ReviewFrontiers in neuroscience2021
Challenges and Opportunities for the Future of Brain-Computer Interface in Neurorehabilitation.
Review in Frontiers in neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 2 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
26 citing papers in PubMed, 2 syntheses or guidelines pooled it, 56 citations in OpenAlex.
- Artificial Intelligence Approaches for EEG Signal Acquisition and Processing in Lower-Limb Motor Imagery: A Systematic Review.Sensors (Basel, Switzerland) · 2025Pooled it
- The effect of brain-computer interface controlled functional electrical stimulation training on rehabilitation of upper limb after stroke: a systematic review and meta-analysis.Frontiers in human neuroscience · 2024Pooled it
- Bidirectional EEG Neurofeedback of Sensorimotor Beta Oscillations Reveals Asymmetric Modulation and Strategy-Dependent Learning.The European journal of neuroscience · 2026Article
- Bridging cognition and control through passive eye movement integration in motor imagery brain-computer interfaces.Frontiers in human neuroscience · 2026Review
- Recommendations for Combining Brain-Computer Interface, Motor Imagery, and Virtual Reality in Upper Limb Stroke Rehabilitation: Qualitative Participatory Design Study.JMIR rehabilitation and assistive technologies · 2025Article
- Review
- TMS-Based Neurofeedback Training of Mental Finger Individuation Induces Neuroplastic Changes in the Sensorimotor System.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- A comprehensive review of rehabilitation approaches for traumatic brain injury: efficacy and outcomes.Frontiers in neurology · 2025Review
- Electroencephalography-Based Brain-Computer Interfaces in Rehabilitation: A Bibliometric Analysis (2013-2023).Sensors (Basel, Switzerland) · 2024Review
- A multiple session dataset of NIRS recordings from stroke patients controlling brain-computer interface.Scientific data · 2024Article
- Physical Therapy in Neurorehabilitation with an Emphasis on Sports: A Bibliometric Analysis and Narrative Review.Sports (Basel, Switzerland) · 2024Review
- Microengineered neuronal networks: enhancing brain-machine interfaces.Annals of medicine and surgery (2012) · 2024Review
- On the role of generative artificial intelligence in the development of brain-computer interfaces.BMC biomedical engineering · 2024Review
- Application of emerging technologies in ischemic stroke: from clinical study to basic research.Frontiers in neurology · 2024Review
- Editorial: Methods and protocols in Brain-Computer Interfaces.Frontiers in human neuroscience · 2024Article
- Non-Invasive Systems Application in Traumatic Brain Injury Rehabilitation.Brain sciences · 2023Review
- Home-Based Rehabilitation of the Shoulder Using Auxiliary Systems and Artificial Intelligence: An Overview.Sensors (Basel, Switzerland) · 2023Review
- Continuous versus discrete robotic feedback for brain-computer interfaces aimed for neurorehabilitation.Frontiers in neurorobotics · 2023Article
- Brain-Computer Interfaces for Upper Limb Motor Recovery after Stroke: Current Status and Development Prospects (Review).Sovremennye tekhnologii v meditsine · 2023Review
- Editorial: Brain imaging relations through simultaneous recordings.Frontiers in neuroscience · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 4 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Brain-computer interfaces (BCIs) provide a unique technological solution to circumvent the damaged motor system. For neurorehabilitation, the BCI can be used to translate neural signals associated with movement intentions into tangible feedback for the patient, when they are unable to generate functional movement themselves. Clinical interest in BCI is growing rapidly, as it would facilitate rehabilitation to commence earlier following brain damage and provides options for patients who are unable to partake in traditional physical therapy. However, substantial challenges with existing BCI implementations have prevented its widespread adoption. Recent advances in knowledge and technology provide opportunities to facilitate a change, provided that researchers and clinicians using BCI agree on standardisation of guidelines for protocols and shared efforts to uncover mechanisms. We propose that addressing the speed and effectiveness of learning BCI control are priorities for the field, which may be improved by multimodal or multi-stage approaches harnessing more sensitive neuroimaging technologies in the early learning stages, before transitioning to more practical, mobile implementations. Clarification of the neural mechanisms that give rise to improvement in motor function is an essential next step towards justifying clinical use of BCI. In particular, quantifying the unknown contribution of non-motor mechanisms to motor recovery calls for more stringent control conditions in experimental work. Here we provide a contemporary viewpoint on the factors impeding the scalability of BCI. Further, we provide a future outlook for optimal design of the technology to best exploit its unique potential, and best practices for research and reporting of findings.
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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.