Introduction to Live Complexity Training - Always Under
Last modified: Feb 5, 2021
Your life exhibits complexity!
Shouldn’t your physiological signals?
Live complexity training (LCT) is a theory driven
implementation of Technology-Assisted Self-Regulation /
Unlike neurofeedback and biofeedback procedures for
management of diagnostic categories, LCT employs a
transdiagnostic biomarker of sickness and wellness
behavior as a “pointing-out instruction”, i.e., feedback.
Changes in complexity in physiological measures (e.g.,
EEG, HRV, GSR, fNIR, MRI, MEG) are often easy to see
on screen as they occur. They are more difficult to
describe mathematically and statistically.
Observable complexity in activities of body, feelings, and
mind increases during successful recovery from sickness
as well as in adaptive maturation.
Signal complexity increases in order to transmit and
process our accumulation of memory and skillful means.
Complexity is only generated by the body-mind when
there is healthy balance between excitation and inhibition
(“self-organized criticality”) and when there is adequate
neuroplasticity and neuroprotection.
Complexity increases as we develop gracefulness and
problem solving abilities and express wellness behavior.
Complexity decreases as a result of most forms of
Complexity without synchrony is randomness; Synchrony
without complexity is rigidity..
I introduced Live Complexity Training in 2015 as an expansion
of my earlier development of TAG Sync neurofeedback around
In 2015 I used a Kuramoto oscillator model to suggest three
fundamental modes that identified the continuum between
wellness behavior (adaptive development) and sickness
behavior. This continuum is transdiagnostic (independent of
diagnosis). Adaptogens cause movement to the left.
By monitoring this biomarker one can evaluate the results of
various interventions. During the COVID pandemic this is a
particularly important issue since traditional face-to-face
diagnoses are often not possible. A transdiagnostic biomarker
may be preferable to the problem of multiple comorbidities.
Adaptive development and maturation into skillful awakeness
can be derailed by combinations of factors ranging from
transgenerational epigenetic factors and early life adversity to
trauma, toxins, and autosuggestion. Having a tool to point out
the return of wellness behavior may sometimes be more
fundamental than fumbling over the current ICD and DSM
codes for multiple comorbidities.
When we are growing up, when we are waking up, when we
are evolving from the cradle to the council our physiological
signals such as EEG, HRV, GSR, etc., show more and more of
a signal I call Sizzle (see illustration above). When you zoom in
to the complexity of wellness behavior you see the inner story -
more complexity - just like when you dissect living tissue. By
contrast when you suffer from delayed development, sleep
deprivation or sickness behavior your EEG, when zoomed in
upon, shows fast waves riding on slow waves - the EEG of
sickness behavior. Since these signals are easy to see visually,
and even easier to catch using digital quantification, why not
use Technology-Assisted Self-Regulation / Self-Realization
(TASR) to document which actions promote sickness behavior
and which actions promote awakeness and skillful means?
A good theory gives you testable predictions and is consistent
with trends in current literature. Theories are neither true nor
false, they are simply useful/skillful or not.
Using a Kuramoto Oscillator model I have shown that
phsyiological signals have three main earily seen variations
that I call 1) sizzle, 2) tsunami, and 3) sickness behavior. Below
is an illustration of how this transdiagnostic biomarker fits in
with current models of vigilance and sleep states.
Below the green-yellow-red line above, to the right of the blue
vertical line, you see the typical “states of loss of vigilance”,
deepening to the right. They are descriptions of changes in the
EEG signals associated with behavioral changes. They
correspond to loss of fully awake connectedness and to deep
disconnections characteristic of both sleep and sickness. I
have added the the state of adaptive complexification (“Sizzle”)
as a new component of the standard vigilance models.
The client often shifts reflexly and habitually along the
continuum, suffering from the changes throughout the day, but
in general not mindful of the rise and fall of the different states.
In Live Complexity Training the client learns to recognize and
regulate such changes as shown below. The spectral displays
below show the same client before LCT on right and after LCT
on the left.
EEG authorities describe the healthy awake EEG as “almost
random” and with no apparent patterns. I describe it as like the
random sizzling of water in a frying pan. The texts are very
clear that the common patterns of sickness behavior such as
chronic encephalitis are generally patterns of fast waves riding
on slow waves.
You can see in the spectral display of sizzle (above left) that
there is low energy both above and below the neat 10 Hz alpha
peak. In contrast on the right above is the dissipative EEG of
canonical sickness behavior (SBeh). In SBeh energy has
entered the regions below and above the 10 Hz control signal. I
call these regions the lower and upper alpha skirts respectively.
This transdiagnostic continuum is characteristic of many of the
physiological rhythms used for biofeedback. It is called the 1/f
signal - "one over f". F stands for frequency. The important
thing is that signal used to be called 1/f noise and pink noise. It
is indeed almost random like white noise, but it is embued with
a hierarchical organization of dominant power in the lower
frequencies. This may be seen as “cross-frequency coupling”
(CFC). You can see in the above illustrated continuum that
movement toward sickness behavior is accompanied by the
appearance of higher power CFCs.
At the same time that we want to inhibit the CFCs and
excessive lower and upper alpha skirt activity seen in Sickness
Behavior, we want to enhance the brain’s long distance
synchrony in and around the main control frequencies. These
include the Meyer-Traube-Herring wave at 0.1 Hz and the 10
Hz alpha EEG signal.
With Technology-Assisted Self-Regulation / Self-Realization
(TASR) we can construct feedback software that helps us to be
mindful of state changes (cf, Abhidharma). Below are screen
shots of Live Complexity Training software for 3 common
systems - Thought Technology, Nexus, and BioExplorer. Each
screen shot has a link to a page where you can view or
download the related Operations Manual, Installation Guide,
Screen Shots, etc.
Thought Technology: [Enlarge Image] [View Documents]
Nexus by MindMedia: [Enlarge Image] [View Documents]
BioExplorer: [Enlarge Image] [View Documents]
Manuals and documentation are available for all three versions
Please use these resources to research what I call “the
sickness in the signal”. It is usually obvious the raw EEG.
LCT Resources - Complexity [Download PDF]
LCT Resources - Transdiagnostics [Download PDF]
LCT Resources - Sickness Behavior [Download PDF]
Copyright © 2010, 2015, 2021 by Douglas Dailey
There is a complexity-based transdiagnostic
biomarker of sickness behavior. Adaptogens
move the physiological signal away from
sickness behavior and toward adaptive