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Prediction of Reaction Time and Vigilance Variability from Spatiospectral Features of Resting-State EEG in a Long Sustained Attention Task
Resting-state brain networks represent the intrinsic state of the brain
during the majority of cognitive and sensorimotor tasks. However, no study has
yet presented concise predictors of task-induced vigilance variability from
spectrospatial features of the pre-task, resting-state electroencephalograms
(EEG). We asked ten healthy volunteers (6 females, 4 males) to participate in
105-minute fixed-sequence-varying-duration sessions of sustained attention to
response task (SART). A novel and adaptive vigilance scoring scheme was
designed based on the performance and response time in consecutive trials, and
demonstrated large inter-participant variability in terms of maintaining
consistent tonic performance. Multiple linear regression using feature
relevance analysis obtained significant predictors of the mean cumulative
vigilance score (CVS), mean response time, and variabilities of these scores
from the resting-state, band-power ratios of EEG signals, p<0.05. Single-layer
neural networks trained with cross-validation also captured different
associations for the beta sub-bands. Increase in the gamma (28-48 Hz) and upper
beta ratios from the left central and temporal regions predicted slower
reactions and more inconsistent vigilance as explained by the increased
activation of default mode network (DMN) and differences between the high- and
low-attention networks at temporal regions. Higher ratios of parietal alpha
from the Brodmann's areas 18, 19, and 37 during the eyes-open states predicted
slower responses but more consistent CVS and reactions associated with the
superior ability in vigilance maintenance. The proposed framework and these
findings on the most stable and significant attention predictors from the
intrinsic EEG power ratios can be used to model attention variations during the
calibration sessions of BCI applications and vigilance monitoring systems.Comment: 11 pages, 6 figures; submitted to the Journal of Biomedical and
Health Informatic