Literature DB >> 25856486

Brain-machine interfaces beyond neuroprosthetics.

Karen A Moxon1, Guglielmo Foffani2.   

Abstract

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. The observed subjects of BMI experiments can also be considered as indirect observers of their own neurophysiological activity, and the relationship between observed neurons and (artificial) behavior can be genuinely causal rather than indirectly correlative. These two characteristics defy the classical object-observer duality, making BMIs particularly appealing for investigating how information is encoded and decoded by neural circuits in real time, how this coding changes with physiological learning and plasticity, and how it is altered in pathological conditions. Within neuroengineering, BMI is like a tree that opens its branches into many traditional engineering fields, but also extends deep roots into basic neuroscience beyond neuroprosthetics.
Copyright © 2015 Elsevier Inc. All rights reserved.

Mesh:

Year:  2015        PMID: 25856486     DOI: 10.1016/j.neuron.2015.03.036

Source DB:  PubMed          Journal:  Neuron        ISSN: 0896-6273            Impact factor:   17.173


  32 in total

1.  A rodent brain-machine interface paradigm to study the impact of paraplegia on BMI performance.

Authors:  Nathaniel R Bridges; Michael Meyers; Jonathan Garcia; Patricia A Shewokis; Karen A Moxon
Journal:  J Neurosci Methods       Date:  2018-05-31       Impact factor: 2.390

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

3.  Quantitative simulation of extracellular single unit recording from the surface of cortex.

Authors:  Mackenna Hill; Estefania Rios; Shyam Kumar Sudhakar; Douglas H Roossien; Ciara Caldwell; Dawen Cai; Omar J Ahmed; Scott F Lempka; Cynthia A Chestek
Journal:  J Neural Eng       Date:  2018-06-20       Impact factor: 5.379

4.  Learning in brain-computer interface control evidenced by joint decomposition of brain and behavior.

Authors:  Jennifer Stiso; Marie-Constance Corsi; Jean M Vettel; Javier Garcia; Fabio Pasqualetti; Fabrizio De Vico Fallani; Timothy H Lucas; Danielle S Bassett
Journal:  J Neural Eng       Date:  2020-07-24       Impact factor: 5.379

5.  Closed-loop intracranial stimulation alters movement timing in humans.

Authors:  Bartlett D Moore; Adam R Aron; Nitin Tandon
Journal:  Brain Stimul       Date:  2018-03-08       Impact factor: 8.955

Review 6.  Brain-machine interfaces from motor to mood.

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

7.  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

8.  Beta band oscillations in motor cortex reflect neural population signals that delay movement onset.

Authors:  Preeya Khanna; Jose M Carmena
Journal:  Elife       Date:  2017-05-03       Impact factor: 8.140

Review 9.  Cortical neuroprosthetics from a clinical perspective.

Authors:  Adelyn P Tsu; Mark J Burish; Jason GodLove; Karunesh Ganguly
Journal:  Neurobiol Dis       Date:  2015-08-05       Impact factor: 5.996

Review 10.  Brain-computer interfaces for dissecting cognitive processes underlying sensorimotor control.

Authors:  Matthew D Golub; Steven M Chase; Aaron P Batista; Byron M Yu
Journal:  Curr Opin Neurobiol       Date:  2016-01-19       Impact factor: 6.627

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