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

Objective. 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 (Tukeyp= 0.0227; rank-based false-discovery-rate (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 (AAC), enhanced delta-alpha PPC, and theta-band increases in PLV-derived node degree, clustering, and global efficiency; local efficiency increased only in SV. Delta-beta AAC 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.