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Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Published on: June 15, 2018

Mental states as macrostates emerging from brain electrical dynamics.

Carsten Allefeld¹, Harald Atmanspacher, Jirí Wackermann

¹Department of Empirical and Analytical Psychophysics, Institute for Frontier Areas of Psychology and Mental Health, Freiburg, Germany.

Chaos (Woodbury, N.Y.)

|April 2, 2009

Summary

This study proposes a new way to link psychology and physiology by viewing mental states as emergent from neural processes. An algorithm was developed to identify these emergent "macrostates" from brain activity data.

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Area of Science:

Neuroscience
Cognitive Science
Philosophy of Mind

Background:

Psychophysiological correlations are fundamental to many scientific disciplines.
The precise relationship between mental and neural processes remains incompletely understood.
A conceptual framework posits mental states as emergent from neural processes, offering dual descriptions (macro/microstates).

Purpose of the Study:

To introduce a method for coarse-graining system states to link psychological and physiological descriptions.
To empirically test the viability of this coarse-graining approach using real-world data.
To identify macrostates from physiological data that correspond to subjective mental states.

Main Methods:

Development of an algorithm to derive coarse-grained system states (macrostates) from finer-grained data (microstates).
Simulation of a dynamical system to validate the coarse-graining algorithm.
Application of the algorithm to electroencephalographic (EEG) recordings.

Main Results:

The algorithm successfully generated macrostates from simulated data.
Macrostates corresponding to mental states were identified in human electroencephalographic recordings.
The approach demonstrates a potential method for bridging psychological and physiological descriptions.

Conclusions:

The proposed coarse-graining method provides a viable approach to linking neural and mental states.
This framework supports the view of mental states as emergent properties of neural systems.
The findings have implications for understanding consciousness and developing new analytical tools in psychophysiology.