# From Signal Stacking to Dynamic Coupling: A Critical Review of Wearable EEG-EMG Fusion Brain-Computer Interfaces for Stroke Rehabilitation

Review · Dai M, Jiao M, Wang Y

- Archive id: doi:10.3390/mi17091013
- Published: 2026-08-27
- Venue: Micromachines
- DOI: https://doi.org/10.3390/mi17091013
- PMID: 42796132
- PMCID: PMC13609253
- Citations: 0
- Source URL: https://doi.org/10.3390/mi17091013
- PDF: https://europepmc.org/articles/PMC13609253/pdf
- Provenance: europepmc

## Abstract

This structured critical review examines wearable brain-computer interface (BCI) systems that integrate electroencephalographic (EEG) and electromyographic (EMG) signals for post-stroke motor rehabilitation. The central engineering problem is the spatio-temporal heterogeneity between cortical and muscular signals, which limits the reliability and generalizability of conventional EEG-EMG fusion. We review acquisition and synchronization methods, data-, feature-, and decision-level fusion, deep-learning architectures, wearable implementation, and clinically oriented closed-loop rehabilitation. Conventional fusion can exploit complementary information but usually treats the cross-modal relationship as fixed. By contrast, dynamic brain-muscle coupling is defined here as the explicit, time-resolved estimation of interaction strength, delay, directionality, or network topology between cortical regions and target muscles. Measurable candidates include time-resolved corticomuscular coherence, phase locking, lagged dependence, information-theoretic directionality, and dynamic graph connectivity. Coupling-aware and graph-based methods are promising, but clinical translation remains constrained by artifacts, inter-subject and cross-session variability, overfitting, limited clinical datasets, interpretability, synchronization error, and embedded-computing requirements. The review therefore proposes a transparent pathway from static signal stacking toward physiologically grounded, dynamically coupled, and adaptively controlled rehabilitation systems.

_Abstract reproduced under CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)._

Topics: [Brain-Computer Interfaces](https://space-kitty.com/topics/brain-computer-interfaces)

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