Information, Complexity and Communication

A Unique Approach to Training the EEG
Results of Acu-Tone™ on the EEG

Neurofeedback has traditionally focused on increasing or decreasing amplitude in particular defined bands, e.g. delta 1-4hz, theta 4-8hz etc.  There have been a few training protocols that have attempted to focus less on predefined bands.  Wide band suppression or “squash” protocols are an example of that.  In these protocols the goal is to reduce amplitude across the whole range of frequencies.  These protocols have traditionally focused on training one or two sites at a time.  The underlying assumption in amplitude training is that increasing or decreasing the amplitude at certain sites will enable the brain to function more efficiently.

Recently there has been increased interest in connectivity, coherence, and synchrony training.  These approaches focus on the ability of the brain to communicate across the cortex.  This is a measure of co-activation, does one location on the cortex activate a particular frequency range in concert with another.  The underlying assumption in connectivity training that there is an ideal amount of connectivity and that either too much or too little connectivity interferes with the brains ability function effectively.

Despite the differences in focus of amplitude and connectivity training they do have one component in common in that both are focusing on particular frequency bands and encouraging change in the specific values in those band.

In recent years there have been multiple studies of the EEG that have begun to quantify the brains electrical activity in new and different ways.

Some of the initial work was done in the field of anesthesia research where there was a desire to better quantify depth of anesthesia and levels of consciousness.  The ability to accurately ascertain level of consciousness is vital in surgery.  The previous physical signs that were used were crude and sometimes unreliable, with patients being either under sedated and experiencing pain or having memories of the surgery or over sedated and experiencing serious medical problems up to and including death.  The Bispectral Index and various computations of Entropy values have been used in measuring depth of anesthesia.  The Bispectral Index (BSI) is a proprietary algorithm, while the various formulations of Entropy are more widely available.  Both have been shown to be accurate measures of level of consciousness.  Several other measures have been created in order to find a metric that is similar to Bispectral Index.  Two of these are the Beta Ratio and Power Fast/Slow. Beta Ratio measures the change in gamma amplitude relative to the beta bands and Power Fast/Slow measures the change in gamma amplitude in relation to the total spectrum.  During anesthesia induction both these measures show changes closely correlated to changes in entropy and BSI*.   In both anesthesia and consciousness research entropy is defined as a measure of available cortical microstates.

Cortical microstates are brief, 100-200 millisecond, epochs of semi stable electrical behavior. Each one reflects the activation of a different network of neurons, and each corresponds to different states, from visual imagery to abstract thought, emotional feelings and face perception.

Consciousness Research and Theory

Recent study of consciousness has brought together the concepts of microstates, complexity, differentiation, and synchronous behavior.

Microstates.

There has been a great deal of research on the Microstate theory of consciousness.

Dietrich Lehmann and his colleagues have researched the make up of cortical microstates.  In more than a dozen studies they have identified differences in microstates depending on the task at hand and have begun to develop a database of normative microstates.  They see the development and dissolution of these microstates as the “building blocks of consciousness”.  Conscious experience depends on the ability to “string together” multiple microstates.  They believe that what we experience as a continuous stream of consciousness actually consists of separate blocks, which follow each other rapidly, involve disparate regions of the cortex and implement different identifiable mental actions and functions.

Lehmann, Thomas Koenig, and E Roy John have published a paper that details normative microstates across developmental stages.

Alexander and Andrew Fingelkurts have accomplished similar research in which they describe “meta-stability” in the EEG.  They describe a process of neurons synchronizing into neural assemblies or pools.  These pools can further synchronize into meta-stable patterns that can then combine to create more complex patterns.  Meta-stability is the ability of autonomous areas of the brain to momentarily enter into coordinated activity.  They have been able to map these and categorize these meta-stable states.  These states are seen as occurring against a backdrop of “multivariabiltiy” or complexity.

EEG Complexity

Complexity in the EEG measures the degrees of freedom or the independence of the variables in a system.   While synchronous activity has long been known to be an essential part of the brains ability to process information, synchrony alone is not sufficient.  Seizure activity and anesthesia induced sleep show a great deal of synchronous behavior.  These states are clearly not ones in which information processing is maximized.   

Several researchers have claimed that complexity is another essential component of conscious processes.  Complexity can be seen in the time domain and spatial domains.   Timing based complexity involves the independence of oscillations of all frequencies in a specific data stream or from a specific location.  Spatial complexity refers to the independence of activity between different sites.  That is to say no one site’s activity is overly dependent on another site’s activity.

A temporally complex EEG signifies greater cortical freedom.   The ability of the cortex to rapidly form and dissolve microstates is maximized.

Spatial complexity is a measure of differentiation in the EGG.  No one site’s activity is overly reliant on another.  A spatially complex EEG allows for the rapid formation and dissolution of communication networks across the cortex.

Spectral entropy is one common measure of temporal complexity in the EEG and mutual information is a measure of spatial complexity (differentiation) across areas of the cortex.

Along with complexity and differentiation, communication between brain areas is also important to robust brain function.

Several recent studies have shown that the gamma activity at distant sites seen under cognitive task is linked to increases in synchronous activity in other bands.  In one such study electrodes were placed directly on the cortex of patients undergoing surgery to address intractable seizure activity.  Patients are awake during this type of surgery as they have to perform different tasks to ascertain if the surgeons are probing the proper areas.   EEG was measured from multiple surface electrodes during those tasks.  One finding of this study was that the activation of gamma waves at distant sites was accompanied by an increase in synchronous theta between those sites.  It was speculated that synchronous theta is the mechanism that supports the activation of gamma at distant sites.

It is the combination of temporal complexity, spatial complexity, and communication/co-activation across distant sites that is essential for a productively functioning brain.

Dr. George Martin © 2007 ____________________________________________________________________________

Rose Marie Raccioppi, Carl Florin, Carmine Franzese                                          5/2/12

Results of Acu-Tone on the EEG:

Selected Indexes:  Average Correlation of amplitude (uV) & BSI

Correlation of Averages (total) @ 5 minute intervals in all bands Pre-values

In Statistics, the Pearson product-moment correlation coefficient (sometimes referred to as the PPMCC or PCC, or Pearson's r, and is typically denoted by r) is a measure of the Correlation (linear dependence) between two variables X and Y, giving a value between +1 and −1 inclusive. It is widely used in the sciences as a measure of the strength of linear dependence between two variables. It was developed by Karl Pearson from a similar but slightly different idea introduced by  Francis Galton in the 1880s.

                       -1= perfect negative correlation, 0 being no correlation and +1 being Perfect correlation

Pre-Values:                                       Post Values:                                                                            

Delta:    .710                                      Delta:  .648

Theta:   .350                                      Theta:  .279

Alpha:   .109                                      Alpha:  .136

SMR:     .03                                        SMR:   .034

15-19:    .075                                      15-19:   .056

19-23:    .053                                      19-23:    .061

23-38:   .014                                       23-38:    .002

38-42:   -.033                                      38-42:   -.011

A/T:       .273                                      A/T:       .210

BSI:       .200                                      BSI:       .158

BSI                                                       BSI 

Left   Hemi:  30.4                              Left   Hemi: 66.6

Right Hemi:  41.7                             Right Hemi: 100.7

Discussion:

           There is there nothing apparent that stands out with the Correlation data so far. Prior to test there could have been many other ways to choose what to correlate. There was no apparent way to know beforehand which measures would be most relevant for this particular condition.  However we are now in a position to contextualize our results within a theoretical framework excerpted from this paper by Dr. George Martin. The information he has distilled from the top researchers representing the most advanced work on conscious states & EEG analysis. He provided this information freely to promote his EEG Entropy protocols for Neurofeedback training.  I have excerpted the relevant parts of this paper here for our analysis.

         

BSI was very significant in both hemispheres but significantly more so in the right hemisphere. Since Entropy correlates to BSI fairly well, it stands to reason that out of all the possible measures we might examine, the relevant ones for BSI are likely to be the same or similar as the graphed indexes above shown to be highly correlated to Entropy. This is based on the fact that BSI changed dramatically in our experiments and BSI is correlated to Entropy. Entropy is correlated to the other Indexes shown above in graphs: Amplitude, dominant frequency, variability. Our results have provided evidence to guide us in a specific direction narrowing down the choices of measures to these few. We may now proceed with more confidence these other variables may also be relevant to our goal.

             

 EEG MEASUREMENT INDEXES:

I. Complex measures:

1.Entropy

2.M.I. (Mutual Information)

3.Complexity

II. Basic Measures Measuring changes in :

1. Band Amplitude (total Average)  

2. Ave Correlation (left right hemispheres)

3. Variability

4. Dominant Frequency

III. Chosen Measures For Acu-tone

1. Correlation of energy between Left & Right (Synchrony)

2. BSI (Bispectral Index) or (Gamma/Spectrum ratio)

IV. Other Possibly relevant Measures Specific to Acu Tone

    (shown in graphs below):

Graphs of Indexes Correlated to Entropy: click image for larger view