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the overall goal of this procedure is to quantify the variability of an eeg time series and relate that variability to the information processing capacity of the underlying neural system this is accomplished by first acquiring high quality eeg recordings of the brainamp;#39;s response the second step is to pre-process the data to remove any artifacts next the statistics of interest are extracted here we will contrast the novel application of multi-scale entropy with more traditional methods of mean amplitude and spectral power the final step is to analyze the statistical significance of the results and interpret the data this step can be facilitated by using data-driven multivariate approaches like partial least-squares analysis ultimately multi-scale entropy is used to show how a sequence of changes in the spatial temporal pattern across multiple time scales contributes to specific cognitive operations the advantage to using msc over existing methods like mean amplitude or sp
