Results 31 to 40 of about 207,295 (281)

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

Effect of tDCS stimulation of motor cortex and cerebellum on EEG classification of motor imagery and sensorimotor band power

Journal of NeuroEngineering and Rehabilitation, 2017
Background Transcranial direct current stimulation (tDCS) is a technique for brain modulation that has potential to be used in motor neurorehabilitation.
Irma N. Angulo-Sherman, Marisol Rodríguez-Ugarte, Nadia Sciacca, Eduardo Iáñez, José M. Azorín   +4 more
doaj   +1 more source

Communication channel analysis and real time compressed sensing for high density neural recording devices [PDF]

, 2016
Next generation neural recording and Brain- Machine Interface (BMI) devices call for high density or distributed systems with more than 1000 recording sites.
Duncan, Kerron, Etienne-Cummings, Ralph, Mitra, Srinjoy, Suo, Yuanming, Tran, Trac Duy, Xiong, Tao, Zhang, Jie   +6 more
core   +1 more source

A Brain-Machine Interface for Control of Medically-Induced Coma [PDF]

, 2013
Medically-induced coma is a drug-induced state of profound brain inactivation and unconsciousness used to treat refractory intracranial hypertension and to manage treatment-resistant epilepsy.
Brown, Emery N., Chemali, Jessica J., Liberman, Max, Shanechi, Maryam M., Solt, Ken   +4 more
core   +2 more sources

Neural encoding of actual and imagined touch within human posterior parietal cortex

eLife, 2021
In the human posterior parietal cortex (PPC), single units encode high-dimensional information with partially mixed representations that enable small populations of neurons to encode many variables relevant to movement planning, execution, cognition, and
Srinivas Chivukula, Carey Y Zhang, Tyson Aflalo, Matiar Jafari, Kelsie Pejsa, Nader Pouratian, Richard A Andersen   +6 more
doaj   +1 more source

Chapter 15 Matching Brain–Machine Interface Performance to Space Applications [PDF]

, 2009
A brain-machine interface (BMI) is a particular class of human-machine interface (HMI). BMIs have so far been studied mostly as a communication means for people who have little or no voluntary control of muscle activity.
Acharya, Achtman, Bai, Bell, Brychta, Cincotti, Citi, Donoghue, Farina, Galán, Gao, Hirzinger, Hochberg, Hoffmann, Karim, Kim, Krepki, Kronegg, Miller, Momen, Mussa‐Ivaldi, Müller‐Putz, Müller‐Putz, Nijboer, Peñín, Pham, Shannon, Shenoy, Sheridan, Tanaka, Tonet, Tonet, Trejo, Truccolo, Wilcox, Wolpaw, Wolpaw   +36 more
core   +1 more source

Brain-Machine: nuevas interfaces / Brain Machines: New Interfaces

Bajo Palabra, 2017
A pesar del creciente número de descubrimientos en la neurociencia, los enormes logros tecnológicos y algún nuevo impulso teórico, aún sigue firme entre los científicos –así como en el sentido común– un contraste claro y marcado entre la mente y el cuerpo.
openaire   +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, Franziska eThinnes-Elker, Franziska eThinnes-Elker, Olga eIljina, Olga eIljina, John Kyle Apostolides, John Kyle Apostolides, Felicitas eKraemer, Andreas eSchulze-Bonhage, Ad eAertsen, Ad eAertsen, Tonio eBall, Tonio eBall   +12 more
openaire   +3 more sources

Real-time EEG-based brain-computer interface to a virtual avatar enhances cortical involvement in human treadmill walking

Scientific Reports, 2017
Recent advances in non-invasive brain-computer interface (BCI) technologies have shown the feasibility of neural decoding for both users’ gait intent and continuous kinematics. However, the dynamics of cortical involvement in human upright walking with a
Trieu Phat Luu, Sho Nakagome, Yongtian He, Jose L. Contreras-Vidal   +3 more
doaj   +1 more source

Evaluating Classifiers to Detect Arm Movement Intention from EEG Signals

Sensors, 2014
This paper presents a methodology to detect the intention to make a reaching movement with the arm in healthy subjects before the movement actually starts.
Daniel Planelles, Enrique Hortal, Álvaro Costa, Andrés Úbeda, Eduardo Iáez, José M. Azorín   +5 more
doaj   +1 more source