Entropy and EEG

The entropy formula


Entropy measures the spontaneous dispersal of energy: how much energy is spread out in a process, or how widely spread out it becomes

A data set with high entropy has a broad distribution.
A data set with low entropy has a narrow or peaked distribution.

EEG entropy was first utilized in anesthesia research as a measure of loss of consciousness.
As a person loses consciousness the level of cortical entropy decreases.
As a person regains consciousness the level of entropy increases.

A higher level of consciousness is related to an increased number of cortical microstates.
A brain with high entropy value has more available cortical microstates.

High Entropy = Awake brain => ‘Freedom’
Low Entropy = Comatose brain => ‘Prison’

Link to collection of papers on Entropy and Microstates

high entropy phase
Phase plot of a signal with high entropy

entopy image 1

A spectrum with high entropy. Notice the broad, relatively flat spectrum

entopy image 2

A spectrum with low entropy. Notice the high peak.

The following three images are from minutes 2, 8, and 13
of an entropy training session.
Notice the shift in the spectrum into the higher frequencies
and the changes in the phase plot.

session 2min
Minute 2

session 8min
Minute 8

session 13min
Minute 13

The following images show changes in entropy, dominant frequency,
and variability during an entropy training session.

entopy review

entopy variability

entopy dom freq