ArticleFrontiers in neuroscience2022
Multimodal Neural Response and Effect Assessment During a BCI-Based Neurofeedback Training After Stroke.
Article in Frontiers in neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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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.
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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
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 24 citations in OpenAlex.
- A systematic review of functional near-infrared spectroscopy-based task paradigms in stroke rehabilitation.Frontiers in human neuroscience · 2025Pooled it
- Effect of electroencephalography-based motor imagery neurofeedback on mu suppression during motor attempt in patients with stroke.Journal of neuroengineering and rehabilitation · 2025Trial
- Functional near-infrared spectroscopy: Systematic mapping of abnormal brain function features in neurological disorders.Neural regeneration research · 2026Article
- Technical system of electroencephalography-based brain-computer interface: Advances, applications, and challenges.Neural regeneration research · 2026Article
- Transforming Intracerebral Hemorrhage Care with Artificial Intelligence: Opportunities, Challenges, and Future Directions.Diagnostics (Basel, Switzerland) · 2026Review
- Current status and future prospects of brain-computer interfaces in the field of neurological disease rehabilitation.Frontiers in rehabilitation sciences · 2026Review
- Effect of Brain Computer Interface Training on Frontoparietal Network Function for Young People: A Functional Near-Infrared Spectroscopy Study.CNS neuroscience & therapeutics · 2025Article
- The Feasibility and Test-Retest Reliability of Wireless Dry-Electrode EEG During a Dynamic Psychomotor Virtual Reality Task.Brain and behavior · 2025Article
- Advancing Neuroscience and Therapy: Insights into Genetic and Non-Genetic Neuromodulation Approaches.Cells · 2025Review
- A predictive model for multimodal rehabilitation efficacy in post-stroke patients with lower limb motor impairment.Frontiers in neurology · 2025Article
- Current implications of EEG and fNIRS as functional neuroimaging techniques for motor recovery after stroke.Medical review (2021) · 2024Review
- 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 review of functional electrical stimulation based on brain-computer interface].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024Review
- A Narrative Review on Multi-Domain Instrumental Approaches to Evaluate Neuromotor Function in Rehabilitation.Healthcare (Basel, Switzerland) · 2023Review
- Decoding the EEG patterns induced by sequential finger movement for brain-computer interfaces.Frontiers in neuroscience · 2023Article
- Aphasia improvement without logotherapy during motor neurorehabilitation of post-stroke hemiparesis using virtual reality or modified constraint-induced movement therapy: A retrospective cohort.NeuroRehabilitation · 2023Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Stroke caused by cerebral infarction or hemorrhage can lead to motor dysfunction. The recovery of motor function is vital for patients with stroke in daily activities. Traditional rehabilitation of stroke generally depends on physical practice under passive affected limbs movement. Motor imagery-based brain computer interface (MI-BCI) combined with functional electrical stimulation (FES) is a potential active neural rehabilitation technology for patients with stroke recently, which complements traditional passive rehabilitation methods. As the predecessor of BCI technology, neurofeedback training (NFT) is a psychological process that feeds back neural activities online to users for self-regulation. In this work, BCI-based NFT were proposed to promote the active repair and reconstruction of the whole nerve conduction pathway and motor function. We designed and implemented a multimodal, training type motor NFT system (BCI-NFT-FES) by integrating the visual, auditory, and tactile multisensory pathway feedback mode and using the joint detection of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). The results indicated that after 4 weeks of training, the clinical scale score, event-related desynchronization (ERD) of EEG patterns, and cerebral oxygen response of patients with stroke were enhanced obviously. This study preliminarily verified the clinical effectiveness of the long-term NFT system and the prospect of motor function rehabilitation.
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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.