A multi-feature resting-state EEG framework for candidate EEG feature discovery in central vertigo.

Central vertigo (CV) lacks objective electrophysiological measures for severity assessment and rehabilitation monitoring. We aimed to characterize multiscale resting-state EEG alterations and identify clinically interpretable candidate features in stroke-related CV. Approach: Resting-state EEG was analyzed in 50 patients with stroke-related CV (31 moderate, 19 severe) and 31 age-matched healthy controls. The framework integrated relative spectral power, cross-frequency coupling, PLV-based sensor-level phase synchrony, graph metrics, machine-learning feature ranking, and associations with balance confidence and dizziness severity. Main results: Severe CV showed widespread relative delta-power reductions of 28.7%-29.4% versus controls. Post hoc analysis showed lower global absolute delta power in severe CV than controls (Tukey p = 0.0227; rank-based FDR q = 0.0459), although the absolute-power effect was less spatially extensive. Both patient groups showed reduced delta-theta and delta-beta amplitude-amplitude coupling, enhanced delta-alpha phase-phase coupling, and theta-band increases in PLV-derived node degree, clustering, and global efficiency; local efficiency increased only in SV. Delta-beta amplitude-amplitude coupling ranked highest across machine-learning methods and correlated moderately with balance confidence (ρ = 0.470, p = 0.001) and dizziness severity (ρ = -0.472, p = 0.001). Zero-lag-robust measures showed the same theta ordering but were nonsignificant after FDR correction and did not establish volume-conduction-independent topology, supporting cautious PLV interpretation. Significance: Stroke-related CV involves coordinated alterations across oscillatory, cross-frequency, and sensor-level network measures. This interpretable framework identifies candidate EEG features for objective characterization that require external and longitudinal validation before clinical use. .