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Consequently, individual boundaries of frequency bands could be justified, making the vigilance classification of VIGALL more exact.
RTs of trials with the same vigilance classification were averaged, thus mean RTs for the different EEG-vigilance (sub-) stages are available for each subjects.
In case of a stable vigilance state within these three seconds, i.e. three equal VIGALL-vigilance classifications within the three-second-sequence, trials were included for calculating the average vigilance-specific re-tapping interval length per subject.
By this method, vigilance monitoring and classification is broadly applicable in future studies to control for intra-individual variability.
Moreover, in our study the classification of vigilance states was computed automatically by the EEG-based algorithm VIGALL, whereas certain EEG-patterns in the previous study were detected by individual raters.
Regardless of the type of reference (mastoid vs. Cz) or vigilance state (EC vs. EO), most models exhibited low classification errors, with high accuracy and sensitivity values.
Vigilance and diligence at local and state levels are needed to ensure consistent classification of early neonatal deaths so that valid comparisons can be made between counties and states.
Therefore, the classification of EEG-vigilance stages via VIGALL may assist in identifying clinically relevant variations.
Hence, VIGALL is now based on EEG-power source estimates using LORETA (Low Resolution Brain Electromagnetic Tomography) and enables the classification of EEG-vigilance stages for 1-sec-segments.
Ah, vigilance.
Your vigilance is commendable".
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