ArticleJournal of neuroengineering and rehabilitation2023
Exploring high-density corticomuscular networks after stroke to enable a hybrid Brain-Computer Interface for hand motor rehabilitation.
Article in Journal of neuroengineering and rehabilitation, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 27 citations in OpenAlex.
- From Signal Stacking to Dynamic Coupling: A Critical Review of Wearable EEG-EMG Fusion Brain-Computer Interfaces for Stroke Rehabilitation.Micromachines · 2026Review
- Dynamics of brain-muscle interaction with neuromuscular fatigability: systematic review.Frontiers in physiology · 2026Review
- Corticomuscular coupling alterations in subacute stroke patients: insights from fNIRS and sEMG.BMC neurology · 2025Article
- Corticomuscular coupling study for post-stroke rehabilitation: a scoping review.Journal of neuroengineering and rehabilitation · 2025Article
- Brain-hemispheric differences in the premotor area for motor planning: An approach based on corticomuscular connectivity during motor decision-making.NeuroImage · 2025Article
- Evolution Trend of Brain Science Research: An Integrated Bibliometric and Mapping Approach.Brain and behavior · 2025Review
- Decoding the brain-machine interaction for upper limb assistive technologies: advances and challenges.Frontiers in human neuroscience · 2025Review
- Multimodal closed-loop strategies for gait recovery after spinal cord injury and stroke via the integration of robotics and neuromodulation.Frontiers in neuroscience · 2025Review
- Current implications of EEG and fNIRS as functional neuroimaging techniques for motor recovery after stroke.Medical review (2021) · 2024Review
- Beta-Band Cortico-Muscular Phase Coherence in Hemiparetic Stroke.Biomedical signal processing and control · 2024Article
- Get ready! High urgency reduces beta band cortico-muscular coherence during motor preparation.Neuroimage. Reports · 2024Article
- A hybrid brain-muscle-machine interface for stroke rehabilitation: Usability and functionality validation in a 2-week intensive intervention.Frontiers in bioengineering and biotechnology · 2024Article
- Deep learning-based framework for real-time upper limb motion intention classification using combined bio-signals.Frontiers in neurorobotics · 2023Article
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundBrain-Computer Interfaces (BCI) promote upper limb recovery in stroke patients reinforcing motor related brain activity (from electroencephalogaphy, EEG). Hybrid BCIs which include peripheral signals (electromyography, EMG) as control features could be employed to monitor post-stroke motor abnormalities. To ground the use of corticomuscular coherence (CMC) as a hybrid feature for a rehabilitative BCI, we analyzed high-density CMC networks (derived from multiple EEG and EMG channels) and their relation with upper limb motor deficit by comparing data from stroke patients with healthy participants during simple hand tasks.
methodsEEG (61 sensors) and EMG (8 muscles per arm) were simultaneously recorded from 12 stroke (EXP) and 12 healthy participants (CTRL) during simple hand movements performed with right/left (CTRL) and unaffected/affected hand (EXP, UH/AH). CMC networks were estimated for each movement and their properties were analyzed by means of indices derived ad-hoc from graph theory and compared among groups.
resultsBetween-group analysis showed that CMC weight of the whole brain network was significantly reduced in patients during AH movements. The network density was increased especially for those connections entailing bilateral non-target muscles. Such reduced muscle-specificity observed in patients was confirmed by muscle degree index (connections per muscle) which indicated a connections' distribution among non-target and contralateral muscles and revealed a higher involvement of proximal muscles in patients. CMC network properties correlated with upper-limb motor impairment as assessed by Fugl-Meyer Assessment and Manual Muscle Test in patients.
conclusionsHigh-density CMC networks can capture motor abnormalities in stroke patients during simple hand movements. Correlations with upper limb motor impairment support their use in a BCI-based rehabilitative approach.
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