ArticleJournal of neuroengineering and rehabilitation2026
Lesion-specific EEG signatures in stroke: a multi-scale framework integrating oscillations, connectivity, and asymmetry for machine learning decoding.
Article in Journal of neuroengineering and rehabilitation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundLesion location is a major source of post-stroke neurophysiological heterogeneity, yet most electroencephalography (EEG) studies analyze patients as a single group, limiting lesion-specific biomarkers and translation. We proposed a lesion-centric, multi-scale EEG framework integrating local oscillations, inter-regional connectivity, and hemispheric asymmetry with machine learning to characterize and decode basal ganglia (P1), fronto-temporal/centrum semiovale (P2), and brainstem (P3) lesions.
methodsFive-minute eyes-open, 128-channel resting EEG ([Formula: see text]) was recorded in 57 subacute stroke patients (P1 = 22, P2 = 18, P3 = 17) and 22 matched controls. From artifact-minimized [Formula: see text] segments, ROI-averaged power spectral density (PSD) (θ: 4–[Formula: see text]; α: 7–[Formula: see text]; [Formula: see text]: 12–[Formula: see text]; peak α frequency), current source density (CSD)-based magnitude-squared coherence, and directional BSI (dirBSI) were computed. Between-group and subgroup differences were assessed using t-tests/Wilcoxon and ANOVA/Kruskal–Wallis with Benjamini–Hochberg FDR ([Formula: see text]). EEG–behavior associations were examined with Spearman correlations. For machine learning, common spatial patterns (CSP) features were classified using linear discriminant analysis (LDA) with leave-one-subject-out cross-validation. To align with clinical workflow, we report HC vs P as “stroke detection/screening” and patient-only P1/P2/P3 classification as “lesion subtype decoding for stratification” (along with pairwise P1 vs P2, P1 vs P3, and P2 vs P3 models). An EEGNet baseline was evaluated for comparison.
resultsIncreased α power and a leftward peak α shift were observed in patients (HC: [Formula: see text]; P: [Formula: see text]; [Formula: see text]). Pre-FDR, θ-band frontal–motor connectivity was strengthened, while posterior P–O connectivity in α/[Formula: see text] was weakened. Ipsilesional dominance in θ was indicated by dirBSI (HC: [Formula: see text]; P: [Formula: see text]; [Formula: see text]). Across lesions, [Formula: see text] power differences in central/parietal/occipital ROIs were detected pre-FDR, with higher parietal [Formula: see text] in P3; α-band asymmetry was stronger in P1/P2 and more symmetric in P3 ([Formula: see text]). EEG–behavior correlations did not survive FDR. Using CSP+LDA, accuracies of 92.41% (HC vs P), 94.87% (P1 vs P3), 85.71% (P2 vs P3), and 82.50% (P1 vs P2) were achieved; all binary AUCs exceeded 0.85; three-class accuracy reached 85.96%.
conclusionThis multi-scale EEG framework identifies lesion-associated neurophysiological signatures and demonstrates feasible lesion subtype decoding, supporting the potential of EEG biomarkers for objective stratification and precision neurorehabilitation.
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