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Background <p>Persistent upper limb deficits after stroke necessitate reliable candidate biomarkers to support precision rehabilitation. While electroencephalogram (EEG) provides a highly accessible tool to characterize post-stroke neurophysiology, its clinical translation is hindered by fragmented evidence. This systematical review critically synthesizes the directional associations between EEG biomarkers and upper limb outcomes, and introduces a novel functional framework to classify these biomarkers into assessment, prognostic, and monitoring roles for natural upper limb recovery under conventional rehabilitation.</p> Methods <p>A systematic search was conducted in MEDLINE, SCOPUS, EMBASE, EBSCO CINAHL, and IEEE Xplore up to March 10, 2026. Studies investigating associations between quantitative EEG measures and upper limb motor outcomes in stroke adults were included. Two reviewers independently screened studies and assessed risk of bias. Data extraction classified EEG biomarkers by assessment, prognosis, and monitoring roles.</p> Results <p>Forty-two studies were included, comprising 23 cross-sectional and 19 longitudinal designs. We categorized the evidence into three biomarker roles: (i) assessment, where measures like the brain symmetry index (BSI), β-band interhemispheric connectivity, and network efficiency correlated with impairment severity; (ii) prognostic, where baseline asymmetry and functional connectivity showed predictive potential; and (iii) monitoring, where longitudinal changes in oscillatory power, connectivity, and network topology paralleled functional gains. Across roles, the BSI emerged as one of the most frequently reported candidate metrics.</p> Conclusion <p>EEG-derived metrics, particularly the BSI, serve as frequently reported candidate biomarkers for potential clinical application in stroke rehabilitation. However, their immediate clinical translation is currently limited by the predominantly fair methodological quality of the underlying evidence. Our proposed framework helps to bridge the gap between current observational findings and future clinical utility. Future progress hinges on standardizing protocols and validating these biomarkers in large-scale rehabilitation trials to facilitate their transition toward potentially clinically useful tools.</p>

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EEG biomarkers for assessment, prognosis, and monitoring of natural upper limb recovery after stroke: a systematic review

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月3日

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  • Yifang Lin,
  • Mingfen Li,
  • Yajuan Su,
  • Hewei Wang,
  • Jie Jia

摘要

Background

Persistent upper limb deficits after stroke necessitate reliable candidate biomarkers to support precision rehabilitation. While electroencephalogram (EEG) provides a highly accessible tool to characterize post-stroke neurophysiology, its clinical translation is hindered by fragmented evidence. This systematical review critically synthesizes the directional associations between EEG biomarkers and upper limb outcomes, and introduces a novel functional framework to classify these biomarkers into assessment, prognostic, and monitoring roles for natural upper limb recovery under conventional rehabilitation.

Methods

A systematic search was conducted in MEDLINE, SCOPUS, EMBASE, EBSCO CINAHL, and IEEE Xplore up to March 10, 2026. Studies investigating associations between quantitative EEG measures and upper limb motor outcomes in stroke adults were included. Two reviewers independently screened studies and assessed risk of bias. Data extraction classified EEG biomarkers by assessment, prognosis, and monitoring roles.

Results

Forty-two studies were included, comprising 23 cross-sectional and 19 longitudinal designs. We categorized the evidence into three biomarker roles: (i) assessment, where measures like the brain symmetry index (BSI), β-band interhemispheric connectivity, and network efficiency correlated with impairment severity; (ii) prognostic, where baseline asymmetry and functional connectivity showed predictive potential; and (iii) monitoring, where longitudinal changes in oscillatory power, connectivity, and network topology paralleled functional gains. Across roles, the BSI emerged as one of the most frequently reported candidate metrics.

Conclusion

EEG-derived metrics, particularly the BSI, serve as frequently reported candidate biomarkers for potential clinical application in stroke rehabilitation. However, their immediate clinical translation is currently limited by the predominantly fair methodological quality of the underlying evidence. Our proposed framework helps to bridge the gap between current observational findings and future clinical utility. Future progress hinges on standardizing protocols and validating these biomarkers in large-scale rehabilitation trials to facilitate their transition toward potentially clinically useful tools.