ArticleBrain sciences2023
High Variability Periods in the EEG Distinguish Cognitive Brain States.
Article in Brain sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Adjustments of balance control during cognitive dual tasking: Evidence from event-related force-plate analysis.Psychological research · 2025Article
- Article
- A novel method for estimating functional connectivity from EEG coherence potentials.Scientific reports · 2025Article
- Information-Theoretical Analysis of the Cycle of Creation of Knowledge and Meaning in Brains under Multiple Cognitive Modalities.Sensors (Basel, Switzerland) · 2024Article
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2 authors.
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Abstract
objectiveTo describe a novel measure of EEG signal variability that distinguishes cognitive brain states.
methodWe describe a novel characterization of amplitude variability in the EEG signal termed "High Variability Periods" or "HVPs", defined as segments when the standard deviation of a moving window is continuously higher than the quartile cutoff. We characterize the parameter space of the metric in terms of window size, overlap, and threshold to suggest ideal parameter choice and compare its performance as a discriminator of brain state to alternate single channel measures of variability such as entropy, complexity, harmonic regression fit, and spectral measures.
resultsWe show that the average HVP duration provides a substantially distinct view of the signal relative to alternate metrics of variability and, when used in combination with these metrics, significantly enhances the ability to predict whether an individual has their eyes open or closed and is performing a working memory and Raven's pattern completion task. In addition, HVPs disappear under anesthesia and do not reappear in early periods of recovery.
conclusionsHVP metrics enhance the discrimination of various brain states and are fast to estimate. SIGNIFICANCE: HVP metrics can provide an additional view of signal variability that has potential clinical application in the rapid discrimination of brain states.
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