskopf, Wessberg, Wilson, Wolpaw, Wolpaw, Wolpaw, Wolpaw, Wolpaw, Xiao, Yoo, Zecca   +131 more
core   +1 more source

Brain-Machine Interfaces beyond Neuroprosthetics [PDF]

Neuron, 2015
The field of invasive brain-machine interfaces (BMIs) is typically associated with neuroprosthetic applications aiming to recover loss of motor function. However, BMIs also represent a powerful tool to address fundamental questions in neuroscience.
Moxon, Karen A., Foffani, Guglielmo
openaire   +2 more sources

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

Neuron, 2020
Modern brain-machine interfaces can return function to people with paralysis, but current hand neural prostheses are unable to reproduce control of individuated finger movements.
Samuel R. Nason, Matthew J. Mender, Alex K. Vaskov, Matthew S. Willsey, Parag G. Patil, C. Chestek   +5 more
semanticscholar   +1 more source

Semi-Autonomous Robotic Arm Reaching With Hybrid Gaze-Brain Machine Interface. [PDF]

Front Neurorobot, 2019
Recent developments in the non-muscular human–robot interface (HRI) and shared control strategies have shown potential for controlling the assistive robotic arm by people with no residual movement or muscular activity in upper limbs.
Zeng H, Shen Y, Hu X, Song A, Xu B, Li H, Wang Y, Wen P.   +7 more
europepmc   +2 more sources

Machine Learning for Neuroimaging with Scikit-Learn [PDF]

, 2013
Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g. multivariate analysis of activation images or resting-state time series.
Abraham, Alexandre, Eickenberg, Michael, Gervais, Philippe, Gramfort, Alexandre, Kossaifi, Jean, Muller, Andreas, Pedregosa, Fabian, Thirion, Bertrand, Varoquaux, Gäel   +8 more
core   +4 more sources