<br><iframe src="https://player.vimeo.com/video/443179794?h=810cdfcb58&amp;color=13979b&amp;title=0&amp;portrait=0" width="640" height="360" frameborder="0" allow="autoplay; fullscreen; picture-in-picture" allowfullscreen=""></iframe>
<p><br></p>
<p></p>
<h2><br></h2>
<h4>Quantitative EEG analysis (qEEG) refers to signal processing and extraction of features from the EEG signal, in some cases there is a normative group that the individual being surveyed is compared to, and sometimes the data is digitized without a comparative
    reference.
</h4>
<p>In the typical neurotherapy practice, 19 channel EEG is digitized, edited or adjusted to remove extra cerebral artifact, and subjected to spectral analysis using the fast Fourier transform (FFT).&nbsp;</p>
<p><img src="https://schoolofneurotherapy.com/classroom/pluginfile.php/476/mod_lesson/page_contents/361/Fast%20Fourier%20Transform.png" alt="Fast Fourier Transform" width="1400" height="948" class="img-responsive atto_image_button_text-bottom"><br></p>
<p><b></b></p>
<h3><b>Inclusion and Exclusion Criteria for Normative Databases</b></h3>
<div>
    <div>
        <p></p>
        <p><b>Normative databases are constructed and validated based upon the scientific standards of gaussianity, cross-validation, amplifier matching and peer-reviewed publications. A clinically normal database is one where individuals are carefully screened for relevant abnormalities using other clinical tools such as psychometric assessment or MRIs.&nbsp;</b></p>
        <p><b>The database should represent the mix of ages, gender, ethnicity, socioeconomic status and other demographic factors present in the overall population.&nbsp;<b>“Representative sampling”</b>&nbsp;means a demographically balanced sample of different
            genders, different ethnic backgrounds, different socio-economic status and different ages.&nbsp;</b>
        </p>
        <p><b>Using a rigorously screened population, deviations represent the difference from “well-functioning” as compared to “average.” Poor screening procedures result in admitting individuals into a normal group who have significant clinical problems. Careful screening of the subjects that comprise a normative database is critical so that representative samples of healthy and otherwise normally functioning individuals are selected and individuals with a history of neurological problems, psychiatric problems, school failure and other deviant behaviors are excluded.&nbsp;</b></p>
        <p><b>While it may be too expensive and exhaustive to require that all individuals have full MRI, PET, fMRI, complete neuropsychological evaluation, genetic analysis, blood and urine testing, etc., it would be ideal. A second-best option, then, is to get questionnaire information and objective assessments of general health, and social and intellectual functioning. It is especially important to get many children sampled in a normative database, because dramatic developmental changes occur in children and adolescents in relatively short time intervals. Where two years difference will not alter the demographics of an adult, it can signify important differences in, say, a 4-year-old and a 6-year-old.</b></p>
        <p><b>It is important for a database to have a large sample size, although ultimately this criterion is less important than careful calibration, elimination of artifact, rigorous standards during data collection and rigorous standards for data analysis and approximation to a Gaussian distribution. To be considered a clinically valid normative database, there must be peer review (published), tests of reliability, tests of statistical validity, cross-validation tests, full disclosure of the inclusion and exclusion criteria, and adequate sample sizes for different age groups. Normative databases must use healthy participants who do not have any psychological or physiological pathologies.</b></p>
        <p><b>Many normative databases available for clinical use contain the mean values of EEG features (absolute power, relative power, coherence measures, etc.); the “normative” quantification of theta data is the standard deviation of the features relative to the normal population (see John, Prichep, &amp; Easton, 1987). Certain databases provide magnitude, (the square root of power), while others provide amplitude (which is power measured in microvolts squared).&nbsp;&nbsp;</b></p>
        <p><b>Relative power represents the percentage of power in any bands compared with the total power in the patient’s EEG (e.g., “relative theta” is the percentage of theta of the combined sum of delta, theta, alpha, and beta). One way to describe relative power is “how the client spends their time.”&nbsp;</b></p>
        <p><b>Other derived measures that have been normed include hemispheric asymmetry for power (which often reflects the power differential between different sites in the brain), coherence (the stability of the phase relationship between electrode sites, and phase (the measure of time delay between activities at two sites). It should be recognized that these measures are not statistically independent, and significant deviations on more than one feature may be representing the same neurophysiological process (i.e. hypercoherence in alpha may also be represented in a global alpha amplitude deviation, due to a contaminated reference). &nbsp;</b></p>
        <p><b><a href="https://vimeo.com/443179794" target="_blank">Click here to watch a brief in depth video of normative databases</a></b></p>
        <p></p>
        <h2><b><a name="_Toc35342982">Asymmetry in Quantitative EEG Maps (NeuroGuide)</a></b></h2><b><br><p></p><p><img src="https://schoolofneurotherapy.com/classroom/pluginfile.php/476/mod_lesson/page_contents/362/Asymmetry.png" alt="Asymmetry" width="500" height="378"></p><h2><br></h2><p><a name="_Toc35342982"><img src="https://schoolofneurotherapy.com/classroom/pluginfile.php/476/mod_lesson/page_contents/362/Coherence.png" alt="Coherence in WinEEG" width="291" height="270"><br></a></p><h4><a name="_Toc35342983">Coherence measures in Quantitative EEG Maps (WinEEG and iSyncBrain)</a></h4><img src="https://schoolofneurotherapy.com/classroom/pluginfile.php/476/mod_lesson/page_contents/362/Coherence%202.png" alt="Asymmetry" width="427" height="379"><br><p></p><p>While some normative databases rely on a linked ears reference, the savvy clinician will reference a database that has multiple montage possibility (in the case of contaminated reference).&nbsp;Coherence measures the stability of the phase relationship between two time series, calculating the difference between two signals measured at the scalp. &nbsp;Coherence in excess is hypercoherence; coherence in deficiency is hypocoherence.&nbsp;Coherence measures do not separate phase and amplitude.&nbsp;</p><p>Unfortunately, coherence measures are also apt to be spurious and false positives are likely.&nbsp;In EEG measures, a linked ears or mastoid reference used can significantly alter the both the power and phase at each electrode site, if a high amplitude source of EEG is at one or more of the temporal electrodes. For similar reasons, any common reference coherence data must be interpreted cautiously. Even in average references, spuriously large coherences can occur from superficial dipoles at large interelectrode differences.&nbsp;&nbsp; Fein et al. (1988) argue the most diligent way to measure coherences is with reference-free recording methods, while Nunez et al (1997) agree, noting that Laplacians give the best intermediate spatial range coherence estimates.&nbsp; Finally , of import to valuation of synchrony measures offered, coherence measures do not separate phase and amplitude.<br></p><p><br></p><h4>Erroneous Coherences (Image Below)</h4><p></p><p></p><h2><a name="_Toc35342984"><img src="https://schoolofneurotherapy.com/classroom/pluginfile.php/476/mod_lesson/page_contents/362/Erroneous%20Coherences.png" alt="Erroneous Coherences" width="700" height="683"><br></a></h2><h2><br></h2><h4><a name="_Toc35342984">Phase Measures in Quantitative EEG Maps (Neuroguide)</a></h4><p><img src="https://schoolofneurotherapy.com/classroom/pluginfile.php/476/mod_lesson/page_contents/362/Phase.png" alt="Phase" width="501" height="379"></p><p></p><p></p><h4><a name="_Toc35342985">Quantitative “Normative” and Non-Normative Database Validation Steps</a></h4><p>A quantitative database should represent the mix of ages, gender, ethnicity, socioeconomic status and other demographic factors present in the overall population. The method of recruitment of normal individuals also should be considered. Poor screening procedures will result in admitting individuals into a normal group who have signiﬁcant clinical problems. Regardless the method of recruitment, care must be exercised not to entice individuals to participate and falsify information because of the ﬁnancial gain. Normative databases are to be constructed and validated in respect to the basic scientific standards of gaussianity, cross-validation, amplifier matching and peer-reviewed publications.</p></b></div>
</div>
<h4><b>Statistical Analysis of Normative Databases</b></h4>
<div>
    <div>
        <p></p>
        <p><b>Spectral power is an often used measure in qEEG studies. The amount of power for each frequency or frequency band for a given electrode is computed and compared to the database mean value. The results are often represented as z-scores, that is the difference between the mean score of a population and the patient’s individual score divided by the standard deviation of the population. Z-scores detect and characterize potential abnormalities from the tested individual, relative to the normative population within his/her own age range. It is not strictly the number of deviations, rather the pattern of deviations that is most relevant. The use of parametric statistics assumes a normal (Gaussian) distribution of the variable(s) in question.</b></p>
        <p><b>The&nbsp;Gaussian&nbsp;distribution&nbsp;is&nbsp;a&nbsp;fundamental&nbsp;distribution&nbsp;that&nbsp;is&nbsp;used&nbsp;throughout&nbsp;science,&nbsp;for&nbsp;example,&nbsp;the&nbsp;Schrodinger&nbsp;wave&nbsp;equation&nbsp;in&nbsp;Quantum&nbsp;mechanics&nbsp;uses&nbsp;the&nbsp;Gaussian&nbsp;distribution&nbsp;&nbsp;as&nbsp;basis&nbsp;functions&nbsp;(Robinett,&nbsp;1997).&nbsp;&nbsp;The&nbsp;application&nbsp;of&nbsp;the&nbsp;EEG&nbsp;to&nbsp;the&nbsp;concept&nbsp;of&nbsp;the Gaussian&nbsp;distribution&nbsp;requires&nbsp;the&nbsp;use&nbsp;of&nbsp;standard&nbsp;mathematical&nbsp;transforms&nbsp;by&nbsp;which all statistical&nbsp;distributions&nbsp;can&nbsp;be&nbsp;transformed&nbsp;to&nbsp;a&nbsp;Gaussian&nbsp;distribution&nbsp;(Box&nbsp;and&nbsp;Cox,&nbsp;1964). Quantitative EEG uses transforms that are applied to the power spectrum of the digital time series in order to approximate a normal distribution.</b></p>
        <h4><b><a name="_Toc35342987">Gaussian distribution</a></b></h4>
        <p><b>&nbsp;<img src="https://schoolofneurotherapy.com/classroom/pluginfile.php/476/mod_lesson/page_contents/363/Gaussian%20Distribution.png" alt="Gaussian Distribution" width="500" height="364"></b></p>
        <p></p>
        <p><b><br></b></p>
        <p><b><br></b></p>
        <p><b><br></b></p>
        <p><b>Here is a video describing Gaussianity:&nbsp;</b></p>
        <div>
            <div>
                <div tabindex="-1" title="Gaussianity" preload="auto" id="id_videojs_6269e5c9d0c4d_1" data-setup-lazy="{&quot;techOrder&quot;: [&quot;youtube&quot;], &quot;sources&quot;: [{&quot;type&quot;: &quot;video/youtube&quot;, &quot;src&quot;:&quot;https://www.youtube.com/watch?v=IIuXF5QRBTY&quot;}], &quot;language&quot;: &quot;en&quot;, &quot;fluid&quot;: true}" lang="en" role="region" aria-label="Video Player">
                    <div><iframe id="id_videojs_6269e5c9d0c4d_1_Youtube_api" frameborder="0" allowfullscreen="1" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" title="YouTube video player" width="640" height="360" src="https://www.youtube.com/embed/IIuXF5QRBTY?controls=0&amp;modestbranding=1&amp;rel=0&amp;showinfo=0&amp;loop=0&amp;fs=0&amp;hl=en&amp;enablejsapi=1&amp;origin=https%3A%2F%2Fschoolofneurotherapy.com&amp;widgetid=1" data-gtm-yt-inspected-10="true"></iframe>
                        <div></div>
                    </div>
                    <div tabindex="-1" aria-disabled="false"></div>
                    <div aria-live="off" aria-atomic="true"></div><b><button type="button" aria-live="polite" title="Play Video" aria-disabled="false"><span aria-hidden="true"></span>Play Video</button></b></div>
            </div>
        </div>
        <p></p>
        <p></p>
        <p><b>The next two figures are representations of raw, un-normed data, and Z-score data, respectively, in spectral graphs. The raw image shows the absolute power of the EEG in microvolts squared. We see that 10Hz shows up at approximately 16mV<sup>2&nbsp;</sup>. The second image shows the data in Z-scores, or how deviant from the norm these frequencies are. We can see that the purple line (representing one electrode) is 3+ standard deviations above the norm between 14 – 16 Hz, and 4+ standard deviations above the norm at 28 Hz. Both of these findings are potentially artifactual, but this cannot be determined without an analysis of the raw EEG.</b></p>
        <h4><b><a name="_Toc35342988">Spectral Power in Microvolts squared (y-axis) and hertz (x-axis)</a></b></h4>
        <p><b>&nbsp;&nbsp;</b></p>
        <p><b>&nbsp;<img src="https://schoolofneurotherapy.com/classroom/pluginfile.php/476/mod_lesson/page_contents/363/Spectral%20Power.png" alt="Spectral Power" width="599" height="553"></b></p>
        <p></p>
        <p><b><br></b></p>
        <p></p>
        <h4><b><a name="_Toc35342989">Spectral Power in Z-Scores (y-axis) and hertz (x-axis)</a></b></h4>
        <p><b>&nbsp;<img src="https://schoolofneurotherapy.com/classroom/pluginfile.php/476/mod_lesson/page_contents/363/Z-Score%20Spectral%20Power.png" alt="Z-Score Spectral Power" width="595" height="541"></b></p>
        <p></p>
        <p><b><br></b></p>
        <p></p>
        <p><b>The next figure we see is showing, one graph per electrode channel, the raw data in the EEG on a spectral display. This imagery is from WinEEG. Each peak represents a rhythm in the data, showing kurtosis, or the expected bell shape that indicates it is a frequency range.</b></p>
        <p><b><br></b></p>
        <h4><b><a name="_Toc35342990">Spectra of Raw EEG (not norm referenced) as seen at each electrode in WinEEG</a></b></h4>
        <p><b>&nbsp;<img src="https://schoolofneurotherapy.com/classroom/pluginfile.php/476/mod_lesson/page_contents/363/WinEEG%20Spectra.png" alt="WinEEG Spectra" width="1000" height="636"></b></p>
        <p></p>
        <p><b><br></b></p>
        <p></p>
        <p><b>The figure below illustrates a common modern quantitative EEG analysis where EEG traces are viewed and examined at the same time that quantitative analyses are displayed so as to facilitate and extend analytical power.</b></p>
        <p><b><br></b></p>
        <p></p>
        <p></p>
        <h4><b><a name="_Toc35342991">Raw EEG and Quantitative EEG</a></b></h4>
        <p></p>
        <p><b><a name="_Toc35342991"><img src="https://schoolofneurotherapy.com/classroom/pluginfile.php/476/mod_lesson/page_contents/363/Raw%20vs.%20Quantitative.png" alt="Raw EEG vs. Quantitative EEG" width="1000" height="604"></a></b></p><b><a name="_Toc35342991"><p><br></p></a></b></div>
</div>
<p><b>Here is a video on Fast Fourier Transform:&nbsp;</b></p>
<div>
    <div><b><div tabindex="-1" title="FFT Video" preload="auto" id="id_videojs_6269e57b9fde2_1" data-setup-lazy="{&quot;techOrder&quot;: [&quot;youtube&quot;], &quot;sources&quot;: [{&quot;type&quot;: &quot;video/youtube&quot;, &quot;src&quot;:&quot;https://www.youtube.com/watch?v=spUNpyF58BY&quot;}], &quot;language&quot;: &quot;en&quot;, &quot;fluid&quot;: true}" lang="en" role="region" aria-label="Video Player"><div><iframe id="id_videojs_6269e57b9fde2_1_Youtube_api" frameborder="0" allowfullscreen="1" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" title="YouTube video player" width="640" height="360" src="https://www.youtube.com/embed/spUNpyF58BY?controls=0&amp;modestbranding=1&amp;rel=0&amp;showinfo=0&amp;loop=0&amp;fs=0&amp;hl=en&amp;enablejsapi=1&amp;origin=https%3A%2F%2Fschoolofneurotherapy.com&amp;widgetid=1" data-gtm-yt-inspected-10="true"></iframe><div></div></div><div tabindex="-1" aria-disabled="false"></div><div aria-live="off" aria-atomic="true"></div><button type="button" aria-live="polite" title="Play Video" aria-disabled="false"><span aria-hidden="true"></span>Play Video</button></div></b></div>
</div><b>&nbsp;</b>
<p></p>
<p>There are numerous clinical normative databases for many different medical comparisons; normative databases help to evaluate clinical disorders. &nbsp;The National Library of Medicine’s database has a comprehensive list of normative databases, that can
    be found by search for “normative database: at:&nbsp;<a href="http://www.ncbi.nlm.nih.gov/sites/entrez">http://www.ncbi.nlm.nih.gov/sites/entrez</a>. In using a normative database, we are comparing a single person to a population of “normal” individuals
    for comparative purposes. A normative database will not only identify frequencies or metrics in which the patient is not normal, but will also identify the degree of their deviance.</p><br><br>
<p></p>