Abstract
We present an open-source Python package to compute information-theoretical quantities for electroencephalographic data. Electroencephalography (EEG) measures the electrical potential generated by the cerebral cortex and the set of spatial patterns projected by the brain's electrical potential on the scalp surface can be clustered into a set of representative maps called EEG microstates. Microstate time series are obtained by competitively fitting the microstate maps back into the EEG data set, i.e., by substituting the EEG data at a given time with the label of the microstate that has the highest similarity with the actual EEG topography. As microstate sequences consist of non-metric random variables, e.g., the letters A–D, we recently introduced information-theoretical measures to quantify these time series. In wakeful resting state EEG recordings, we found new characteristics of microstate sequences such as periodicities related to EEG frequency bands. The algorithms used are here provided as an open-source package and their use is explained in a tutorial style. The package is self-contained and the programming style is procedural, focusing on code intelligibility and easy portability. Using a sample EEG file, we demonstrate how to perform EEG microstate segmentation using the modified K-means approach, and how to compute and visualize the recently introduced information-theoretical tests and quantities. The time-lagged mutual information function is derived as a discrete symbolic alternative to the autocorrelation function for metric time series and confidence intervals are computed from Markov chain surrogate data. The software package provides an open-source extension to the existing implementations of the microstate transform and is specifically designed to analyze resting state EEG recordings.
Highlights
AND BACKGROUNDElectroencephalography (EEG) is a routine technique in neuroscientific research and the clinical sciences, used to measure electrical potentials generated by the cerebral cortex
In wakeful resting state EEG recordings, we found new characteristics of microstate sequences such as periodicities related to EEG frequency bands
Among the data reduction techniques that have been employed to compress EEG recordings, the microstate algorithm is of special importance as it has been evaluated in a variety of experimental conditions (Lehmann et al, 1987; Wackermann et al, 1993; Pascual-Marqui et al, 1995)
Summary
Electroencephalography (EEG) is a routine technique in neuroscientific research and the clinical sciences, used to measure electrical potentials generated by the cerebral cortex. The transition matrix can only fully represent a first-order Markov process, for which the complete information about the future state Xt+1 is contained in the random variable Xt. We have shown that resting state EEG microstate sequences do not follow the Markov property, when testing Markovianity of the time series statistically (von Wegner et al, 2017). The transition matrix approach and the random walk embedding contradict each other on a theoretical level, as the first uses a memory-less Markov model and the latter uses an infinite memory, scale-free approach To overcome these limitations, in a recent publication we introduced information-theoretical methods in order to (i) work with an arbitrary number of microstate labels directly, and (ii) to assess the memory structure of microstate sequences for all time lags, i.e., beyond t → t + 1 transitions, as captured by the transition matrix method (von Wegner et al, 2017). The code provided along with this manuscript allows to reproduce our previous results, and to perform new studies using the same methodology
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