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__

Phase plot of a signal with high entropy

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

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.

Minute 2

Minute 8

Minute 13

The following images show changes in entropy, dominant frequency,

and variability during an entropy training session.