Mental States as Macrostates Emerging from EEG Dynamics

TL;DR

This paper proposes a method to derive mental states as macrostates from EEG data, supporting the idea that psychological phenomena can emerge from neural dynamics, and demonstrates its application on real EEG recordings.

Contribution

It introduces a novel algorithm for coarse-graining EEG data to identify mental states as emergent macrostates, bridging neural activity and psychological states.

Findings

Successfully identified EEG-derived macrostates corresponding to mental states

Demonstrated the method on simulated and real EEG data

Supports the emergent view of mental phenomena from neural processes

Abstract

Correlations between psychological and physiological phenomena form the basis for different medical and scientific disciplines, but the nature of this relation has not yet been fully understood. One conceptual option is to understand the mental as "emerging" from neural processes in the specific sense that psychology and physiology provide two different descriptions of the same system. Stating these descriptions in terms of coarser- and finer-grained system states (macro- and microstates), the two descriptions may be equally adequate if the coarse-graining preserves the possibility to obtain a dynamical rule for the system. To test the empirical viability of our approach, we describe an algorithm to obtain a specific form of such a coarse-graining from data, and illustrate its operation using a simulated dynamical system. We then apply the method to an electroencephalographic (EEG) recording, where we are able to identify macrostates from the physiological data that correspond to mental states of the subject.