Results 21 to 30 of about 207,295 (281)

Sensory Integration in Human Movement: A New Brain-Machine Interface Based on Gamma Band and Attention Level for Controlling a Lower-Limb Exoskeleton

Frontiers in Bioengineering and Biotechnology, 2020
Brain-machine interfaces (BMIs) can improve the control of assistance mobility devices making its use more intuitive and natural. In the case of an exoskeleton, they can also help rehabilitation therapies due to the reinforcement of neuro-plasticity ...
Mario Ortiz, Mario Ortiz, Laura Ferrero, Eduardo Iáñez, José M. Azorín, José L. Contreras-Vidal   +5 more
doaj   +1 more source

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

Brain-computer interface [PDF]

, 2011
A brain–computer interface (BCI), sometimes called a direct neural interface or a brain–machine interface (BMI), is a direct communication pathway between the brain and an external device.
Marchenko, D.O., Sirobaba, N.S.
core   +1 more source

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

Preparing Laboratory and Real-World EEG Data for Large-Scale Analysis: A Containerized Approach. [PDF]

, 2016
Large-scale analysis of EEG and other physiological measures promises new insights into brain processes and more accurate and robust brain-computer interface models.
Bigdely-Shamlo, Nima, Makeig, Scott, Robbins, Kay A   +2 more
core   +1 more source

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

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

GUIDER: a GUI for semiautomatic, physiologically driven EEG feature selection for a rehabilitation BCI [PDF]

, 2017
GUIDER is a graphical user interface developed in MATLAB software environment to identify electroencephalography (EEG)-based brain computer interface (BCI) control features for a rehabilitation application (i.e. post-stroke motor imagery training).
Cincotti, Febo, Colamarino, Emma, Martinoia, M., Mattia, D., Pichiorri, Floriana, Schettini, Francesca   +5 more
core   +1 more source