Literature DB >> 26796293

Brain-computer interfaces for dissecting cognitive processes underlying sensorimotor control.

Matthew D Golub1, Steven M Chase2, Aaron P Batista3, Byron M Yu4.   

Abstract

Sensorimotor control engages cognitive processes such as prediction, learning, and multisensory integration. Understanding the neural mechanisms underlying these cognitive processes with arm reaching is challenging because we currently record only a fraction of the relevant neurons, the arm has nonlinear dynamics, and multiple modalities of sensory feedback contribute to control. A brain-computer interface (BCI) is a well-defined sensorimotor loop with key simplifying advantages that address each of these challenges, while engaging similar cognitive processes. As a result, BCI is becoming recognized as a powerful tool for basic scientific studies of sensorimotor control. Here, we describe the benefits of BCI for basic scientific inquiries and review recent BCI studies that have uncovered new insights into the neural mechanisms underlying sensorimotor control.
Copyright © 2015 Elsevier Ltd. All rights reserved.

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Year:  2016        PMID: 26796293      PMCID: PMC4860084          DOI: 10.1016/j.conb.2015.12.005

Source DB:  PubMed          Journal:  Curr Opin Neurobiol        ISSN: 0959-4388            Impact factor:   6.627


  66 in total

1.  Bayesian integration in sensorimotor learning.

Authors:  Konrad P Körding; Daniel M Wolpert
Journal:  Nature       Date:  2004-01-15       Impact factor: 49.962

Review 2.  Computational mechanisms of sensorimotor control.

Authors:  David W Franklin; Daniel M Wolpert
Journal:  Neuron       Date:  2011-11-03       Impact factor: 17.173

3.  Behavioral and neural correlates of visuomotor adaptation observed through a brain-computer interface in primary motor cortex.

Authors:  Steven M Chase; Robert E Kass; Andrew B Schwartz
Journal:  J Neurophysiol       Date:  2012-04-11       Impact factor: 2.714

4.  Closed-loop decoder adaptation shapes neural plasticity for skillful neuroprosthetic control.

Authors:  Amy L Orsborn; Helene G Moorman; Simon A Overduin; Maryam M Shanechi; Dragan F Dimitrov; Jose M Carmena
Journal:  Neuron       Date:  2014-06-18       Impact factor: 17.173

5.  Automatic scan test for detection of functional connectivity between cortex and muscles.

Authors:  Sagi Perel; Andrew B Schwartz; Valérie Ventura
Journal:  J Neurophysiol       Date:  2014-04-23       Impact factor: 2.714

6.  Impairments of reaching movements in patients without proprioception. I. Spatial errors.

Authors:  J Gordon; M F Ghilardi; C Ghez
Journal:  J Neurophysiol       Date:  1995-01       Impact factor: 2.714

7.  Cortical adaptation to a chronic micro-electrocorticographic brain computer interface.

Authors:  Adam G Rouse; Jordan J Williams; Jesse J Wheeler; Daniel W Moran
Journal:  J Neurosci       Date:  2013-01-23       Impact factor: 6.167

Review 8.  Brain-computer interfaces: a powerful tool for scientific inquiry.

Authors:  Jeremiah D Wander; Rajesh P N Rao
Journal:  Curr Opin Neurobiol       Date:  2013-12-27       Impact factor: 6.627

Review 9.  Creating new functional circuits for action via brain-machine interfaces.

Authors:  Amy L Orsborn; Jose M Carmena
Journal:  Front Comput Neurosci       Date:  2013-11-05       Impact factor: 2.380

10.  Neural constraints on learning.

Authors:  Patrick T Sadtler; Kristin M Quick; Matthew D Golub; Steven M Chase; Stephen I Ryu; Elizabeth C Tyler-Kabara; Byron M Yu; Aaron P Batista
Journal:  Nature       Date:  2014-08-28       Impact factor: 49.962

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  29 in total

1.  Distinct types of neural reorganization during long-term learning.

Authors:  Xiao Zhou; Rex N Tien; Sadhana Ravikumar; Steven M Chase
Journal:  J Neurophysiol       Date:  2019-02-06       Impact factor: 2.714

2.  Learning is shaped by abrupt changes in neural engagement.

Authors:  Aaron P Batista; Steven M Chase; Byron M Yu; Jay A Hennig; Emily R Oby; Matthew D Golub; Lindsay A Bahureksa; Patrick T Sadtler; Kristin M Quick; Stephen I Ryu; Elizabeth C Tyler-Kabara
Journal:  Nat Neurosci       Date:  2021-03-29       Impact factor: 24.884

Review 3.  Interfacing to the brain's motor decisions.

Authors:  Giovanni Mirabella; Mikhail А Lebedev
Journal:  J Neurophysiol       Date:  2016-12-21       Impact factor: 2.714

4.  An inter-subject model to reduce the calibration time for motion imagination-based brain-computer interface.

Authors:  Yijun Zou; Xingang Zhao; Yaqi Chu; Yiwen Zhao; Weiliang Xu; Jianda Han
Journal:  Med Biol Eng Comput       Date:  2018-11-29       Impact factor: 2.602

5.  Reconfiguring Motor Circuits for a Joint Manual and BCI Task.

Authors:  Benjamin Lansdell; Ivana Milovanovic; Cooper Mellema; Eberhard E Fetz; Adrienne L Fairhall; Chet T Moritz
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2019-09-27       Impact factor: 3.802

Review 6.  Parsing learning in networks using brain-machine interfaces.

Authors:  Amy L Orsborn; Bijan Pesaran
Journal:  Curr Opin Neurobiol       Date:  2017-08-24       Impact factor: 6.627

Review 7.  Phantom Limbs, Neuroprosthetics, and the Developmental Origins of Embodiment.

Authors:  Mark S Blumberg; James C Dooley
Journal:  Trends Neurosci       Date:  2017-10       Impact factor: 13.837

Review 8.  Computation Through Neural Population Dynamics.

Authors:  Saurabh Vyas; Matthew D Golub; David Sussillo; Krishna V Shenoy
Journal:  Annu Rev Neurosci       Date:  2020-07-08       Impact factor: 12.449

Review 9.  Brain-machine interfaces from motor to mood.

Authors:  Maryam M Shanechi
Journal:  Nat Neurosci       Date:  2019-09-24       Impact factor: 24.884

10.  Learning active sensing strategies using a sensory brain-machine interface.

Authors:  Andrew G Richardson; Yohannes Ghenbot; Xilin Liu; Han Hao; Cole Rinehart; Sam DeLuccia; Solymar Torres Maldonado; Gregory Boyek; Milin Zhang; Firooz Aflatouni; Jan Van der Spiegel; Timothy H Lucas
Journal:  Proc Natl Acad Sci U S A       Date:  2019-08-13       Impact factor: 11.205

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