Pattern analysis of sleep‐deprived human EEG

Abstract

Progress during the past decade in non‐linear dynamics and instability theory has provided useful tools for understanding spatio‐temporal pattern formation. Procedures which apply principle component analysis (using the Karhunen–Loeve decomposition technique) to the multichannel electroencephalograph (EEG) time series have been developed. This technique shows localized changes of cortical functioning; it identifies increases and decreases of the activity of localized cortical regions over time while the subject performs a simple task or test. It can be used to demonstrate the change in cortical dynamics in response to a continuous challenge. Using 16 EEG electrodes, the technique provides spatio‐temporal information not obtained with power spectrum analysis, and includes the weighted information given with omega complexity. As an application, we performed a pattern analysis of sleep‐deprived human EEG data in 20 healthy young men. Electroencephalograph recordings were performed on subjects for <2 min, with eyes closed after normal sleep and after 24 h of experimentally‐induced sleep deprivation. The significant changes in the eigenvector components indicated the relative changes of local activity in the brain with progressive sleep deprivation. A sleep deprivation effect was observed, which was hemispherically correlated but with opposite directional dynamics. These changes were seen in the temporo‐parietal regions bilaterally. The application of the technique showed that the simple test task was performed with a limited unilateral hemispheric involvement at baseline, but needed a much larger cortical participation with decreased frontal activity and increased coherence and bilateral hemispheric involvement. The calculations performed demonstrated that the same weighted changes as those obtained with omega complexity were shown, but the technique had the added advantage of showing the localized directional changes of the principle eigenvector at each studied electrode, pointing out the cortical localized region affected by the sleep deprivation and toward which direction the environmental challenge induced the spatial change. This methodology may allow the evaluation of changes in local dynamics in brain activity in normal and pathological conditions.

Waking EEG Cortical Markers of Chronic Pain and Sleepiness

“…Alpha band activity is thought to represent inhibitory drive from activation of GABAergic synapses from the thalamic projections to the cortex [22]. Alpha (8)(9)(10)(11)(12) activity is also reported to be lower in individuals with chronic pain [9], localized in the insula [2] and frontal electrodes [9], with reduced high alpha (13-15 Hz) activity reported in the frontal electrodes [3]. This pattern of findings indicating abnormal low frequency oscillations between the thalamus and the cortex has been labeled "thalamo-cortical dysrhythmia" [1,23] and may underlie, or at least put individuals at risk for, the experience of pain.…”

“…One issue that has not been addressed is the potential biasing impact of sleep deficits on EEG-measured activity. Sleep deficits are commonly reported by people with chronic pain [8], and the EEG bandwidth markers associated with sleep deprivation have similarities to those associated with chronic pain [3,[9][10][11][12][13][14]. These findings raise important basic questions: 1) Can we distinguish between the contributions of chronic pain and sleep deficits found within the EEG bandwidth power spectrum?…”

Effects of sleep deprivation on neural functioning: an integrative review

“…Several studies reported an increase in the degree of correlation between activity in both hemispheres as a result of prolonged wakeful-ness [see e.g., ref. 99 , 100 ]. Furthermore, an anterior shift of activity has been reported after 24 h of wakefulness: the overall power, as well as the power contained in the alpha band, decreased at occipital and temporal channels and increased at central and frontal channels [ 76 ].…”

Mathematical models for water vapour resistance prediction of printed garments

“…Essentially, a garment disrupts sweat evaporation from the skin surface, and this insufficient evaporation often provides the feel of discomfort found while wearing a garment [26]. during sweating, sweat is released into the environment through the garment.…”

“…The rate of sweat evaporation largely depends on the garment that represents a barrier in this process. Essentially, a garment disrupts sweat evaporation from the skin surface, and this insufficient evaporation often provides the feel of discomfort found while wearing a garment . The evaporation rate itself affects comfort that is received through wearing of a certain garment.…”