Summary
Background
Paralysis or amputation of an arm results in the loss of the ability to orient the hand and grasp, manipulate, and carry objects, functions that are essential for activities of daily living. Brain–machine interfaces could provide a solution to restoring many of these lost functions. We therefore tested whether an individual with tetraplegia could rapidly achieve neurological control of a high-performance prosthetic limb using this type of an interface.
Methods
We implanted two 96-channel intracortical microelectrodes in the motor cortex of a 52-year-old individual with tetraplegia. Brain–machine-interface training was done for 13 weeks with the goal of controlling an anthropomorphic prosthetic limb with seven degrees of freedom (three-dimensional translation, three-dimensional orientation, one-dimensional grasping). The participant's ability to control the prosthetic limb was assessed with clinical measures of upper limb function. This study is registered with ClinicalTrials.gov, NCT01364480.
Findings
The participant was able to move the prosthetic limb freely in the three-dimensional workspace on the second day of training. After 13 weeks, robust seven-dimensional movements were performed routinely. Mean success rate on target-based reaching tasks was 91·6% (SD 4·4) versus median chance level 6·2% (95% CI 2·0–15·3). Improvements were seen in completion time (decreased from a mean of 148 s [SD 60] to 112 s [6]) and path efficiency (increased from 0·30 [0·04] to 0·38 [0·02]). The participant was also able to use the prosthetic limb to do skilful and coordinated reach and grasp movements that resulted in clinically significant gains in tests of upper limb function. No adverse events were reported.
Interpretation
With continued development of neuroprosthetic limbs, individuals with long-term paralysis could recover the natural and intuitive command signals for hand placement, orientation, and reaching, allowing them to perform activities of daily living.
Funding
Defense Advanced Research Projects Agency, National Institutes of Health, Department of Veterans Affairs, and UPMC Rehabilitation Institute.
To read this article in full you will need to make a payment
Purchase one-time access:
Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online accessOne-time access price info
- For academic or personal research use, select 'Academic and Personal'
- For corporate R&D use, select 'Corporate R&D Professionals'
Access any 5 articles from the Lancet Family of journals
Subscribe:
Subscribe to The Lancet
Already a print subscriber? Claim online access
Already an online subscriber? Sign in
Register: Create an account
Institutional Access: Sign in to ScienceDirect
References
- 1.
Postural hand synergies for tool use.J Neurosci. 1998; 18: 10105-10115
- 2.
Psychophysical determination of coordinate representation of human arm orientation.Neuroscience. 1984; 13: 595-604
- 3.
Postural and synergic control for three-dimensional movements of reaching and grasping.J Neurophysiol. 1995; 74: 905-910
- 4.
Motor cortical representation of speed and direction during reaching.J Neurophysiol. 1999; 82: 2676-2692
- 5.
Cortical control of a prosthetic arm for self-feeding.Nature. 2008; 453: 1098-1101
- 6. Brain-computer interface control of an anthropomorphic robotic arm. Carnegie Mellon University, 2011 (PhD thesis)
- 7.
Direct control of paralysed muscles by cortical neurons.Nature. 2008; 456: 639-642
- 8.
Restoration of grasp following paralysis through brain-controlled stimulation of muscles.Nature. 2012; 485: 368-371
- 9.
Degenhart AD, Collinger JL, Tyler-Kabara EC, et al. Three-dimensional control using an electrocorticographic brain-machine interface by an individual with tetraplegia. Neural Interfaces Conference; Salt Lake City, UT, USA; June 18–20, 2012.
- 10.
Electroencephalographic (EEG) control of three-dimensional movement.J Neural Eng. 2010; 7: 036007
- 11.
Continuous three-dimensional control of a virtual helicopter using a motor imagery based brain-computer interface.PLoS One. 2011; 6: e26322
- 12.
Reach and grasp by people with tetraplegia using a neurally controlled robotic arm.Nature. 2012; 485: 372-375
- 13. Muscles: testing and function. Lippincott Williams and Wilkins, Philadelphia, PA1993
- 14.
Neuronal population coding of movement direction.Science. 1986; 233: 1416-1419
- 15.
Motor cortical representation of position and velocity during reaching.J Neurophysiol. 2007; 97: 4258-4270
- 16.
Generalized inverses, ridge regression, biased linear estimation, and nonlinear estimation.Technometrics. 1970; 12: 591-612
- 17.
A performance test for assessment of upper limb function in physical rehabilitation treatment and research.Int J Rehabil Res. 1981; 4: 483-492
- 18.
A standardized approach to performing the action research arm test.Neurorehab Neural Repair. 2008; 22: 78-90
- 19.
Direct cortical control of 3D neuroprosthetic devices.Science. 2002; 296: 1829-1832
- 20.
Functional network reorganization during learning in a brain-computer interface paradigm.Proc Natl Acad Sci USA. 2008; 105: 19486-19491
- 21.
Neural correlates of skill acquisition with a cortical brain-machine interface.J Mot Behav. 2010; 42: 355-360
- 22.
Reversible large-scale modification of cortical networks during neuroprosthetic control.Nat Neurosci. 2011; 14: 662-667
- 23.
Neural control of cursor trajectory and click by a human with tetraplegia 1000 days after implant of an intracortical microelectrode array.J Neural Eng. 2011; 8: 025027
- 24.
Real-time control of a prosthetic hand using human electrocorticography signals.J Neurosurg. 2011; 114: 1715-1722
- 25.
Evaluation of shoulder complex motion-based input strategies for endpoint prosthetic-limb control using dual-task paradigm.J Rehabil Res Dev. 2011; 48: 669-678
- 26.
Targeted muscle reinnervation for real-time myoelectric control of multifunction artificial arms.JAMA. 2009; 301: 619-628
- 27.
Intrafascicular thin-film multichannel electrodes for sensory feedback: Evidences on a human amputee.Conf Proc IEEE Eng Med Biol Soc. 2010; 2010: 1800-1803
- 28.
Direct neural sensory feedback and control of a prosthetic arm.IEEE Trans Neural Syst Rehabil Eng. 2005; 13: 468-472
- 29.
Collinger JL, Boninger ML, Bruns TM, Curley K, Wang W, Weber DJ. Functional priorities, assistive technology, and brain-computer interfaces after spinal cord injury. J Rehabil Res Dev (in press).
Article info
Publication history
Published: December 17, 2012
Identification
Copyright
© 2013 Elsevier Ltd. All rights reserved.
ScienceDirect
Access this article on ScienceDirectLinked Articles
- Brain–machine interface: closer to therapeutic reality?
-
Various neurological diseases and traumatic injuries permanently abolish sensorimotor functions, dramatically affecting the quality of life of millions of individuals. Progress continues in the development of neural repair interventions to enhance functional recovery after neuromotor disorders in animals.1,2 So far, no interventions have shown efficacy in the restoration of useful sensorimotor functions in severely paralysed people.
- Full-Text
-
- Neuroprosthetic control and tetraplegia – Authors'reply
- Neuroprosthetic control and tetraplegia
-
The neuroprosthetic achievements reported by Jennifer Collinger and colleagues (Feb 16, p 557)1 are remarkable. The diagnosis of the tetraplegic patient of the study is, however, puzzling. The patient has spinocerebellar ataxia without cerebellar features. Material available elsewhere2,3 suggests that her symptoms began rather suddenly 13 years before taking part in the study. She describes relapsing weakness, has normal looking hands, and, head rest excepted, no symptoms above the neck. This is unusual for spinocerebellar ataxia, which typically has slow onset with gradual deterioration.
- Full-Text
-