Complexity of the Taskless Mind at Different Time-Scales: an Empirically Weighted Approach to Decomposition and Measurement

The neurodynamical state of an eyes-closed at ‘rest’ subject is an area of keen interest in the neuroscience community due to Raichle’s field changing concept of the Default Mode Network [1]. The dynamic analysis of neurobiologically derived data commonly involves the computation of distributional measures and time‐frequency transforms, and more recently the use of ergodic measures. However, many of the methods used in these computations rely upon questionable assumptions such as stationarity or approximate linearity.

The Empirical Mode Decomposition of Huang et al.., [2], which preserves nonlinearity and non‐stationarity, has led to alternative signal processing techniques. We append to this growing set of techniques a well‐defined class of Weighting Functionals, WF. The strength is that they are easily applied to any number of time‐frequency transforms and ergodic/complexity measurements because the WFs rescale all the results according to the proportion of energy contained at the individual time‐scales.

The application to ergodic/complexity measurements has not been addressed in the context of Intrinsic Mode Functions, and is done so here for the first time. Our interest is to take these methods and demonstrate time dependence of the signal across multiple time‐scales in the comparison of normal controls and a variety of psychopathological and neuropathological conditions.

The article was first published at: AIP Conference Proceedings 1339: 275-281.

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This work was supported (in part) by the Fetzer Franklin Fund of the John E. Fetzer Memorial Trust.