Literature DB >> 24120558

Transfer of information by BMI.

E J Tehovnik1, L C Woods, W M Slocum.   

Abstract

Brain machine interfaces (BMI) have become important in systems neuroscience with the goal to restore motor function in paralyzed patients. We assess the current ability of BMI devices to move objects. The topics discussed include: (1) the bits of information generated by a BMI signal, (2) the limitations of including more neurons for generating a BMI signal, (3) the superiority of a BMI signal using single cells versus electroencephalography, (4) plasticity and BMI, (5) the selection of a neural code for generating BMI, (6) the suppression of body movements during BMI, and (7) the role of vision in BMI. We conclude that further research on understanding how the brain generates movement is necessary before BMI can become a reasonable option for paralyzed patients.
Copyright © 2013 IBRO. Published by Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  BMI; EEG; bit rate; brain machine interfaces; electroencephalography; monkeys; quadriplegics; skeleto-motor; vision

Mesh:

Year:  2013        PMID: 24120558     DOI: 10.1016/j.neuroscience.2013.10.003

Source DB:  PubMed          Journal:  Neuroscience        ISSN: 0306-4522            Impact factor:   3.590


  14 in total

Review 1.  Brain control and information transfer.

Authors:  Edward J Tehovnik; Lewis L Chen
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2.  Modulation of neural activity by reward in medial intraparietal cortex is sensitive to temporal sequence of reward.

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4.  Identifying Engineering, Clinical and Patient's Metrics for Evaluating and Quantifying Performance of Brain-Machine Interface (BMI) Systems.

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6.  How to read neuron-dropping curves?

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7.  An optical brain-to-brain interface supports rapid information transmission for precise locomotion control.

Authors:  Lihui Lu; Ruiyu Wang; Minmin Luo
Journal:  Sci China Life Sci       Date:  2020-03-20       Impact factor: 6.038

8.  Advancing brain-machine interfaces: moving beyond linear state space models.

Authors:  Adam G Rouse; Marc H Schieber
Journal:  Front Syst Neurosci       Date:  2015-07-28

Review 9.  Decoding methods for neural prostheses: where have we reached?

Authors:  Zheng Li
Journal:  Front Syst Neurosci       Date:  2014-07-16

10.  Real-time decoding of covert attention in higher-order visual areas.

Authors:  Jinendra Ekanayake; Chloe Hutton; Gerard Ridgway; Frank Scharnowski; Nikolaus Weiskopf; Geraint Rees
Journal:  Neuroimage       Date:  2017-12-14       Impact factor: 6.556

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