Ten-dimensional anthropomorphic arm control in a human brain−machine interface: difficulties, solutions, and limitations
Open accessAbstract
OBJECTIVE: In a previous study we demonstrated continuous translation, orientation and one-dimensional grasping control of a prosthetic limb (seven degrees of freedom) by a human subject with tetraplegia using a brain-machine interface (BMI). The current study, in the same subject, immediately followed the previous work and expanded the scope of the control signal by also extracting hand-shape commands from the two 96-channel intracortical electrode arrays implanted in the subject's left motor cortex. APPROACH: Four new control signals, dictating prosthetic hand shape, replaced the one-dimensional grasping in the previous study, allowing the subject to control the prosthetic limb with ten degrees of freedom (three-dimensional (3D) translation, 3D orientation, four-dimensional hand shaping) simultaneously. MAIN RESULTS: Robust neural tuning to hand shaping was found, leading to ten-dimensional (10D) performance well above chance levels in all tests. Neural unit preferred directions were broadly distributed through the 10D space, with the majority of units significantly tuned to all ten dimensions, instead of being restricted to isolated domains (e.g. translation, orientation or hand shape). The addition of hand shaping emphasized object-interaction behavior. A fundamental component of BMIs is the calibration used to associate neural activity to intended movement. We found that the presence of an object during calibration enhanced successful shaping of the prosthetic hand as it closed around the object during grasping. SIGNIFICANCE: Our results show that individual motor cortical neurons encode many parameters of movement, that object interaction is an important factor when extracting these signals, and that high-dimensional operation of prosthetic devices can be achieved with simple decoding algorithms. ClinicalTrials.gov Identifier: NCT01364480.
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Using the same fundamental neural decoding and control methods, two participants have achieved similar scores on functional tasks with vision alone.
“First, using the same fundamental neural decoding and control methods, two participants have achieved similar scores on functional tasks with vision alone (5, 6, 28).”
From A brain-computer interface that evokes tactile sensations improves robotic arm control (2021) · cites this paper as [5]
Section 3.4 reports similar functional-task scores under 7D and 10D control, but both scores are from the same participant. Section 2.2 identifies a single participant; this paper therefore does not establish the claim about two participants achieving similar scores with vision alone. Page numbers are unavailable in our copy. (LLM, conf=0.88)
Experimental — accuracy not yet measuredICMS can deliver localized tactile percepts with natural features and can be used in human-brain-computer interface applications for better control using area S1.
“ICMS can also be employed to deliver localized tactile percepts with natural features as well as in human-brain-computer interface applications for better control using area S1 (Collinger et al., 2013; Wodlinger et al., 2015; Downey et al., 2016).”
From Selectivity and Longevity of Peripheral-Nerve and Machine Interfaces: A Review. (2017) · cites this paper as Wodlinger et al., 2015
The Abstract reports brain–machine-interface control of a prosthetic arm, supporting the general application to human interface control. But §2.2 (Participant) places the recording arrays in motor cortex, not S1; the paper does not report ICMS, localized tactile percepts, or natural tactile features. Page numbers were not supplied for these passages. (LLM, conf=0.88)
Experimental — accuracy not yet measuredBy combining improvements in bionic arms and decoding algorithms, it is now possible for a human patient to perform tasks with a bionic arm by thoughts alone.
“By combining these innovations, it is now possible for a human patient to perform tasks with a bionic arm by thoughts alone (Collinger et al., 2013; Wodlinger et al., 2015).”
From Selectivity and Longevity of Peripheral-Nerve and Machine Interfaces: A Review. (2017) · cites this paper as Wodlinger et al., 2015
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- Authors
- Wodlinger, B., Downey, J. E., Tyler-Kabara, E. C., Schwartz, A. B., Boninger, M. L., & Collinger, J. L.
- Affiliations
- University of Pittsburgh (US)
- Venue
- Journal of Neural Engineering
- Publisher
- IOP Publishing
- Published
- 2014-12-16
- Volume
- 12
- Issue
- 1
- Pages
- 016011
- ISSN
- 1741-2552 (ISSN-L)
- 1741-2560
- OpenAlex
- W2033144847
- Cited by
- 536
- References
- 36
- Citations by year
- 2026: 27 · 2025: 52 · 2024: 45 · 2023: 57 · 2022: 45 · 2021: 57 · 2020: 50 · 2019: 45 · 2018: 41 · 2017: 51 · 2016: 45 · 2015: 20 · 2013: 1
- FWCI
- 21.52
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of Neural Engineering (publishedVersion)
- PubMed (publishedVersion)
- Funders
- U.S. Department of Veterans Affairs (B6789C, B7143R, and RX720)
- Defense Advanced Research Projects Agency (contract number N66001-10-C-4056)
- UPMC Rehabilitation Institute
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Keywords
- brain-computer interface, intracortical electrodes, motor cortex, prosthetic hand control, grasping, tetraplegia, neural tuning, object interaction, calibration
- MeSH terms
- Adult, Arm, Artificial Limbs, Computer Simulation, Electroencephalography, Female, Humans, Imagination, Joints, Models, Biological, Prosthesis Design, Quadriplegia, Robotics, Evoked Potentials, Motor, Equipment Failure Analysis, Feedback, Physiological, Brain-Computer Interfaces
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