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A multi-feature resting-state EEG framework for candidate EEG feature discovery in central vertigo
Jiedong Nan1, Xuan Yang2, Haoran Jiang1
1Tianjin University, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China, Tianjin, 300072, China.
Journal of Neural Engineering
|August 12, 2026
Summary
Central vertigo (CV) lacks objective measures. This study found specific electroencephalography (EEG) patterns in stroke patients with CV, identifying potential biomarkers for severity assessment and rehabilitation monitoring.
Area of Science:
- Neuroscience
- Clinical Electrophysiology
- Medical Imaging
Background:
- Central vertigo (CV) presents a diagnostic challenge due to the absence of objective electrophysiological markers for assessing severity and monitoring rehabilitation.
- Stroke is a common cause of CV, necessitating reliable methods for patient stratification and treatment evaluation.
Purpose of the Study:
- To characterize multiscale resting-state electroencephalography (EEG) alterations in stroke-related CV.
- To identify clinically interpretable EEG features for objective severity assessment and rehabilitation monitoring in CV patients.
Main Methods:
- Resting-state EEG data were collected from 50 patients with stroke-related CV (moderate and severe) and 31 healthy controls.
- Analysis integrated relative spectral power, cross-frequency coupling, phase locking value (PLV)-based sensor-level phase synchrony, and graph metrics.
- Machine learning was employed for feature ranking, and associations with balance confidence and dizziness severity were examined.
Main Results:
- Severe CV exhibited widespread relative delta-power reductions and lower global absolute delta power compared to controls.
- Both CV groups showed altered amplitude-amplitude coupling (e.g., reduced delta-theta, delta-beta) and phase-phase coupling (e.g., enhanced delta-alpha).
- Theta-band PLV-derived network metrics (node degree, clustering, global efficiency) were increased in CV patients; delta-beta amplitude-amplitude coupling showed the strongest correlation with clinical measures.
Conclusions:
- Stroke-related CV is associated with coordinated alterations across oscillatory, cross-frequency, and sensor-level network EEG measures.
- The study identified candidate EEG features, particularly delta-beta amplitude-amplitude coupling, for objective characterization of CV.
- These findings require external validation for potential clinical application in severity assessment and rehabilitation monitoring.