English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Meeting Abstract

EEG microstate parameters as indicators of vigilance loss during eyes- closed rest

MPS-Authors
/persons/resource/persons214587

Walter,  M
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;

External Resource
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

Krylova, M., Alizadeh, S., Jamalabadi, H., Izyurov, I., van der Meer, J., & Walter, M. (2019). EEG microstate parameters as indicators of vigilance loss during eyes- closed rest. In 45. Jahrestagung Psychologie und Gehirn (PuG 2019) (pp. 32).


Cite as: http://hdl.handle.net/21.11116/0000-0003-A878-9
Abstract
Records of spontaneous brain electrical activity show that EEG topography remains quasi-stable for short time periods (≈100ms) that are often referred to as “EEG microstates” (Lehmann et.al. 1998). Changes in parameters of microstates (e.g. duration, occurrence, contribution) are associated with psychiatric disorders and mental states. In this study we investigated how parameters of the EEG microstates change with loss of vigilance during typical eyes-closed rest. We analysed resting-state recordings from two data sets (39 and 19 healthy male subjects respectively). Four microstates classes were identified using EEGLAB plugin for microstate analysis by Thomas Koenig (www.thomaskoenig.ch/index.php/software/). Time courses of duration, occurrence, contribution, and transition probabilities were estimated using non-overlapping 6s windows. Vigilance time series were estimated as ratio of the root mean square (rms) amplitude in alpha (7-13Hz) to the rms amplitudes in delta and theta (1-7Hz) bands for the same windows. To test for the group level association between vigilance time series and time courses of the microstate parameters we used one-sample t-test on the Fisher z-transformed individual correlation coefficients. We also used support vector machine regression to predict vigilance time series based on the parameters of microstates. We found that microstate parameters have temporal dynamics that is partly modulated by vigilance. The time courses of duration and contribution of microstate C are positively correlated while the time courses of occurrence and contribution of microstates AandB are negatively correlated with vigilance time series. We also show that microstate parameters can be used for the prediction of the vigilance level (p<0.001).