Article
Refine
Document Type
- Article (3) (remove)
Language
- English (3)
Has Fulltext
- yes (3)
Is part of the Bibliography
- no (3)
Keywords
- EEG (3) (remove)
Institute
Publisher
Manual sleep scoring for research purposes and for the diagnosis of sleep disorders is labor-intensive and often varies significantly between scorers, which has motivated many attempts to design automatic sleep stage classifiers. With the recent introduction of large, publicly available hand-scored polysomnographic data, and concomitant advances in machine learning methods to solve complex classification problems with supervised learning, the problem has received new attention, and a number of new classifiers that provide excellent accuracy. Most of these however have non-trivial barriers to use. We introduce the Greifswald Sleep Stage Classifier (GSSC), which is free, open source, and can be relatively easily installed and used on any moderately powered computer. In addition, the GSSC has been trained to perform well on a large variety of electrode set-ups, allowing high performance sleep staging with portable systems. The GSSC can also be readily integrated into brain-computer interfaces for real-time inference. These innovations were achieved while simultaneously reaching a level of accuracy equal to, or exceeding, recent state of the art classifiers and human experts, making the GSSC an excellent choice for researchers in need of reliable, automatic sleep staging.
Separating EEG correlates of stress: Cognitive effort, time pressure, and social‐evaluative threat
(2022)
Abstract
The prefrontal cortex is a key player in stress response regulation. Electroencephalographic (EEG) responses, such as a decrease in frontal alpha and an increase in frontal beta power, have been proposed to reflect stress‐related brain activity. However, the stress response is likely composed of different parts such as cognitive effort, time pressure, and social‐evaluative threat, which have not been distinguished in previous studies. This distinction, however, is crucial if we aim to establish reliable tools for early detection of stress‐related conditions and monitoring of stress responses throughout treatment. This randomized cross‐over study (N = 38) aimed to disentangle EEG correlates of stress. With linear mixed models accounting for missing values in some conditions, we found a decrease in frontal alpha and increase in beta power when performing the Paced Auditory Serial Addition Test (PASAT; cognitive effort; n = 32) compared to resting state (n = 33). No change in EEG power was found when the PASAT was performed under time pressure (n = 29) or when adding social‐evaluative threat (video camera; n = 29). These findings suggest that frontal EEG power can discriminate stress from resting state but not more fine‐grained differences of the stress response.
Recent research suggests that the P3b may be closely related to the activation of the locus coeruleus-norepinephrine (LC-NE) system. To further study the potential association, we applied a novel technique, the non-invasive transcutaneous vagus nerve stimulation (tVNS), which is speculated to increase noradrenaline levels. Using a within-subject cross-over design, 20 healthy participants received continuous tVNS and sham stimulation on two consecutive days (stimulation counterbalanced across participants) while performing a visual oddball task. During stimulation, oval non-targets (standard), normal-head (easy) and rotated-head (difficult) targets, as well as novel stimuli (scenes) were presented. As an indirect marker of noradrenergic activation we also collected salivary alpha-amylase (sAA) before and after stimulation. Results showed larger P3b amplitudes for target, relative to standard stimuli, irrespective of stimulation condition. Exploratory post hoc analyses, however, revealed that, in comparison to standard stimuli, easy (but not difficult) targets produced larger P3b (but not P3a) amplitudes during active tVNS, compared to sham stimulation. For sAA levels, although main analyses did not show differential effects of stimulation, direct testing revealed that tVNS (but not sham stimulation) increased sAA levels after stimulation. Additionally, larger differences between tVNS and sham stimulation in P3b magnitudes for easy targets were associated with larger increase in sAA levels after tVNS, but not after sham stimulation. Despite preliminary evidence for a modulatory influence of tVNS on the P3b, which may be partly mediated by activation of the noradrenergic system, additional research in this field is clearly warranted. Future studies need to clarify whether tVNS also facilitates other processes, such as learning and memory, and whether tVNS can be used as therapeutic tool.