ReviewBrain sciences2022
Sensorimotor Rhythm-Based Brain-Computer Interfaces for Motor Tasks Used in Hand Upper Extremity Rehabilitation after Stroke: A Systematic Review.
Review in Brain sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it, 26 citations in OpenAlex.
- Efficacy and safety of brain-computer interface for stroke rehabilitation: an overview of systematic review.Frontiers in human neuroscience · 2025Pooled it
- Mapping the evolution of neurofeedback research: a bibliometric analysis of trends and future directions.Frontiers in human neuroscience · 2024Pooled it
- Mapping knowledge structure and emerging trends in non-invasive brain-computer interface for stroke rehabilitation.IBRO neuroscience reports · 2026Article
- Closed-loop brain-computer interfaces for post-stroke sensorimotor loop restoration.Frontiers in neuroscience · 2026Review
- Active Rehabilitation Technologies for Post-Stroke Patients.Biosensors · 2025Review
- 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
- Exploring Machine Learning Classification of Movement Phases in Hemiparetic Stroke Patients: A Controlled EEG-tDCS Study.Brain sciences · 2024Article
- A multiple session dataset of NIRS recordings from stroke patients controlling brain-computer interface.Scientific data · 2024Article
- Noninvasive brain stimulation during EEG improves machine learning classiResearch square · 2024Article
- The Role of Interoceptive Sensitivity and Hypnotizability in Motor Imagery.Brain sciences · 2024Article
- Editorial: Brain-computer interfaces in neurological disorders: expanding horizons for diagnosis, treatment, and rehabilitation.Frontiers in neuroscience · 2024Article
- Brain-Computer Interfaces with Intracortical Implants for Motor and Communication Functions Compensation: Review of Recent Developments.Sovremennye tekhnologii v meditsine · 2024Review
- Application of Foot Hallux Contact Force Signal for Assistive Hand Fine Control.Sensors (Basel, Switzerland) · 2023Article
- Upper Limb Function Recovery by Combined Repetitive Transcranial Magnetic Stimulation and Occupational Therapy in Patients with Chronic Stroke According to Paralysis Severity.Brain sciences · 2023Article
- Brain-Computer Interfaces for Upper Limb Motor Recovery after Stroke: Current Status and Development Prospects (Review).Sovremennye tekhnologii v meditsine · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Brain-computer interfaces (BCIs) are becoming more popular in the neurological rehabilitation field, and sensorimotor rhythm (SMR) is a type of brain oscillation rhythm that can be captured and analyzed in BCIs. Previous reviews have testified to the efficacy of the BCIs, but seldom have they discussed the motor task adopted in BCIs experiments in detail, as well as whether the feedback is suitable for them. We focused on the motor tasks adopted in SMR-based BCIs, as well as the corresponding feedback, and searched articles in PubMed, Embase, Cochrane library, Web of Science, and Scopus and found 442 articles. After a series of screenings, 15 randomized controlled studies were eligible for analysis. We found motor imagery (MI) or motor attempt (MA) are common experimental paradigms in EEG-based BCIs trials. Imagining/attempting to grasp and extend the fingers is the most common, and there were multi-joint movements, including wrist, elbow, and shoulder. There were various types of feedback in MI or MA tasks for hand grasping and extension. Proprioception was used more frequently in a variety of forms. Orthosis, robot, exoskeleton, and functional electrical stimulation can assist the paretic limb movement, and visual feedback can be used as primary feedback or combined forms. However, during the recovery process, there are many bottleneck problems for hand recovery, such as flaccid paralysis or opening the fingers. In practice, we should mainly focus on patients' difficulties, and design one or more motor tasks for patients, with the assistance of the robot, FES, or other combined feedback, to help them to complete a grasp, finger extension, thumb opposition, or other motion. Future research should focus on neurophysiological changes and functional improvements and further elaboration on the changes in neurophysiology during the recovery of motor function.
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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.