A multi-feature resting-state EEG framework for candidate EEG feature discovery in central vertigo
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Le résumé fourni par la source
Abstract 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 (Tukey p = 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.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- A multi-feature resting-state EEG framework for candidate EEG feature discovery in central vertigo
- Date Crossref
- 01/08/2026
- Éditeur
- IOP Publishing
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Tianjin University pays non établi dans la noticeUniversité ou école supérieure
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Human Computer Interaction (Switzerland) pays non établi dans la noticeEntreprise
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Tianjin Huanhu Hospital pays non établi dans la noticeÉtablissement de santé
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Xinjiang Institute of Engineering pays non établi dans la noticeUniversité ou école supérieure
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Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration pays non établi dans la noticeStructure de recherche
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Tianjin Key Laboratory of Cerebral Vascular and Neurodegenerative Diseases pays non établi dans la noticeStructure de recherche
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Xinjiang Hetian College pays non établi dans la noticeUniversité ou école supérieure
Tianjin University, Human Computer Interaction (Switzerland) et Tianjin Huanhu Hospital, avec 4 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.