ArticleFrontiers in human neuroscience2022
EEG-Based Brain Network Analysis of Chronic Stroke Patients After BCI Rehabilitation Training.
Article in Frontiers in human neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 4 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
21 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Comparative Effectiveness of Noninvasive Brain-Computer Interface-Based Interventions for Upper Limb Rehabilitation in Poststroke Hemiplegia: Systematic Review and Network Meta-Analysis of Randomized Controlled Trials.Journal of medical Internet research · 2026Pooled it
- Biomarkers of stroke recovery using EEG-based resting-state functional connectivity: a systematic review.Journal of neuroengineering and rehabilitation · 2026Pooled it
- Brain-computer interface for post-stroke upper-limb recovery: a systematic review integrating clinical efficacy with neuroplasticity evidence.Frontiers in neurology · 2026Pooled 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
- Efficacy of brain-computer interface training with motor imagery-contingent feedback in improving upper limb function and neuroplasticity among persons with chronic stroke: a double-blinded, parallel-group, randomized controlled trial.Journal of neuroengineering and rehabilitation · 2025Trial
- Effects of visually induced motor imagery-based brain-computer interface training on motor function in patients with incomplete spinal cord injury: a small-sample exploratory trial.Frontiers in neurologyTrial
- EEG based multifunctional connectivity fusion across frequency bands and parameters promote motor function assessment in stroke: a pilot study.Journal of neuroengineering and rehabilitation · 2026Article
- Rehab-DRLX: explainable neurorehabilitation prognosis using deep reinforcement learning and transformer-based models.Frontiers in computational neuroscience · 2026Article
- Sensorimotor Integration by Targeted Priming in Muscles with Electromyography-Driven Electro-vibro-feedback in Robot-Assisted Wrist/Hand Rehabilitation after Stroke.Cyborg and bionic systems (Washington, D.C.) · 2026Article
- EEG-based stroke severity classification using higher-order topological features and graph convolutional networks.Frontiers in neuroscience · 2026Article
- Source-Level Resting-State EEG Connectivity Reveals Frequency-Specific Neural Reorganization and Predicts Motor Recovery in Individuals Post Stroke Following Gait Rehabilitation.Research square · 2025Article
- Toward Precision Post-Stroke Rehabilitation Medicine: Integrating Molecular, Imaging, and Computational Biomarkers for Functional Outcome Prediction.Journal of clinical medicine · 2025Review
- Classification of Different Motor Imagery Tasks with the Same Limb Using Electroencephalographic Signals.Sensors (Basel, Switzerland) · 2025Article
- Long-Term Brain-Computer Interface Functional Electrical Stimulation Enhances Neuroplasticity and Functional Recovery in Elderly Stroke: A 4.5-Year Longitudinal Study Integrating Electroencephalography Biomarkers and Clinical Assessments.Research (Washington, D.C.) · 2025Article
- Exploring Machine Learning Classification of Movement Phases in Hemiparetic Stroke Patients: A Controlled EEG-tDCS Study.Brain sciences · 2024Article
- Time-frequency-space transformer EEG decoding for spinal cord injury.Cognitive neurodynamics · 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
- The role of triglyceride-glucose index in the differential diagnosis of atherosclerotic stroke and cardiogenic stroke.BMC cardiovascular disorders · 2024Article
- Exploring the Role of Visual Guidance in Motor Imagery-Based Brain-Computer Interface: An EEG Microstate-Specific Functional Connectivity Study.Bioengineering (Basel, Switzerland) · 2023Article
- Motor imagery brain-computer interface rehabilitation system enhances upper limb performance and improves brain activity in stroke patients: A clinical study.Frontiers in human neuroscience · 2023Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Traditional rehabilitation strategies become difficult in the chronic phase stage of stroke prognosis. Brain-computer interface (BCI) combined with external devices may improve motor function in chronic stroke patients, but it lacks comprehensive assessments of neurological changes regarding functional rehabilitation. This study aimed to comprehensively and quantitatively investigate the changes in brain activity induced by BCI-FES training in patients with chronic stroke. We analyzed the EEG of two groups of patients with chronic stroke, one group received functional electrical stimulation (FES) rehabilitation training (FES group) and the other group received BCI combined with FES training (BCI-FES group). We constructed functional networks in both groups of patients based on direct directed transfer function (dDTF) and assessed the changes in brain activity using graph theory analysis. The results of this study can be summarized as follows: (i) after rehabilitation training, the Fugl-Meyer assessment scale (FMA) score was significantly improved in the BCI-FES group (
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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.