EEG-based resting-state functional connectivity (FC) has been widely explored as a prognostic tool in stroke recovery, offering a cost-effective alternative to fMRI. However, it remains unclear whether FC measures are reliable biomarkers for predicting stroke recovery. This systematic review provides a comprehensive overview of existing EEG-based FC measures and their clinical relevance in stroke recovery. Specifically, this study aims to identify the most reliable and predictive FC measures of recovery by examining their relationship with longitudinal changes, clinical outcomes, and neurorehabilitation protocols. Results show that while some studies report associations between FC and recovery, no consistent patterns emerge. Significant methodological heterogeneity, such as differences in sensor- vs. source-level analysis, study design, connectivity measures, and reliance on correlational rather than predictive approach, limits the interpretability and comparability of results. Overall, these inconsistencies raise concerns about the reliability of EEG-based FC measures as biomarkers of stroke recovery. Moreover, while most studies focused on motor recovery, emerging evidence suggests FC may also help predict cognitive recovery. Future research should prioritize predictive models tailored to clinical needs, explore multidimensional recovery domains, and establish standardized protocols to enhance methodological consistency. By addressing these challenges and harnessing advanced computational techniques, EEG-based FC holds the potential to transform personalized rehabilitation strategies and optimize outcomes for stroke patients. Given the wide range of analytical scenarios and the absence of a superior method, we propose that a “multiverse” analytical approach could offer valuable insights into the most promising pathways for establishing connectivity as a potential biomarker for stroke recovery.
Napoli et al. (2026) studied this question.