Literature DB >> 21159949

Incorporating feedback from multiple sensory modalities enhances brain-machine interface control.

Aaron J Suminski1, Dennis C Tkach, Andrew H Fagg, Nicholas G Hatsopoulos.   

Abstract

The brain typically uses a rich supply of feedback from multiple sensory modalities to control movement in healthy individuals. In many individuals, these afferent pathways, as well as their efferent counterparts, are compromised by disease or injury resulting in significant impairments and reduced quality of life. Brain-machine interfaces (BMIs) offer the promise of recovered functionality to these individuals by allowing them to control a device using their thoughts. Most current BMI implementations use visual feedback for closed-loop control; however, it has been suggested that the inclusion of additional feedback modalities may lead to improvements in control. We demonstrate for the first time that kinesthetic feedback can be used together with vision to significantly improve control of a cursor driven by neural activity of the primary motor cortex (MI). Using an exoskeletal robot, the monkey's arm was moved to passively follow a cortically controlled visual cursor, thereby providing the monkey with kinesthetic information about the motion of the cursor. When visual and proprioceptive feedback were congruent, both the time to successfully reach a target decreased and the cursor paths became straighter, compared with incongruent feedback conditions. This enhanced performance was accompanied by a significant increase in the amount of movement-related information contained in the spiking activity of neurons in MI. These findings suggest that BMI control can be significantly improved in paralyzed patients with residual kinesthetic sense and provide the groundwork for augmenting cortically controlled BMIs with multiple forms of natural or surrogate sensory feedback.

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Year:  2010        PMID: 21159949      PMCID: PMC3046069          DOI: 10.1523/JNEUROSCI.3967-10.2010

Source DB:  PubMed          Journal:  J Neurosci        ISSN: 0270-6474            Impact factor:   6.167


  43 in total

1.  Chronic, multisite, multielectrode recordings in macaque monkeys.

Authors:  Miguel A L Nicolelis; Dragan Dimitrov; Jose M Carmena; Roy Crist; Gary Lehew; Jerald D Kralik; Steven P Wise
Journal:  Proc Natl Acad Sci U S A       Date:  2003-09-05       Impact factor: 11.205

2.  Relationship of intrinsic connections to forelimb movement representations in monkey motor cortex: a correlative anatomic and physiological study.

Authors:  G W Huntley; E G Jones
Journal:  J Neurophysiol       Date:  1991-08       Impact factor: 2.714

3.  Congruent activity during action and action observation in motor cortex.

Authors:  Dennis Tkach; Jacob Reimer; Nicholas G Hatsopoulos
Journal:  J Neurosci       Date:  2007-11-28       Impact factor: 6.167

4.  Control of limb dynamics in normal subjects and patients without proprioception.

Authors:  R L Sainburg; M F Ghilardi; H Poizner; C Ghez
Journal:  J Neurophysiol       Date:  1995-02       Impact factor: 2.714

5.  Sensory and motor responses of precentral cortex cells during comparable passive and active joint movements.

Authors:  E E Fetz; D V Finocchio; M A Baker; M J Soso
Journal:  J Neurophysiol       Date:  1980-04       Impact factor: 2.714

6.  Instant neural control of a movement signal.

Authors:  Mijail D Serruya; Nicholas G Hatsopoulos; Liam Paninski; Matthew R Fellows; John P Donoghue
Journal:  Nature       Date:  2002-03-14       Impact factor: 49.962

7.  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

8.  Neural control of computer cursor velocity by decoding motor cortical spiking activity in humans with tetraplegia.

Authors:  Sung-Phil Kim; John D Simeral; Leigh R Hochberg; John P Donoghue; Michael J Black
Journal:  J Neural Eng       Date:  2008-11-18       Impact factor: 5.379

9.  Exploiting multiple sensory modalities in brain-machine interfaces.

Authors:  Aaron J Suminski; Dennis C Tkach; Nicholas G Hatsopoulos
Journal:  Neural Netw       Date:  2009-05-22

10.  Emergence of a stable cortical map for neuroprosthetic control.

Authors:  Karunesh Ganguly; Jose M Carmena
Journal:  PLoS Biol       Date:  2009-07-21       Impact factor: 8.029

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  85 in total

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Authors:  Nicholas G Hatsopoulos; Aaron J Suminski
Journal:  Neuron       Date:  2011-11-03       Impact factor: 17.173

Review 2.  Optimal feedback control and the long-latency stretch response.

Authors:  J Andrew Pruszynski; Stephen H Scott
Journal:  Exp Brain Res       Date:  2012-02-28       Impact factor: 1.972

3.  Synthesizing complex movement fragment representations from motor cortical ensembles.

Authors:  Nicholas G Hatsopoulos; Yali Amit
Journal:  J Physiol Paris       Date:  2011-09-10

Review 4.  Implantable neurotechnologies: bidirectional neural interfaces--applications and VLSI circuit implementations.

Authors:  Elliot Greenwald; Matthew R Masters; Nitish V Thakor
Journal:  Med Biol Eng Comput       Date:  2016-01-11       Impact factor: 2.602

5.  Brain-state classification and a dual-state decoder dramatically improve the control of cursor movement through a brain-machine interface.

Authors:  Nicholas A Sachs; Ricardo Ruiz-Torres; Eric J Perreault; Lee E Miller
Journal:  J Neural Eng       Date:  2015-12-11       Impact factor: 5.379

6.  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

7.  The critical stability task: quantifying sensory-motor control during ongoing movement in nonhuman primates.

Authors:  Kristin M Quick; Jessica L Mischel; Patrick J Loughlin; Aaron P Batista
Journal:  J Neurophysiol       Date:  2018-06-27       Impact factor: 2.714

8.  A recurrent neural network for closed-loop intracortical brain-machine interface decoders.

Authors:  David Sussillo; Paul Nuyujukian; Joline M Fan; Jonathan C Kao; Sergey D Stavisky; Stephen Ryu; Krishna Shenoy
Journal:  J Neural Eng       Date:  2012-03-19       Impact factor: 5.379

9.  Improving brain-machine interface performance by decoding intended future movements.

Authors:  Francis R Willett; Aaron J Suminski; Andrew H Fagg; Nicholas G Hatsopoulos
Journal:  J Neural Eng       Date:  2013-02-21       Impact factor: 5.379

Review 10.  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

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