Entropy analysis of EEG offers insight into how meditation influences brain state

Electroencephalography (EEG) recording of cortical brain signals has been widely used within psychology and neuroscience to investigate brain network dynamics responsible for various cognitive processes or connected to mental and neurological disorders. Recently, a physics-inspired "brain states" perspective has provided additional success, by modeling major neuronal processing networks like physical states of matter and the brain as a material often operating in a coexistence state with two more network dynamics. Akin to observing changes in a phase by cooling a material, we characterized temporal and statistical changes in brain network dynamics after specific cognitive and calming activities. Specifically, we explored various entropy metrics as viable parameters for distinguishing between brain states, and we offer additional insights into how such metrics can be used to learn how different spatiotemporal configurations of neuronal synchronization relate to changes in behavior, cognition, and consciousness. In this paper, we explain entropy from the perspectives of physics and Information Theory (IT) and its applications for neural time-series data. We then present our methodologies for several time series entropy metrics which we use to analyze continuous EEG recordings of participants' resting brain states, primarily before and after brief meditation. To conclude, we consider the theoretical and philosophical implications of utilizing an entropic approach to better understand brain states and dynamics.