ReviewNature reviews. Neurology2016
Brain-computer interfaces for communication and rehabilitation.
Review in Nature reviews. Neurology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. It is linked to trial NCT04704427 (Rehabilitative BCI for Lower Limb Function Recovery in Acute Ischemic Stroke), which is not on this map. Cited by 235 papers, 8 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Rehabilitative BCI for Lower Limb Function Recovery in Acute Ischemic Stroke
Who cites it
235 citing papers in PubMed, 8 syntheses or guidelines pooled it, 888 citations in OpenAlex.
- Brain-computer interfaces and neural synchronization in esports: a systematic review of effects on reaction time, decision-making, and cognitive performance.Frontiers in human neuroscience · 2026Pooled it
- Effects of brain-computer interface based training on post-stroke upper-limb rehabilitation: a meta-analysis.Journal of neuroengineering and rehabilitation · 2025Pooled it
- Efficacy and safety of brain-computer interface for stroke rehabilitation: an overview of systematic review.Frontiers in human neuroscience · 2025Pooled it
- Knowledge mapping and research trends of brain-computer interface technology in rehabilitation: a bibliometric analysis.Frontiers in human neuroscience · 2024Pooled it
- Past, Present, and Future of EEG-Based BCI Applications.Sensors (Basel, Switzerland) · 2022Pooled it
- A systematic review of research on augmentative and alternative communication brain-computer interface systems for individuals with disabilities.Frontiers in human neuroscience · 2022Pooled it
- Efficacy of Brain-Computer Interface and the Impact of Its Design Characteristics on Poststroke Upper-limb Rehabilitation: A Systematic Review and Meta-analysis of Randomized Controlled Trials.Clinical EEG and neuroscience · 2022Pooled it
- Brain-Computer Interfaces Systems for Upper and Lower Limb Rehabilitation: A Systematic Review.Sensors (Basel, Switzerland) · 2021Pooled it
- Effects of a Brain-Computer Interface-Operated Lower Limb Rehabilitation Robot on Motor Function Recovery in Patients with Stroke.Journal of healthcare engineering · 2021Trial
- Volitional control of single-electrode high gamma local field potentials by people with paralysis.Journal of neurophysiology · 2019Trial
- From Signal Stacking to Dynamic Coupling: A Critical Review of Wearable EEG-EMG Fusion Brain-Computer Interfaces for Stroke Rehabilitation.Micromachines · 2026Review
- Towards neuromorphic neurotechnologies: integrating brain-inspired computing with brain-computer interfaces.npj biomedical innovations · 2026Review
- Motor Imagery Brain-Computer Interface (MI-BCI)-Assisted Upper Limb Neurorehabilitation for Acute Stroke During Inpatient Rehabilitation: A Prospective Feasibility Study with Economic Evaluation Protocol.Journal of clinical medicine · 2026Article
- Sensory-guided human-machine joint learning accelerates the acquisition of motor imagery brain computer interface control.Nature communications · 2026Article
- Consent in flux: when brain‑computer interface embodiment undermines informed consent.BMC medical ethics · 2026Article
- ZMW-RSVP: a time-frequency prior-guided normalization-free RSVP-BCI decoding model.Scientific reports · 2026Article
- Effects of Brain-Computer Interface-Controlled Hand Robot Training on Post-Stroke Recovery of Upper Limb Motor Functions: A Meta-Analysis of Dose-Matched Randomized Controlled Trials.Brain sciences · 2026Review
- Global Trends in Research of Brain-Computer Interfaces in Neuroscience From 2014 to 2023: A Bibliometric Analysis.CNS neuroscience & therapeutics · 2026Review
- Review
- Two-dimensional semiconductor-based active array for high-fidelity spatiotemporal monitoring of neural activities.Nature materials · 2026Article
175 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Brain-computer interfaces (BCIs) use brain activity to control external devices, thereby enabling severely disabled patients to interact with the environment. A variety of invasive and noninvasive techniques for controlling BCIs have been explored, most notably EEG, and more recently, near-infrared spectroscopy. Assistive BCIs are designed to enable paralyzed patients to communicate or control external robotic devices, such as prosthetics; rehabilitative BCIs are designed to facilitate recovery of neural function. In this Review, we provide an overview of the development of BCIs and the current technology available before discussing experimental and clinical studies of BCIs. We first consider the use of BCIs for communication in patients who are paralyzed, particularly those with locked-in syndrome or complete locked-in syndrome as a result of amyotrophic lateral sclerosis. We then discuss the use of BCIs for motor rehabilitation after severe stroke and spinal cord injury. We also describe the possible neurophysiological and learning mechanisms that underlie the clinical efficacy of BCIs.
Indexed as
Identifiers
What OpenQuestion holds
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.