Direct Cortical Control of 3D Neuroprosthetic Devices

By Dawn M. Taylor, S. Tillery, Andrew B. Schwartz. Science, 2002

1,569 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/2b8898447fd4a8576e5b978282b1a758c8fa06aa

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

Citations in the corpus

Cortical Ensemble Adaptation to Represent Velocity of an Artificial Actuator Controlled by a Brain-Machine Interface (2005, methodology): “evokes plastic changes in the activity patterns of cortical neurons (Taylor et al., 2002; Carmena et al., 2003; Musallam et al., 2004), a number of questions about cortical representation of artificial actuators remain to be addressed.”
Long-term stability of neural prosthetic control signals from silicon cortical arrays in rhesus macaque motor cortex (2011, background): “7 Author to whom any correspondence should be addressed.”
Complex-Valued Neural Networks (2006, neutral): “An application of learning by optimization on the manifold S^>p(E) is to dimension reduction for image retrieval [47], which was designed to optimize class separation with respect to metrics derived from cross-correlation of spectral histograms.”
Functional network reorganization during learning in a brain-computer interface paradigm (2008, methodology): “The PV algorithm used for brain control (12, 18) assumes that each unit is cosine-tuned with intended movement direction (17): f b0 m cos , [1] where f is the firing rate of a model unit, b0 is its baseline firing rate, m is its modulation depth, and is the angle between the direction of intended mo”
Decoding two-dimensional movement trajectories using electrocorticographic signals in humans (2007, background): “When monkeys are provided feedback based on brain signals rather than actual hand movements, they initially continue to move the hand but then learn to produce the same signals without the actual physical movements [4].”
A brain–computer interface using electrocorticographic signals in humans (2004, background): “Due to technological limitations preventing real-time recording and analysis of data from many individual cortical neurons simultaneously, it was not until 2002 that Schwartz and colleagues became the first to obtain three-dimensional closed-loop, real-time control of a cursor in a monkey [13].”