High Variability Periods in the EEG Distinguish Cognitive Brain States

Abstract

Objective: To describe a novel measure of EEG signal variability that distinguishes cognitive brain states. Method: We 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. Results: We 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. Conclusions: HVP 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.

Information-Theoretical Analysis of the Cycle of Creation of Knowledge and Meaning in Brains under Multiple Cognitive Modalities

“…Significant progress has been made using scalp EEG arrays for cognitive state identification of people performing everyday tasks, some of them in sport [ 6 ] and meditation [ 7 ], and for mental state detection for pilots and accident prevention [ 8 ]. Studies have also involved participants tested with closed vs. open eyes in visual and auditory recognition tasks, as well as motor tasks [ 9 , 10 ] including emotion recognition in human computer interaction [ 11 ]. Several challenges in different areas are reviewed in [ 12 ] that included safety and health, for example, via measuring physiological signals such as EEG.…”