Functional Rehabilitation of the Paralyzed Upper Extremity After Spinal Cord Injury by Noninvasive Hybrid Neuroprostheses
Abstract
For individuals with high spinal cord injury (SCI), restoring missing grasping function is a high priority. Neuroprostheses based on functional electrical stimulation (FES) can partly compensate the loss of upper extremity function in people suffering from tetraplegia. With noninvasive, multichannel neuroprostheses a pinch and power grasp can be accomplished for everyday use. Hybrid systems combining FES with active orthoses hold promise for restoring a completely lost arm function. Novel control interfaces are needed to make full use of the many degrees of freedom of complex hybrid neuroprostheses. Motor imagery (MI)-based brain-computer interfaces (BCIs) are an emerging technology that may serve as a valuable adjunct to traditional control interfaces for neuroprosthetic control. Shared control and context-specific autonomy are most effective for reducing the users' workload. The modularity of upper extremity neuroprostheses as well as their associated control interfaces enable customization of the systems to adapt to the impairment and needs of each individual end user. This work provides an overview of the application of noninvasive hybrid BCI-controlled upper extremity neuroprostheses in individuals with high SCI with a strong focus on the results from the European Integrated Project Tools for Brain-Computer Interaction and will describe the challenges and promises for the future.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The high-yielding application performances come as a result of the pilots' ability to adequately master all four individual subtasks required by the application: intentional control for spin, jump, slide and intentional non-control for rest/idle.
“Fig 2 illustrates that the high-yielding application performances come as a result of our pilots' ability to adequately master all four individual subtasks required by the application: the intentional control (IC) ability to deliver the correct command on the action pads (spin, jump, slide) and the intentional non-control (INC) ability to “rest/idle” on the white pads [48–50].”
From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [49]
The citation lists haven’t been fetched yet
References 99 · Cited by 84
- Authors
- Rupp, R., Rohm, M., Schneiders, M., Kreilinger, A., & Muller-Putz, G. R.
- Affiliations
- Heidelberg University (DE)
- Graz University of Technology (AT)
- Venue
- Proceedings of the IEEE
- Published
- 2015-06
- Volume
- 103
- Issue
- 6
- Pages
- 954-968
- ISSN
- 0018-9219 (ISSN-L)
- 1558-2256
- OpenAlex
- W1694127584
- Cited by
- 84
- References
- 99
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 6 · 2023: 6 · 2022: 9 · 2021: 8 · 2020: 12 · 2019: 7 · 2018: 11 · 2017: 13 · 2016: 5 · 2015: 2
- FWCI
- 6.20
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Proceedings of the IEEE (publishedVersion)
- Funders
- European ICT Program Project “Tools for Brain-Computer Interaction-TOBI” (FP7-224631)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Engineering › Biomedical Engineering › Prosthetics and Rehabilitation Robotics
- Health Sciences › Medicine › Pathology and Forensic Medicine › Spinal Cord Injury Research
- Keywords
- spinal cord injury, upper limb paralysis, tetraplegia, functional electrical stimulation, active orthoses, brain-computer interface, motor imagery, shared control
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review