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bioRxiv · 10.64898/2026.06.01.728505

Electrophysiological Signature of Stroke Recovery: Investigating EEG Biomarkers for Prognostic Insights

Khalili-Ardali, M.·Sharma, V.·Mandahar, T. S.·Pascoa dos Santos, F.·Tiesinga, P.·Ramsey, N.
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Abstract

Stroke is a leading cause of long-term disability, often resulting in persistent motor impairments reflecting disruptions in large-scale brain networks. While electroencephalography (EEG) has long been used to monitor neurophysiological changes following stroke, an integrated framework capturing spatiotemporal dynamics would help understand changes over time. In this study, we analysed resting-state EEG from stroke patients at one week (Session 1) and three months (Session 2) post-stroke to investigate electrophysiological biomarkers of motor recovery, indexed by changes in the Fugl-Meyer scale ({Delta}FM ). We quantified spectral properties, focusing on the relative alpha band power, microstate metrics such as mean duration, complexity, and transition probabilities, and measures of metastability and synchrony derived from the Kuramoto Order Parameter. Among all the EEG measures, the longitudinal change in relative alpha power emerged as the strongest single correlate of motor improvement, accounting for the largest proportion of variance among the examined EEG measures. Although metastability and synchrony alone did not reach statistical significance, they showed moderate positive correlations with {Delta}FM, particularly in the alpha and theta ranges, and once combined with alpha power, added 26% in explaining the variance in {Delta}FM . Microstate parameters did not explain additional variance in recovery once alpha power and network-level dynamics were considered. A hierarchical model combining alpha power, metastability/synchrony, and microstates explained over 78% of the variance in {Delta}FM, indicating that stroke recovery involves restoring balanced alpha oscillations and flexible large-scale brain coordination. Future research with larger samples and more frequent longitudinal assessments is required to confirm the prognostic utility of integrated EEG biomarkers for guiding personalised stroke rehabilitation strategies.

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Khalili-Ardali, M., Sharma, V., Mandahar, T. S., Pascoa dos Santos, F., Tiesinga, P., Ramsey, N.. 2026-06-04. Electrophysiological Signature of Stroke Recovery: Investigating EEG Biomarkers for Prognostic Insights. https://doi.org/10.64898/2026.06.01.728505

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