Multi-scale entropy (MSE) has been recently established as a promising tool
for the analysis of the moment-to-moment variability of neural signals.
Appealingly, MSE provides a measure of the predictability of neural operations
across the multiple time scales on which the brain operates. An important
limitation in the application of the MSE to some classes of neural signals is
MSE’s apparent reliance on long time series. However, this sparse-data
limitation in MSE computation could potentially be overcome via MSE estimation
across shorter time series that are not necessarily acquired continuously
(e.g., in fMRI block-designs). In the present study, using simulated, EEG, and
fMRI data, we examined the dependence of the accuracy and precision of MSE
estimates on the number of data points per segment and the total number of
data segments. As hypothesized, MSE estimation across discontinuous segments
was comparably accurate and precise, despite segment length. A key advance of
our approach is that it allows the calculation of MSE scales not previously
accessible from the native segment lengths. Consequently, our results may
permit a far broader range of applications of MSE when gauging moment-to-
moment dynamics in sparse and/or discontinuous neurophysiological data typical
of many modern cognitive neuroscience study designs