ReviewIEEE transactions on bio-medical engineering2014
Brain-computer interfaces using sensorimotor rhythms: current state and future perspectives.
Review in IEEE transactions on bio-medical engineering, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05345301 (Electroencephalographic Correlates of Glove-stretch Therapy Post-stroke), which is not on this map. Cited by 127 papers.
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.
Electroencephalographic Correlates of Glove-stretch Therapy Post-stroke
Who cites it
127 citing papers in PubMed.
- Efficacy of kinesthetic motor imagery based brain computer interface combined with tDCS on upper limb function in subacute stroke.Scientific reports · 2025Trial
- Exploiting the heightened phase synchrony in patients with neuromuscular disease for the establishment of efficient motor imagery BCIs.Journal of neuroengineering and rehabilitation · 2018Trial
- Steady state visual evoked potential (SSVEP) based brain-computer interface (BCI) performance under different perturbations.PloS one · 2018Trial
- Highly stretchable, hair-compatible multichannel neural interface with time-stable hydrogel for high-fidelity long-term EEG sensing.Science advances · 2026Article
- Technical system of electroencephalography-based brain-computer interface: Advances, applications, and challenges.Neural regeneration research · 2026Article
- Sensory-guided human-machine joint learning accelerates the acquisition of motor imagery brain computer interface control.Nature communications · 2026Article
- An EEG-EMG-kinematics dataset from wrist pointing tasks for biomarker research in neurorehabilitation.Scientific data · 2026Article
- Electroencephalography Enables Continuous Decoding of Hand Motion Angles in Polar Coordinates.Cyborg and bionic systems (Washington, D.C.) · 2026Article
- Review
- Enhancing classification of a large lower-limb motor imagery EEG dataset for BCI in knee pain patients.Scientific data · 2025Article
- Investigating the neurovascular coupling across multiple motor execution and imagery conditions: a whole-brain EEG-informed fMRI analysis.NeuroImage · 2025Article
- Motor activity in gamma and high gamma bands recorded with a Stentrode from the human motor cortex in two people with ALS.Journal of neural engineering · 2025Article
- Filter bank temporally delayed CCA for uncalibrated SSVEP-BCI.Medical & biological engineering & computing · 2025Article
- Investigating mu and alpha oscillations as indicators of intra-individual success and inter-individual ability in motor imagery performance.Frontiers in psychology · 2025Article
- Non-Invasive Brain-Computer Interfaces: State of the Art and Trends.IEEE reviews in biomedical engineering · 2025Review
- Continuous Reaching and Grasping With a BCI Controlled Robotic Arm in Healthy and Stroke-Affected Individuals.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2025Article
- MACNet: A Multidimensional Attention-Based Convolutional Neural Network for Lower-Limb Motor Imagery Classification.Sensors (Basel, Switzerland) · 2024Article
- A continuous pursuit dataset for online deep learning-based EEG brain-computer interface.Scientific data · 2024Article
- EEG-based sensorimotor neurofeedback for motor neurorehabilitation in children and adults: A scoping review.Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology · 2024Article
- A multistrategy differential evolution algorithm combined with Latin hypercube sampling applied to a brain-computer interface to improve the effect of node displacement.Scientific reports · 2024Article
67 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Many studies over the past two decades have shown that people can use brain signals to convey their intent to a computer using brain-computer interfaces (BCIs). BCI systems extract specific features of brain activity and translate them into control signals that drive an output. Recently, a category of BCIs that are built on the rhythmic activity recorded over the sensorimotor cortex, i.e., the sensorimotor rhythm (SMR), has attracted considerable attention among the BCIs that use noninvasive neural recordings, e.g., electroencephalography (EEG), and have demonstrated the capability of multidimensional prosthesis control. This paper reviews the current state and future perspectives of SMR-based BCI and its clinical applications, in particular focusing on the EEG SMR. The characteristic features of SMR from the human brain are described and their underlying neural sources are discussed. The functional components of SMR-based BCI, together with its current clinical applications, are reviewed. Finally, limitations of SMR-BCIs and future outlooks are also discussed.
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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.