Literature DB >> 22254555

Monkey models for brain-machine interfaces: the need for maintaining diversity.

Paul Nuyujukian1, Joline M Fan, Vikash Gilja, Paul S Kalanithi, Cindy A Chestek, Krishna V Shenoy.   

Abstract

Brain-machine interfaces (BMIs) aim to help disabled patients by translating neural signals from the brain into control signals for guiding prosthetic arms, computer cursors, and other assistive devices. Animal models are central to the development of these systems and have helped enable the successful translation of the first generation of BMIs. As we move toward next-generation systems, we face the question of which animal models will aid broader patient populations and achieve even higher performance, robustness, and functionality. We review here four general types of rhesus monkey models employed in BMI research, and describe two additional, complementary models. Given the physiological diversity of neurological injury and disease, we suggest a need to maintain the current diversity of animal models and to explore additional alternatives, as each mimic different aspects of injury or disease.

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Year:  2011        PMID: 22254555     DOI: 10.1109/IEMBS.2011.6090306

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  15 in total

1.  A high performing brain-machine interface driven by low-frequency local field potentials alone and together with spikes.

Authors:  Sergey D Stavisky; Jonathan C Kao; Paul Nuyujukian; Stephen I Ryu; Krishna V Shenoy
Journal:  J Neural Eng       Date:  2015-05-06       Impact factor: 5.379

2.  A Non-Human Primate Brain-Computer Typing Interface.

Authors:  Paul Nuyujukian; Jonathan C Kao; Stephen I Ryu; Krishna V Shenoy
Journal:  Proc IEEE Inst Electr Electron Eng       Date:  2016-09-12       Impact factor: 10.961

Review 3.  Neuroplasticity subserving the operation of brain-machine interfaces.

Authors:  Karim G Oweiss; Islam S Badreldin
Journal:  Neurobiol Dis       Date:  2015-05-09       Impact factor: 5.996

4.  Brain-computer interface control along instructed paths.

Authors:  P T Sadtler; S I Ryu; E C Tyler-Kabara; B M Yu; A P Batista
Journal:  J Neural Eng       Date:  2015-01-21       Impact factor: 5.379

Review 5.  The science and engineering behind sensitized brain-controlled bionic hands.

Authors:  Chethan Pandarinath; Sliman J Bensmaia
Journal:  Physiol Rev       Date:  2021-09-20       Impact factor: 37.312

6.  Intention estimation in brain-machine interfaces.

Authors:  Joline M Fan; Paul Nuyujukian; Jonathan C Kao; Cynthia A Chestek; Stephen I Ryu; Krishna V Shenoy
Journal:  J Neural Eng       Date:  2014-02       Impact factor: 5.379

Review 7.  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

8.  A high-performance keyboard neural prosthesis enabled by task optimization.

Authors:  Paul Nuyujukian; Joline M Fan; Jonathan C Kao; Stephen I Ryu; Krishna V Shenoy
Journal:  IEEE Trans Biomed Eng       Date:  2014-09-04       Impact factor: 4.538

9.  Augmenting intracortical brain-machine interface with neurally driven error detectors.

Authors:  Nir Even-Chen; Sergey D Stavisky; Jonathan C Kao; Stephen I Ryu; Krishna V Shenoy
Journal:  J Neural Eng       Date:  2017-12       Impact factor: 5.379

10.  Advances in neuroprosthetic learning and control.

Authors:  Jose M Carmena
Journal:  PLoS Biol       Date:  2013-05-21       Impact factor: 8.029

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