Vigilance end-spurt patterns in event-related potentials.

The end-spurt effect, where performance declines with time-on-task and then increases toward the end of a task, has garnered little attention in the vigilance literature. Researchers have suggested the performance enhancement is due to increased motivation or arousal with knowledge of the end of the vigil. However, recent examination of neural signature patterns during a simultaneous discrimination task, where task length was unknown, provided preliminary support that the end-spurt reflects pacing of resources. The current effort expands this previous work to an additional simultaneous task and to a successive discrimination task across two sessions, one where task length was not known and one where task length was known. Twenty-eight (Study 1) and a separate 24 (Study 2) participants completed a Simultaneous Radar task (Study 1) in one session and Simultaneous and Successive Lines tasks (Study 2) across two sessions while neural data was collected. Several event-related potentials exhibited non-monotonic patterns during the vigilance tasks, in some cases reflecting end-spurt patterns, but more commonly reflecting higher-order polynomial patterns. These patterns were more prevalent in anterior regions as opposed to posterior regions. Of note, the N1 anterior exhibited consistent general patterns across all the vigilance tasks and across sessions. Importantly, even when participants had knowledge of session length, some ERPs still exhibited higher-order polynomial trends, suggesting pacing rather than an end-spurt from motivation or arousal as the vigil ends. These insights can help inform predictive modeling of vigilance performance and the implementation of mitigation efforts to allay the vigilance decrement.

A practical comparison of algorithms for the measurement of multiscale entropy in neural time series data.

There is a broad family of statistical methods for capturing time series regularity, with increasingly widespread adoption by the neuroscientific community. A common feature of these methods is that they permit investigators to quantify the entropy of brain signals - an index of unpredictability/complexity. Despite the proliferation of algorithms for computing entropy from neural time series data there is scant evidence concerning their relative stability and efficiency. Here we evaluated several different algorithmic implementations (sample, fuzzy, dispersion and permutation) of multiscale entropy in terms of their stability across sessions, internal consistency and computational speed, accuracy and precision using a combination of electroencephalogram (EEG) and synthetic 1/ƒ noise signals. Overall, we report fair to excellent internal consistency and longitudinal stability over a one-week period for the majority of entropy estimates, with several caveats. Computational timing estimates suggest distinct advantages for dispersion and permutation entropy over other entropy estimates. Considered alongside the psychometric evidence, we suggest several ways in which researchers can maximize computational resources (without sacrificing reliability), especially when working with high-density M/EEG data or multivoxel BOLD time series signals.