ReviewIEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society2017
BCI Use and Its Relation to Adaptation in Cortical Networks.
Review in IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 3 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Efficacy of Brain-Computer Interface Therapy for Upper Limb Rehabilitation in Chronic Stroke: Systematic Review and Meta-Analysis of Randomized Controlled Trials.Journal of medical Internet research · 2026Pooled it
- Influence of age, cognitive function, attention, and mental state on the effectiveness of EEG-based brain-computer interface device use: a systematic review.Journal of neuroengineering and rehabilitation · 2025Pooled it
- Effects of brain-computer interface based training on post-stroke upper-limb rehabilitation: a meta-analysis.Journal of neuroengineering and rehabilitation · 2025Pooled it
- Mapping knowledge structure and emerging trends in non-invasive brain-computer interface for stroke rehabilitation.IBRO neuroscience reports · 2026Article
- An fMRI study on the generalization of motor learning after brain actuated supernumerary robot training.NPJ science of learning · 2024Article
- Distinct neural representations during a brain-machine interface and manual reaching task in motor cortex, prefrontal cortex, and striatum.Scientific reports · 2023Article
- Riemannian geometry-based metrics to measure and reinforce user performance changes during brain-computer interface user training.Frontiers in computational neuroscience · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Brain-computer interfaces (BCIs) carry great potential in the treatment of motor impairments. As a new motor output, BCIs interface with the native motor system, but acquisition of BCI proficiency requires a degree of learning to integrate this new function. In this review, we discuss how BCI designs often take advantage of the brain's motor system infrastructure as sources of command signals. We highlight a growing body of literature examining how this approach leads to changes in activity across cortex, including beyond motor regions, as a result of learning the new skill of BCI control. We discuss the previous research identifying patterns of neural activity associated with BCI skill acquisition and use that closely resembles those associated with learning traditional native motor tasks. We then discuss recent work in animals probing changes in connectivity of the BCI control site, which were linked to BCI skill acquisition, and use this as a foundation for our original work in humans. We present our novel work showing changes in resting state connectivity across cortex following the BCI learning process. We find substantial, heterogeneous changes in connectivity across regions and frequencies, including interactions that do not involve the BCI control site. We conclude from our review and original work that BCI skill acquisition may potentially lead to significant changes in evoked and resting state connectivity across multiple cortical regions. We recommend that future studies of BCIs look beyond motor regions to fully describe the cortical networks involved and long-term adaptations resulting from BCI skill acquisition.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.