How can we live a fuller and healthier lifestyle as we get older? Perhaps keeping our body and brain engaged can help. That seems to be the case in Japan where the number of centegenarians is greater than 20,000.

THE ART OF AGING:THE LIMITLESS POTENTIAL OF THE BRAIN introduces a number of these “super-seniors” who lead healthy lives at nearly 100-years-old and, through them,searches for the “keys” to living a healthy and vital life regardless of age.

Click here to view the embedded video.

These results are from 300 seconds of linked ear EEG data, note the dominant slower alpha peak frontally…. And the raw coherence values of that linked ear data. The raw EEG file sample is also included, so you can see the waveforms these values are being drawn from.

Linked Ears

Raw Coherence Values of Linked Ear Data

Raw EEG

The same exact 300 seconds of EEG data, reprocessed now with the weighted average montage.  Note the difference in spectra, and waveform!!!  The temporal slower alpha is now seen as the source of that slower alpha content.

The alpha hypercoherence in the EEG is easily seen in this data, but not in the linked ears.

This shows that you need to find the EEG montage that shows the actual EEG data for your case first, and THEN calculate coherence.

Weighted Average Spectra

Weighted Average Waveform

Weighted Average Raw

The images below show the Spectral plot, coherence plot and raw EEGs. Just like the other montages did. The Cz coherences are so inflated with field effects they are at 0.8 across the full spectrum at some sites, obviously artifactually high.

Spectral Plot

Coherence Plot

Raw EEG

I suggested at the time that all the emotion was merely an example of someone yelling “the sky is falling”, like Chicken Little. There was no real problem, just lots of crying out and hand wringing.

I requested in an open international forum for anyone to send me a sample of the problem, and none could be produced. I suspected there was no real problem, as the sample issue was concerning a 500 sample/second device having a time skew… though this was in comparison to a database collected on a 100 sample per second device, with the waveforms interpolated from these samples.

It was highly suspect from my technical perspective when this issue was raised, and it was even more suspect when nobody could produce actual data showing the coherence or phase issue.

Testing now has shown that the old style Mitsar, with the non-simultaneous sampling is identical in performance to the new style amplifier that has simultaneous sampling, and thus no skewing error is possible in the newest amp.  There is also an intermediate style amplifier tested, which is one of the smaller amps, but with a more current sampling design.

The data clearly show that there is no difference in coherence between these devices.

The Mitsar amplifier also has been tested with the new BranMaster  Discovery amplifier, and it also was shown to have indenticle coherence findings with the Mitsar amplifier.

Clearly there is no real issue.

Data rules…. the experimental details are below.

Jay

We performed the following experiment.

We took three different Mitsar amplifiers:

1. Mitsar-EEG-201 – old model of amplifiers with relatively large time shift between channels (1.75 ms maximum) 2. Mitsar-EEG-201M – new model of amplifiers with relatively small time shift between channels (470 microsecond maximum) 3. Mitsar-EEG-202 – 32-channels amplifiers with zero time shift between channels.

Than we take Electro-Cap and put it on the head of one subject. We used linked ears referent for EEG recording.

We sequentially connect Electro-Cap and referents to three different amplifiers and perform independent recording of EEG in eyes closed condition. The duration of recording was longer than 300 seconds.

When we reconnect Electro-Cap and referents from one amplifier to another we do not touch to electrodes on the head and ears.

The total time of out experiment was approximately 20 minutes. This means the functional state of subject remain relatively stable.

Than we remontage the EEG to average referent (very important for time  shift influence measurements), compute the coherence for all three EEG recordings using the same processing parameters and compare them. The duration of time interval for processing was the same for these EEG recording and was equal to 300 seconds.

We do not find any dramatic differences in coherence corresponding to different amplifiers. The small fluctuations can be explaned by amplifiers noise and non-stationarity of EEG.

Mitsar Calibration

The Mitsar system is calibrated at the manufacturer. There is no need to recalibrate the amplifier unless there is a serious problem from damage. The calibration button is so the user can run a test calibration signal to demonstrate that the channels are in fact correct and equal. If these were ever not correct (or equal) then the manufacturer would recalibrate the hardware device. It is blocked so that a user cannot accidentally mess the calibrations up. So in summary the calibration button in the software is pressed and then the button to observe EEG is pressed. Then the test calibration signal is generated and can be recorded. This is a good idea if the case is a medical or legal evaluation so that when the data is presented as evidence there is validation that a microvolt equals a microvolt.

In Part 2 you see how Noah parents learn there are alternatives to Ritalin and other drugs that may be given to their child. Learn about how Neurofeedback and EEG Brain Mapping may be able to help without the use of dangerous pharmaceutical drugs.

Dr. Linden is a Clinical Psychologist and Nationally Certified in Neurofeedback and Biofeedback. He is the director of The Attention Learning Center, which has offices located in San Juan Capistrano, Irvine and Carlsbad, California.

Dr. Linden is a regular contributor to the Journal of Neurotherapy and has been a speaker in many seminars and conferences related to ADD/ADHD and neurotherapy.

The thalamic involvement in the generation of the alpha rhythm is being under-valued when looking at the LORETA images of alpha current source generators. The alpha power may come from the sources that LORETA identifies, but the thalamus is intimately involved in alpha rhythm generation, and this is not part of the LORETA image of the sources.

The polarization within the thalamus sets the base frequency of the alpha, but the cortical rhythm requires a complex multi-layer feedback loop from the thalamus to the cortex, and back to the thalamus. Without the cortex, there is a total disruption of the normal spatio-temporal distribution of the alpha wave’s spike trains within the thalamus, and cortical damage often disturbs coherence due to this mechanism.

The thalamus distributes the alpha posteriorly via specific sensory relays, which have a simple return circuit. Like the white matter relay from the lateral geniculate of the thalamus to the occipital lobe’s primary visual areas, and directly back. This thalamo-cortical-thalamic loop is relatively faster than the loop seen frontally. The frontal return circuitry is not simple, but the descending routes are complex and somewhat circuitous, taking more time, and thus it is common for the frontal lobe’s alpha to be at the slower end of the individual’s alpha frequency range. The frontal lobe has a return path through the striatum.

The five divisions of the frontal-striatal pathways are the motor circuit, the oculomotor circuit (from the frontal eye fields), the dorsolateral prefrontal circuit (cortical gating), lateral orbito-frontal circuit (emotive), and the anterior cingulate circuit (emotional and cognitive flexibility). The striatal-thalamic pathways are divided into two descending pathways which both start from the cortex to the head of the caudate and then the putamen, and then this pathway divides between the globus pallidus and substantia nigra, and then these both go to the thalamus. The thalamo-cortical completion of the circuit projects to both the premotor and motor cortex directly.

Not all circuits are simple thalamus-to-cortex-to-thalamus “echoic” returns to the original source…

A cortico-thalamo-cortical projection system exists which originates from the primary visual cortex, relayed by the lateral posterior nucleus of the thalamus, projecting to the suprasylvian visual area (which is involved in highest levels of visual integration and comprehension). This finding suggests that the thalamus modulates transmission of cortical signals from one cortical area to another… the coherence or “connectivity” of the cortex is not cortical-cortical, but cortical-thalamo-cortical.

With maturation, the cortex provides a stimulatory effect on the alpha frequency, raising it to a slightly faster frequency tuning through feedback to the thalamus, but the basic frequencies of alpha are generated by the reticular nucleus of the thalamus providing acetylcholine to the thalamic nuclei, and by the underlying polarization within the thalamus, which is effected by the NE levels from the brainstem, and by fluctuating DC field strength levels in the brain. The other effects are the thalamo cortical transmission times, and an effect of the cortical-thalamic processing time for any given pathway…. Longer time needed for frontal than posterior circuits.

Crudely stated: The frequencies of alpha are set in the thalamus, and the spatial and temporal distribution of alpha are controlled by the cortex, with rhythmic “initiation” (phase reset) done by the DC system’s “modulatory” influence on the AC rhythms of the EEG.

The thalamus can provide rhythms in the range from 3 to 16, with the common range of 8-12 representing an adult group’s “average”. Hyperpolarization of the thalamus slows the alpha, and hypopolarization speeds it up until it desynchronizes at about 16 Hz, and becomes a low voltage fast EEG.

The addition of some GABA (an inhibitory neurotransmitter) easily acquired with the addition of some alcohol will slow the alpha back into a rhythmic pattern. This basic mechanism is the reason alpha-theta training works so well on the low voltage fast EEGs seen so commonly in alcohol addiction.

LORETA may show a generator in the precuneus/cuneus area for the occipital alpha component, and the posterior cingulate for the parietal component (when alpha modulators are identified with ICA analysis and then source localized)… but these localizations miss the full beauty of the real mechanism’s complexity and especially the primary importance of the thalamus.

The thalamus gates our perceptions into “perceptual packets”, with the “thalamic gate” being open during the negative half-wave (up-side of the waveform), and less open during the positive half wave (the downward going half). Two stimuli presented within 75 to 100 milliseconds of each other will be “perceptually synchronized”, or though of as being instantaneously simultaneous.

The alpha frequency is the perceptual sampling rate… how many perceptual packets are evaluated per unit time, with a better semantic or declarative memory function seen with faster alpha frequencies. This is from the work on IAF (individual alpha frequency) from Professor Dr. Wolfgang Klimesch’s lab in Salzburg Austria, with significant contributions from Drs Michael Doppelmayr and Simon Hanselmayr.

The databases have difficulty characterizing alpha frequency tuning issues, with many identifying too much power at a slower frequency (like 7 Hz)… although the power values would be healthy and normal if alpha were only faster (like 9 Hz)… the databases seldom tell you it is merely 2 Hz slow. The normal alpha coherence values, if the alpha is slowed, are seen as hypercoherence, although they are perfectly normal for alpha. Databases that rely on predetermined band’s peak frequency may miss a shift if it exceeds their defined band, and this will miss the mean frequency if the peak is good but the band width has less faster content than slower content.

Faster alpha may cause similar issues (too much 12-15 Hz power or 12-15 Hz hypercoherence) when it is not “too much” of either that is really wrong, just alpha being too fast.

Thus when there are tuning issues, databases often have difficulty characterizing the core issue of tuning. When a tuning issue is noted, the coherence and power values may be “off” according to the database, when the real values are not really abnormal, just that they are too slow or too fast.

Theoretically, these issues may prove to be an area where Z-score training may have difficulty, flagging red herrings of power and coherence… though this is an empirical question that will be answered with time and experience

I’d like to just throw out there a few other findings that were discovered in a few exploratory investigations while working on some studies with our colleague Alicia Townsend, at the time at Univ. of North Texas. Lexicor funded these projects and now the arrangements are such that I can’t disclose more than was published in the abstracts from our talks at ISNR and AAPB.  I did at least want to point to these very preliminary findings because theoretically they are in concert with your explanations.

First, we explored 10 participants between the ages of 65 and 85 were recruited at the University of North Texas Health Science Center.  Each was diagnosed by the Alzheimer’s Disease Assessment Scale and a medical interview.  The aim of the study was to identify current source density markers in AD.  EEG recording of the eyes closed condition of an AD group was compared to an age-sex matched control group using within-subject multiple t-test procedures. sLORETA difference maps in nine frequency bands were investigated. Interestingly the results showed that there was a significant increase in current source density in the delta and theta bands in the Brodmann Area (BA) 39 of the right temporal lobe and BA 31, the cingulate gyrus respectively.  Additionally there were decreases in alpha in the BA 21 of the right temporal lobe and right inferior parietal lobule (Sherlin, Townsend & Hall, 2006).

This was corroborative previous findings of increased delta and theta and decreased alpha from a single case study of AD I analyzed with Tom Budzynski  (Budzyski, Budzynski, & Sherlin, 2002).  Results varied from previous studies that showed diffuse differences although the temporal lobe slowing is replicated.  We recognized that the proximity of the significant locations to the precuneus and fusiform gyrus which are both important in facial recognition and processing social information.  The precuneus is also involved in episodic memory retrieval and imagery of motor functions. A correlation study found similar patterns with sLORETA.

I believe that future investigation for patterns in different types of dementia (vascular vs. alzheimer’s vs. frontal lobe vs. mild cognitive impairment) may increase our ability to differentially diagnose.

The second study we completed was to examine the relationship between memory loss and brain electrical activity that was not AD diagnosable. Eighty-four participants between the ages of 50 and 85 were recruited for the original study. Participants were administered the Alzheimer’s Disease Assessment Scale – Cognitive (ADAS-Cog), a QEEG, and a clinical interview. The cross spectra was averaged and LORETA correlation maps.  Correlations were computed for each individual’s ADAS-Cog score compared to each voxel (7×7x7 mm) of their baseline sLORETA.

What we found were significant positive correlations between ADAS-Cog scores and frontal and parietal delta activity, and theta activity in the precuneus. Significant negative correlations were found between ADAS-Cog scores and temporal alpha. This corroborated prior findings and further alluded that as our memory continues to become impaired we expect frontal and parietal delta as well as anterior midline theta to increase. And that alpha will decrease as impairment grows (Townsend, Sherlin & Hall, 2006). This is exactly as you reported as expectations in the EEG.

Budzinski, T., Budzinski, H., & Sherlin, L. (2002).  Short and Long Term effects of Audio Visual Stimulation (AVS) on an Alzheimer’s Patient as documented by Quantitative Electroencephalography (QEEG) and Low Resolution Electromagnetic brain Tomography (LORETA) [Abstract].  Journal of Neurotherapy. Vol 6:1.

Sherlin, L. ,Townsend, A., & Hall, J. (2006). LORETA Analysis of Alzheimer’s Disease. [Abstract].  Journal of Neurotherapy. Vol 9:4.

Townsend, A., Sherlin, L., & Hall, J.  (2006).  LORETA and QEEG Correlations with the Alzheimer’s Disease Assessment Scale. [Abstract].  Journal of Neurotherapy. Vol 9:4.

The Foundation gives an award to the author(s) of the publication which has
“contributed the most to furthering the field of neurofeedback” during the
past year. Past recipients have included Drs Rob Coben, John Gruzelier, as
well as Johan Levesque and Mario Beauregard.

This year the Foundation has chosen Professor Dr. Juri Kropotov as recipient
of this years award, based on his book and the body of work Juri has
contributed over the years.

This award selection was announced recently at the EEG Spectrum Clinical
Interchange Conference in Los Angeles.  The award includes a gorgeous plaque
as well as an honorarium.

We salute both the Foundation for helping promote the fiend of NF, as well
as all the award recipients for their publications and the substantial
contributions they all have given to our field.

Our organization needs to provide information regarding the types of training/treatment that has been proven  over and over to help clients that have severe impediments to their lives.  If you feel similarly and would like to either sign this letter or write your own, it may cause some movement in APA and the Monitor to recognize the services we provide.

Merlyn Hurd PhD;BCIAC/EEG Fellow
Editor of NeuroConnections the ISNR/AAPB Neurofeedback division

Letter to APA regarding qEEG – March 09 2009

James H Bray PhD, President APA
Rhea K. Farberman, Executive Editor Monitor on Psychology
750 First Street, N.E.
Washington, DC 20002-4242

Dear Drs. Bray and Farberman,

Imagine the excitement of seeing “Brain Imaging” on the front of the Monitor for the March 2009 edition.  Finally, the APA is writing about QEEGs (quantitative electroencephalograms) and the types of work that is being done by thousands of psychologists in the neurofeedback world.

No, the first article is “A pacemaker for your brain?” which reviews the effect of deep brain stimulation (DBS) for the treatment of depression.  The second article “From the Research Lab” informed us about implanting electrodes on the surface of the brain to pinpoint where to surgically remove parts of the brain to correct the disorder from which the client was suffering.  The instrument discussed is fMRI. Perhaps I have not been keeping up with the areas of scope of practice for psychologists but this falls under medical procedures and few psychologists can afford an fMRI machine in their private office.  Yes, many psychologists work with fMRI’s in hospital settings, still it is a small number compared to the number of psychologists/neurofeedback therapists in the United States.

It is time the APA and the Monitor recognize the value of the thousands of psychologists around the world and approximately 3000 in the USA who use non invasive imaging techniques and treatments/training to reduce/eliminate epilepsy, traumatic brain injury; depression; ADHD;  and a host of other disorders.  As you probably know, early on, neurofeedback was, studied and researched in psychology laboratories,and is based on the principles of operant conditioning, which is a major area of development and focus for psychologists (beginning with its founder, psychologist B.F. Skinner) Some of the most seminal research in brain imaging and neurofeedback was and is conducted by psychologists ( for example Barry Sterman, Ph.D whose research led to the validity of neurofeedback, Joel Lubar, Ph.D. and Robert Thatcher, Ph.D.)Also early studies by Steven S. Fox, Ph.D. ( Univ. of Iowa, dept of psychology) with his 1967-1971 EEG/EP operant conditioning studies in cats and humans as well as Peter Rosenfeld, Ph.D. and Alan Rudell, Ph.D. who also published rigorous scientific studies on EEG/EP biofeedback in the late 1960s and early 1970s need to be recognized.

Do not be persuaded by the American Neurolog ical Academy’s viewpoint that QEEGs are not valid.  Recently, in a court case in NY State the QEEG was admitted as meeting Daubert criteria. This is one of many in the United States courts that have admitted QEEG’S as part of the defense.   It submitted thousands of studies using QEEG to verify its validity. The criterion used to locate the studies were computerized analysis of QEEG results. Also do not be persuaded that the Neurofeedback training is not valid, especially, when hundreds of studies have shown excellent outcomes.  These studies have been published in numerous journals.

Furthermore, Carl U. Weitman, Phd., F. BCIA-EEG chaired a liaison task force from 1995-97 between APA and AAPB that resulted in the APA practice directorate and APA council recognizing neurofeedback and QEEG as proficiencies within the scope of psychology; applied psychophysiology and biofeedback.  Among the results, state boards aligned their views with those of the practice directorate. In the interim, the practice code  90876 came to be “Psychophysiological Psychotherapy.
Perhaps you could see to having an edition that looks at Brain Imaging that employs the techniques that psychologists use and the training that they employ guided by those images and interpretations which are against normative databases.

Please log on to isnr. org; aapb.org; skiltopo.com and appliedneuroscience.com to review the studies, member’s lists and activities of these professional organizations.

Thank you for your kind attention to this letter.

Sincerely,

Merlyn Hurd PhD; BCIAC/EEG Fellow
Editor of NeuroConnections.. ISNR/AAPB Neurofeedback division Newsletter
Gerald Gluck Ph.D     Marvin H. Berman Ph.D.       Laurence R. Lewis Ph.D.
Alfred Collins Ph.D.  BCIA-EEG     Anne Ward Steven Ph.D.    David Cantor Ph.D.

Merlyn Hurd PhD; BCIAC/EEG Fellow
88 University Place, 8th Floor
New York, New York 10003
133526162
NYState Lic 7301
Tel: 212 807 8690

Technical Issues in qEEG Power point Presentation

Continue Reading to see parts 2 and 3.

Part 2 Discusses Z-Scores and Standard Deviations

Following the careful recording of the EEG, the quantitative analysis is begun with the sampling of the data to be used in the analysis by the Fourier transform. The Fourier analysis assumes there are no transients (epileptic discharges, episodic voltage changes etc.) or state changes (light sleep, drug effect, mental task, etc.), so these must be avoided when selecting data for analysis in qEEG for eyes closed resting database comparison. There are some eyes open and task databases available more recently (Hudspeth, Sterman, Duffy etc.)

Transients are an event with a rapid onset and ending, with an increase in amplitude of greater than 50% over the ongoing activity. Epileptiform activity is one common example of this phenomenon. The only time transient discharges may validly be included is when dipole localization or “mapping” of the sources of this activity is the intent, and in this circumstance only significant discharges should be sampled, with the ongoing background treated as the state change and eliminated.

Less rigorous analysis selection standards exist for data not intended for database comparison, such as reading or other task related data. Usually these task data will only be used for gross comparison to the more carefully collected steady state data, looking for gross changes in brain function. When there is an intent to compare to an eyes closed normative database, transients will increased the variability of the dataset, but will be averaged out in the mapping unless persistent or very prominent. These transients will simultaneously alter the dataset’s standard deviation from the norm.

State changes include typically sleep stages collected for an eyes closed awake database comparison. Stage 1 sleep is a subtle drowsy state, where people recorded will usually deny being drowsy when alerted. The alpha is beginning to wax and wane, with subtle increases in theta and occasionally slow rolling eye movements and a decreased EMG tonus. This is just prior to stage 2, where an object being held will slip from grasp, alerting the client, with most realizing the drowsing is present in stage 2. Many people doing a repetitive task or automatic pilot type task will be in stage 1 without being aware of time passing. This is a dangerous situation if response to change is needed, as there are delays in reaction time associated with this state.

The problem with stage 1 (drowsing) being added to a dataset is that it is a mixing of states, violating the FFT assumptions and making database comparison validity more than merely suspect. The inclusion invalidates comparison.

The task of artifacting is to sample enough data to provide reliable maps while maintaining the validity of the sampling, not taking state changes or transients. The amount of data acquired should provide highly repeatable or reliable mapping. The time required to achieve these results is different for each frequency.

Beta becomes reliable in the first 30 to 45 seconds, with alpha following at 60-90 seconds. The intermittent nature of theta makes it the least easily established reliability, with 120-180 seconds required. Delta is reliable at about 120 seconds. Our lab tries to get 120 seconds of data when possible.

Reliability may be established another way, with split-half replication. This actually looks at the lability within the sampled data by looking for the invariant similarity of the repeatable parts of the two data sets, with the more variable parts looked at with less confidence.

The time span or length of the epochs selected determines the sensitivity to the slower frequencies. The Fourier transform has to have a completed waveform in the data epoch sampling for the frequency to be detected and quantified. A 1/2 Hz low frequency sensitivity is thus achieved only when 2 second epoch lengths or longer are sampled. There is a downside to too long an epoch. It increases the likelihood of including state changes and transients, though when clean state stable data exists in long episodes, it should be sampled.

Some equipment has preset epoch lengths and interactions between the sampling rates and the epoch length, which is problematic in sampling data flexibly. The epochs in some equipment are not able to be adjusted in time to ’slide’ past artifacts, making the data artifacting such that it is difficult to sample the clean data in a record with intermittents like eye movement or other transients. Careful selection of the equipment should precede entry into this field.

The artifacting is mostly concerned with eliminating the more common artifacts of eye movement and EMG, as well as movement or electrode artifacts. This cleaning of the data is part of the art of doing good qEEGs, though the science of adequate sampling and the assumptions of the Fourier must be kept in mind.

New artifacts introduced by the digital processing.

The digital recording and processing of the raw analog waveform of the EEG should be understood in technical detail to properly interpret the resultant maps and numerical tables of findings.

The EEG is digitally converted from the analog data by an analog-to-digital or A-D converter and a resultant digital dataset is derived. The digitizing sampling rate and the bit length of the computer data will determine the resolution of the resultant image and tabular datasets. The faster the sampling rate, the faster the frequency that can be resolved, with a minimal sampling frequency defined by the Nyquist principle as 2 times the frequency being resolved.

Proper reproduction of the EEG for visual perspective requires a more conservative sampling rate than the 2:1 Nyquist ratio. This greater than 2:1 ratio must be set by the individual’s preferences. Few would choose less than 128 samples per second, most would prefer 500/second to 1000/second. (The manufacturer’s with set buffer sizes for the epochs, like the Lexicor, will however need to look at the impact in loss of lower frequency sensitivity. For this situation 128 to 256 is the highest reasonable choice.)

The channel sampling should be simultaneous, to avoid remontaging error or slew. If not simultaneous, a faster sampling rates will reduce this error (as will burst mode sampling) reducing the phase or time base error to a minimum.

The bit length of the computer “word” processed by the CPU effects the amplitude resolution in qEEG, irrespective the sampling rate. The longer the bit length, the better the resolution ( a bit length of 12 is acceptable, but 16 is preferred. Older units will have 8 bit processing and are not fully adequate without further scaling adjustments).

The epochs selected during artifacting will all have an abrupt start and stop, without a zero voltage point at each end of all the epochs. This sudden voltage is seen by the FFT (fast Fourier transform) of the computer analysis as a square wave at the start and stop of each epoch. The resultant output of the Fourier is that all frequencies were present at that point to “reconstruct” the square wave. This is termed “leakage artifact”, or “Gibb’s artifact”.

The result of the Gibb’s artifact is that if a 10 Hz waveform was put into the FFT, a spectral plot of the output would have a rise of the baseline where all frequencies were used to reconstruct these abrupt starts and stops of the epochs. There would be a spectral peaking at 10 Hz, with a tapered response and a broadened base to the frequency plot.

To correct for the leakage or Gibb’s artifact, a “windowing” filter is used. The result of this windowing is the return to the baseline of the generally elevated spectral plot mentioned previously. There is a residual broadening of the idealized spectral peak at 10 Hz. This residual artifact of the broadening of the spectrum following the windowing is “smearing” artifact.

The windowing used in qEEG is usually a Hanning window. This filter slowly ramps up the start and ramps down at the end, to avoid the apparent square wave the FFT sees. Other windowing techniques are triangular, Blackman, Hamming, Meyer’s , with the lack of windowing occasionally referred to as a “rectangular window”. A full discussion of these details, contrasting the different styles of windowing is outside the scope of this chapter.

“Aliasing” is an artifact caused by a frequency source near the sampling rate (and above the nyquist sampling rate) so that a beat frequency is created as an alias of the source frequency/sampling rate interaction. Aliasing filters are used to control for this artifact in all modern devices.

Database issues

The datasets derived from the artifacted EEG are the starting point for the comparison of these data points to the databases used in qEEG analysis.

The databases are the means and standard deviations used to establish the significance probabilities for the observed measurements. These will be represented as Z-Scores, roughly these may be seen as the standard
deviations from the normative database of the data points in the dataset. They are calculated as the patient mean minus the database mean, divided by the standard deviation of the population.

Z-scores are reported in tabular form and may be mapped. Significance probability mapping ( a term derived by Frank Duffy, M.D. of Harvard’s Children’s Hospital) allows the interpreting individual to view the spatial distribution and extent of deviation in an easily discernible display. 1.96 Z-score deviation is equal to 2 standard deviations, and 3.08 Z-score deviation is three standard deviations.

Normative databases are constructed with highly screened normal individuals with an age range establishing the limits of the database. The database is constructed controlling for socio-economic and other demographic influences. Importantly, the databases must be established with different norms for male and female, to account for the significantly different neurophysiologic structures and rates of development of the male and female brains.

The database used should be selected to match the end use and population to be seen. The age range may be critical for those seeing children or the elderly. For others, the presence of multivariate stepwise discriminants used in determining the likelihood of membership in one of two or more clinical groupings will be critical in the selection.

For others, the presence of an eyes open database for use in clinical eyes open work, or the task related data will be critical.

The database from E.Roy John, Ph.D. of NYU’s Brain Reasearch Laboratory contains more than univariate measures, with multivariate parametric statistical evaluations of normal and many clinical subgroups. This has stepwise discriminant analysis associated with it. Discriminants for head trauma are also available from Thatcher. The Duffy database is used, though is not is commercial distribution now that the Nicholet BEAM instrumentation does not carry it. Sterman has an age limited performance based database available, with new databases such as Hudspeth’s coming into availability recently as well.

In using the discriminants, if available in a database, care should be exercised to assure the applicability of the discriminant to the client being evaluated. The client must fit the conditions that were set for the construction of the discriminant.

If a discriminant is set up to decide the likelihood of being a member of group A or group B, a member of another group, C, will be classified as A or B, not properly identified as another type, type C. This weakness of discriminants must be controlled in the selection and use of the discriminant, not after it has been performed.

Displays

The displays in the qEEG report will be presented as a progressive analysis of the data. The displays may be sorted into those that are closer to and farther from the raw EEG, being farther from the EEG as more statistical manipulations are performed. The farther from the raw data one goes, the easier it is to mistakenly interpret artifacts as real or misinterpret relational data.

To avoid these easy and eventually certain mistakes, the full visual interpretation of the EEG must precede any review of the analyzed data. Only then should this be followed by a review of the raw amplitude mapping, in as detailed a frequency display as possible. This may be followed by broad band analysis. The amplitude mapping should be followed by the power and then the relative power analysis. Only after this step-wise evaluation this should absolute or relative power Z score or other database comparisons be done.

Following the spectral evaluation, the statistically extracted measures of symmetry, coherence and phase are evaluated, without being as likely to make a mistake.

The presence of artifact should be expected in clinical work. In clinical work the luxury of prolonged recordings and rejection from a study due to artifacts is not present as it is in academic situations. The progressive step by step evaluation will control for these situations as best they may be.

To err may be human, but it should be controlled and accounted for with methodologic routine to the extent humanly possible.