{"id":"doi:10.3390/mi17091013","kind":"review","title":"From Signal Stacking to Dynamic Coupling: A Critical Review of Wearable EEG-EMG Fusion Brain-Computer Interfaces for Stroke Rehabilitation","authors":["Dai M","Jiao M","Wang Y"],"author_count":3,"year":2026,"published":"2026-08-27","venue":"Micromachines","cited_by":0,"doi":"10.3390/mi17091013","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.3390/mi17091013","archive_url":"https://space-kitty.com/papers/doi:10.3390/mi17091013","updated":null,"pmid":"42796132","pmcid":"PMC13609253","openalex_id":null,"pdf_url":"https://europepmc.org/articles/PMC13609253/pdf","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_license":"CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)","license":"CC-BY","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":[],"details":null,"provenance":"europepmc","attribution":"Abstract of \"From Signal Stacking to Dynamic Coupling: A Critical Review of Wearable EEG-EMG Fusion Brain-Computer Interfaces for Stroke Rehabilitation\" by Dai M, Jiao M, Wang Y, https://doi.org/10.3390/mi17091013, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified.","topics":[{"slug":"brain-computer-interfaces","name":"Brain-Computer Interfaces","domain_slug":"neuroscience"}],"cited_in_dossiers":[]}