<p></p>
<h2></h2>
<h3><a name="_Toc35343001">I. Neuroguide</a></h3>
<iframe src="https://player.vimeo.com/video/410223335?h=d32379c382&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>Robert Thatcher’s Neuroguide database was collected at the University of Maryland in 1990, where initially a sample of 625 normal subjects constituted the normative database. This database included clinical selection criteria, an age range of 2 months
    to 282 years, cross-validation tests, demographics.</p>
<p><br>The NeuroGuide database has been tested and re-tested and the sensitivity of the statistical distributions has been calculated for each montage and condition. Normalcy was determined by response to a neurological history questionnaire which was given
    to the child’s parents and/or filled out by each subject. IQ and other age appropriate psychometric testing, academic achievement, as well as classroom performance as determined by school grades and teacher reports also were used in determining normalcy.
    Nine hundred and forty-three (943) variables were computed for each subject including measures of absolute and relative power, coherence, phase, asymmetry, and power ratios. Z-score transforms are available in single Hz bins. Sliding averages were
    used to compute age-appropriate norms. Results were inspected for Gaussian distribution (for further details see Thatcher, Walker, Biver, North, &amp; Curtin, Sensitivity and cross-validation of a lifespan normative EEG database — at: http://www.applied
    neuroscience.com/LSNDB.htm.) Recording with task challenges was not performed. The construction and composition of this database are relatively well documented.<br>The NeuroGuide software allows the user to import previously recorded EEG data, edit
    and analyze data, and process the data into maps and necessary statistics. Neuroguide is built from 625 normative comparisons of relative power, absolute power, power ratios, coherence, and phase and amplitude asymmetry.<br>Some of the attributes
    of Neuroguide include automatic artifact rejection, dynamic FFTs with comparisons to FFT lifespan, and Eyes Open and Eyes Closed Norms covering ages from birth to 82 years of age. NeuroGuide includes a Traumatic Brain Injury and Learning Disability
    discriminant functions, as well as an integrated statistical package. It also includes predictions of neuropsychological test scores using the Brain Performance Index.</p>
<p><br>One of the tools is EEG Neuroimaging to evaluate Brodmann Areas and nodes and connections of functional networks in the brain likely linked to symptoms, statically or in real-time.</p>
<p><br>NeuroGuide provides a retrospective view of EEG waveforms and offline artifact rejection. NeuroGuide also offers calculations of Spectral Energy, Power and Coherence along with discriminant Functions, Signal Generator, LORETA Norms, and Brain Performance
    Index (QEEG Information).</p>
<p><br>NeuroGuide uses a Fast Fourier Transform (FFT) with .5Hz and 1Hz resolution to provide color headmaps with a bandwidth of 0-40Hz, a Joint-Time-Frequency Analysis component to identify time events greater or less than a threshold value, as well as
    LORETA analysis. Montage selection and creation are allowed, as well, including bipolar montages, current source density montages and average reference montages in eyes closed and eyes open conditions.
</p>
<p><br>The&nbsp;mathematical&nbsp;details&nbsp;of&nbsp;complex&nbsp;demodulation&nbsp;used&nbsp;to&nbsp;compute&nbsp;the&nbsp;instantaneous&nbsp;Z&nbsp; scores&nbsp;as&nbsp;contained&nbsp;in&nbsp;the&nbsp;Applied&nbsp;Neuroscience,&nbsp;Inc.&nbsp;“dll”&nbsp;are&nbsp;published&nbsp;in&nbsp;Otnes&nbsp;and&nbsp;<br>Enochson,&nbsp;(1977);Granger&nbsp;and&nbsp;Hatanaka,&nbsp;(1964);&nbsp;Bloomfield,&nbsp;2000;&nbsp;Thatcher&nbsp;et&nbsp;al,&nbsp;<br>(2007).&nbsp;Complex&nbsp;demodulation&nbsp;is&nbsp;a&nbsp;time&nbsp;domain&nbsp;digital&nbsp;method&nbsp;of&nbsp;spectral&nbsp;analysis&nbsp;<br>whereas&nbsp;the&nbsp;fast&nbsp;Fourier&nbsp;transform&nbsp;(FFT)&nbsp;is&nbsp;a&nbsp;frequency&nbsp;domain&nbsp;method.&nbsp;&nbsp;These&nbsp;two&nbsp;<br>methods&nbsp;are&nbsp;related&nbsp;by&nbsp;the&nbsp;fact&nbsp;they&nbsp;both&nbsp;involve&nbsp;sines&nbsp;and&nbsp;cosines&nbsp;and&nbsp;both&nbsp;operate&nbsp;in&nbsp;<br>the&nbsp;complex&nbsp;domain&nbsp;and&nbsp;in&nbsp;this&nbsp;way&nbsp;represent&nbsp;the&nbsp;same&nbsp;mathematical&nbsp;descriptions&nbsp;of&nbsp;the&nbsp;<br>power&nbsp;spectrum.&nbsp;&nbsp;The&nbsp;advantage&nbsp;of&nbsp;complex&nbsp;demodulation&nbsp;is&nbsp;that&nbsp;it&nbsp;is&nbsp;a&nbsp;time&nbsp;domain&nbsp;<br>method&nbsp;and&nbsp;less&nbsp;sensitive&nbsp;to&nbsp;artifact&nbsp;and&nbsp;it&nbsp;does&nbsp;not&nbsp;require&nbsp;windowing&nbsp;nor&nbsp;even&nbsp;integers&nbsp;<br>of&nbsp;the&nbsp;power&nbsp;of&nbsp;2&nbsp;as&nbsp;does&nbsp;the&nbsp;FFT.&nbsp;&nbsp;&nbsp;The&nbsp;FFT&nbsp;integrates&nbsp;power&nbsp;in&nbsp;a&nbsp;frequency&nbsp;band&nbsp;over&nbsp;<br>the&nbsp;entire&nbsp;epoch&nbsp;length&nbsp;and&nbsp;requires&nbsp;windowing&nbsp;functions&nbsp;which&nbsp;can&nbsp;dramatically&nbsp;effect&nbsp;<br>the&nbsp;power&nbsp;values&nbsp;whereas,&nbsp;as&nbsp;mentioned&nbsp;previously,&nbsp;complex&nbsp;demodulation&nbsp;does&nbsp;not
    require&nbsp;windowing&nbsp;(Otnes&nbsp;and&nbsp;Enochson,&nbsp;1972).&nbsp;&nbsp;&nbsp;Complex&nbsp;demodulation&nbsp;was&nbsp;computed&nbsp;
    <br>for&nbsp;the&nbsp;linked&nbsp;ears&nbsp;and&nbsp;eyes&nbsp;open&nbsp;and&nbsp;eyes&nbsp;closed&nbsp;conditions&nbsp;for&nbsp;all&nbsp;625&nbsp;subjects&nbsp;in&nbsp;the&nbsp;<br>normative&nbsp;database.&nbsp;</p>







































<h2><a name="_Toc35343002"></a><a name="_Toc33954368">II. SKIL</a></h2>

<p>Information in this description taken from<br><i>Use of Databases in QEEG Evaluation, </i>Jack Johnstone, PhD &amp;<i> </i>Jay Gunkelman, QEEG-D</p>
<p><br>The SKIL database currently includes 135 adults ranging from 18 to 55 years old (see Sterman &amp;<br> Kaiser, 2001). No normative information is currently available for children or young adults. SKIL does not consider age as a factor in computing
    z-score deviations. The reference population is comprised of students and laboratory personnel (50%), volunteers recruited from the community (25%), and U.S. Air Force personnel (25%). Screening was based on questionnaire regarding medical history,
    drug use, and recent life events.</p>
<p><br>&nbsp;The SKIL database incorporates recordings at rest (eyes closed and open) and during task&nbsp; challenges involving audio-visual information processing and visual-spatial tracking. A correction for the time of day of recording is available which
    is based on combined cross-sectional and longitudinal data rather than the preferred method of tracking within-subject changes over time.&nbsp; The SKIL database covers a restricted range of frequencies, from 2 Hz to 25 Hz. This method deletes signiﬁcant
    slow and fast frequency data which may be of clinical importance. However, the database does have the advantage of providing norms for each single Hz increment over this frequency range. The SKIL database relies exclusively on the linked ear reference.
    The SKIL analysis has not received FDA 510(k) clearance and is labeled as “not intended for medical use.”</p>
<p><br> &nbsp;The SKIL database does not have a measure of EEG coherence but rather includes a similarity measure termed “comodulation,” which is quite like coherence but does not yield a measure of phase.&nbsp; Comodulation is essentially the correlation
    of the spectrum for two recording electrodes over time using a sliding one second data window moved in 250 millisecond increments. Although this effectively deals with windowing issues, it also is clear that the degree of correlation between electrode
    sites is not computed on independent, but rather overlapping, spectral analyses.</p>
<p><br> &nbsp;Since the SKIL database relies exclusively on the linked ear reference, the comodulation similarity measure is strongly inﬂuenced by the fact that both sites are connected to a common source. This can lead to shared variance, which can cause
    erroneous comodulation results. Nevertheless, the comodulation metric is currently being explored for possible clinical utility.
</p>
<p><br> &nbsp;As discussed previously, there must be a balance between the number of individuals in a database and the number of variables used in assessment to account for multiple statistical tests. The SKIL database has the advantage of a large number
    of features (e.g., multiple conditions, 1 Hz bins) but has the disadvantage of a relatively small number of individuals represented in the database. This tends to increase the number of false positive ﬁndings. A solution to the problem of false positive
    results is to replicate ﬁndings on independent samples of data from the individual patient.
</p>

<p><i></i></p>











<h3><a name="_Toc33954369">Image of Bands of Power in SKIL</a></h3>
<h2><a name="_Toc35343003"></a></h2>
<p></p>
<p style="text-align: center;"><a name="_Toc33954369" style="background-color: rgb(255, 255, 255); color: rgb(82, 96, 105); font-size: 1rem;">&nbsp;<img src="@@PLUGINFILE@@/SKIL.png" alt="SKIL" width="538" height="439" style="text-align: center; " class="img-responsive atto_image_button_middle"></a></p>
<a name="_Toc33954369">
    <p style="text-align: center;"><br></p>
</a>
<p></p>
<h3><a name="_Toc33954370" style="color: rgb(55, 71, 79); font-size: 2.143rem; background-color: rgb(255, 255, 255);">Image of Theta Unity measure in SKIL</a></h3>
<p></p>
<p style="text-align: center;"><a name="_Toc33954370" style="background-color: rgb(255, 255, 255); color: rgb(82, 96, 105); font-size: 1rem;">&nbsp;<img src="@@PLUGINFILE@@/Theta%20UNity.png" alt="Theta Unity" width="500" height="467" style="text-align: center; " class="img-responsive atto_image_button_middle"></a></p>
<p></p>
<p><br></p>
<p><br></p>
<p></p>
<h3><a name="_Toc33954371">Image of spectra at each site from SKIL</a></h3>
<h2><a name="_Toc35343005"></a></h2>
<p></p>
<p style="text-align: center;"><a name="_Toc33954371" style="background-color: rgb(255, 255, 255); color: rgb(82, 96, 105); font-size: 1rem;">&nbsp;<img src="@@PLUGINFILE@@/Spectra%20in%20SKIL.png" alt="Spectra in SKIL" width="600" height="312" style="text-align: center; " class="img-responsive atto_image_button_middle"></a></p>
<a name="_Toc33954371">
    <p style="text-align: center;"><br></p>
</a>
<p></p>
<h2><a name="_Toc35343006" style="color: rgb(55, 71, 79); font-size: 2.143rem; background-color: rgb(255, 255, 255);">III. iSync Brain Database (South Korean Database)</a><br></h2>
<div style="padding:56.25% 0 0 0;position:relative;"><iframe src="https://player.vimeo.com/video/429332161?h=cd9ca06aab&amp;badge=0&amp;autopause=0&amp;player_id=0&amp;app_id=58479" frameborder="0" allow="autoplay; fullscreen; picture-in-picture" allowfullscreen="" style="position:absolute;top:0;left:0;width:100%;height:100%;" title="JAY - KOREAN DATABASES, FREQUENCIES"></iframe></div>
<script src="https://player.vimeo.com/api/player.js"></script>
<p><a href="https://vimeo.com/manage/429332161/general">Jay Gunkelman on the iSync Brain Database</a><br></p><iframe src="https://player.vimeo.com/video/415200171?h=7de4021202&amp;color=13979b&amp;title=0&amp;portrait=0" width="640" height="360" frameborder="0" allow="autoplay; fullscreen; picture-in-picture" allowfullscreen=""></iframe><br>

<p>iSync uses independent component analysis (ICA), a statistical method to separate independent sources from superimposed signals. It is the most common method that has been used in EEG data decomposition, and can be used to identify and remove the artifacts
    from raw EEG data. </p>

<p>The iSync database initially cleans out artifact with the application of high-pass and low-pass filers. This reduces distortions (artifacts) from common physiological sources such as face or eye muscle movements, as well as extrinsic non-physiological
    sources such as electrical devices, power lines, and poor electrode contact. This is the only database currently on the market (2020) that uses the automatic cleaning process (one can choose not to have this process undertaken).</p>

<p>Features in the iSyncBrain database include time series, power spectrum density (PSD), component scalp map (Topomap), dipole source location (Source location) extracted from ICA are shown for each component.</p>

<h3><a name="_Toc35343007"></a><a name="_Toc33954365">Independent Components Analysis</a></h3>

<p>Independent components are spatially and spectrally independent variables in the EEG. Independent component analysis (ICA) is a statistical method to separate independent sources from extracerebellar signals. It is the most common method that has been
    used in EEG data decomposition, and can be used to identify and remove the artifacts from raw EEG data. Features including time series, power spectrum density (PSD), component scalp map (Topomap), and dipole source location are extracted from ICA
    are shown for each component. iSyncBrain computes the EEG and breaks it down into its component parts, represented in components that are ranked by their variability. </p>
<h3><a name="_Toc35343008">Image of an Independent Component
from iSyncBrain Quantitative Database</a></h3>
<p style="text-align: center; "><a name="_Toc35343008"><img src="@@PLUGINFILE@@/isync.png" alt="isync" width="743" height="305" class="img-responsive atto_image_button_text-bottom"><br></a></p>
<p><a name="_Toc35343008"></a></p>
<h2><a name="_Toc35343009"></a><a name="_Toc33954366">Subcortical Power Findings in iSyncBrain</a></h2>
<p style="text-align: center;"><img src="@@PLUGINFILE@@/isync2.png" alt="Subcortical Power Findings" width="600" height="519" style="font-size: 1rem;" class="img-responsive atto_image_button_text-bottom"></p>
<p></p>
<p><a name="_Toc35343008"></a></p>
<h2><a name="_Toc35343010"></a><a name="_Toc33954367">Connectivity Findings in the EEG in iSync Brain</a></h2>
<p></p>
<p style="text-align: center;"><a name="_Toc33954367" style="background-color: rgb(255, 255, 255); color: rgb(82, 96, 105); font-size: 1rem;">&nbsp;<img src="@@PLUGINFILE@@/Connectivity%20in%20isync.png" alt="Connectivity in isync" width="600" height="491" style="text-align: center; " class="img-responsive atto_image_button_middle"></a></p>
<a name="_Toc33954367">
    <p style="text-align: center;"><br></p>
</a>
<p></p>
<p><br></p>
<p><a name="_Toc35343008"></a></p>
<h2><a name="_Toc35343011">ICA Cleaning as seen in iSyncBrain</a></h2>
<p style="text-align: center; "><a name="_Toc35343011"><img src="@@PLUGINFILE@@/ICA%20Cleaning.png" alt="ICA Cleaning in iSYNC" width="676" height="493" class="img-responsive atto_image_button_text-bottom"><br></a></p>
<p><a name="_Toc35343011"></a></p>
<h2><a name="_Toc35343012">IV. WinEEG</a></h2>
<p><a name="_Toc35343012"></a><br></p>
<iframe src="https://player.vimeo.com/video/410219568?h=0f72277857&amp;color=13979b&amp;title=0&amp;portrait=0" width="640" height="360" frameborder="0" allow="autoplay; fullscreen; picture-in-picture" allowfullscreen=""></iframe>
<p><a href="https://vimeo.com/410219568">Juri Kropotov, Phd - NF</a> from <a href="https://vimeo.com/user102563893">NeuroField Neurotherapy</a> on <a href="https://vimeo.com">Vimeo</a>.</p>
<p>WinEEG Advanced software allows for the recording, editing and analysis of continuously recorded EEG using a Mitsar amplifier and will also process data recorded in other formats. Raw EEG can be exported to files with different formats such as ASCII,
    Binary, EDF, EDF+, UDF, ASCII, LEXICOR, and NeuroScan v.4.</p>

<p>EEG spectra, coherence, ERP, ERD and parameters of task performance can be exported to ASCII file automatically for collection of recordings selected by user.</p>

<p>The EEG (up to 32 channels) is stored on the hard disk and available for subsequent analysis. During recording, the signals are continuously displayed on the screen. The software emulates the “moving” paper mode. EEG can be recorded synchronously with
    visual and acoustic stimuli presentation for ERP/ERD studies. An automatic impedance measurement built into the program helps to control a quality of electrodes setting.</p>

<p>Recorded EEG can be displayed in different montages: monopolar, bipolar, average reference, weighted average reference and etc. EEG can be filtered by IIR digital filters to change frequency band of recording to and eliminate environmental noise (50 or
    60 Hz). </p>

<p>WinEEG allows for epoch selection and rejection for the cleaning of artifacts, as well as Advanced artifact correction procedures based on PCA or ICA decomposition of raw EEG and spatial filtering.</p>
<p>The use of ICA is helpful for finding spatially and spectrally independent components in the data.&nbsp;</p>
<p><a href="https://www.youtube.com/watch?v=t5epryuAHHo">Juri Kropotov on ICA</a></p>

<p>WinEEG software includes the multichannel spectral analysis, brain mapping and coherence. Power spectra and coherence can be computed for any selected part of recorded EEG. Different parameters of spectra computed for predefined frequency band ranges
    can be displayed as histograms, maps and tables. .</p>

<p>Build in equivalent dipole localization algorithm help to identify a brain position of source of paroxysmal activity and 3D mapping of data can be performed by LORETA software to which mapped data is transferred automatically.</p>
<p><br></p>
<h2><a name="_Toc35343013">Image from WinEEG Software</a></h2>
<p style="text-align: center;"><img src="@@PLUGINFILE@@/WinEEG%20Software.png" alt="WinEEG Software" width="1048" height="588" style="font-size: 1rem;" class="img-responsive atto_image_button_middle"></p>
<p></p>
<p></p>
<p><a name="_Toc35343008"></a></p>
<h2><a name="_Toc35343014">Image from WinEEG Software</a></h2>
<p style="text-align: center; "><a name="_Toc35343014"><img src="@@PLUGINFILE@@/WinEEG%20Software%202.png" alt="Spectra in WinEEGE " width="650" height="513" class="img-responsive atto_image_button_text-bottom"><br></a></p>
<h2><a name="_Toc35343015">Image from WinEEG Software</a></h2>
<p><a name="_Toc35343015"><img src="@@PLUGINFILE@@/winEEG3.png" alt="WinEEG 3" width="1094" height="616" style="text-align: center; " class="img-responsive atto_image_button_middle"></a></p>
<p style="text-align: center;"><a name="_Toc35343015"><br></a></p>
<p></p>
<p><br></p>
<h2><a name="_Toc35343016" style="color: rgb(55, 71, 79); font-size: 2.143rem; background-color: rgb(255, 255, 255);">Human Brain Institute Database</a><br></h2>

<p>The HBI database was developed by Juri Kropotov to help researchers record and analyze raw EEG, quantitative EEG and quantitative ERPs studies. The HBI database is built in WinEEG software.</p>
<div style="padding:56.25% 0 0 0;position:relative;"><iframe src="https://player.vimeo.com/video/429327602?h=2ac543ead1&amp;badge=0&amp;autopause=0&amp;player_id=0&amp;app_id=58479" frameborder="0" allow="autoplay; fullscreen; picture-in-picture" allowfullscreen="" style="position:absolute;top:0;left:0;width:100%;height:100%;" title="JAY - ERP, JURI (with intro and outro)"></iframe></div>
<script src="https://player.vimeo.com/api/player.js"></script>
<p>The HBI database includes the data from over 3000 EEG recordings collected from more than 1000 healthy subjects from ages 7 to 89 years old. EEGs were recorded at seven different conditions: Eye Opened, Eye Closed tasks and during performing 5 different
    tasks using for ERP analysis such as Visual Continuous Performance Task (VCPT), Auditory Task, Reading Task, Mathematical Task and Mismatch Negativity Task.</p>

<h2><a name="_Toc35343017">ERPs</a></h2>
<p>Professor Juri Kropotov on Event Related Potentials</p>
<div style="padding:56.25% 0 0 0;position:relative;"><iframe src="https://player.vimeo.com/video/593333759?h=30c241bf2e&amp;badge=0&amp;autopause=0&amp;player_id=0&amp;app_id=58479" frameborder="0" allow="autoplay; fullscreen; picture-in-picture" allowfullscreen="" style="position:absolute;top:0;left:0;width:100%;height:100%;" title="Event Related Potentials 101 with Juri Kropotov"></iframe></div>
<script src="https://player.vimeo.com/api/player.js"></script>

<p>In Event Related Potentials analysis, brain responses (i.e. evoked potentials) to psychological tasks are decomposed into independent components. The components are associated with distinctive psychological operations. Comparing the amplitude and latency
    of the components with the normative data gives insights into the different stages of information processing in patients.</p>
<h2><a name="_Toc35343018">Pictures for a Visual Event Related Potentials Test in HBI</a></h2>
<p style="text-align: center;"><img src="@@PLUGINFILE@@/HBI%20Images.png" alt="HBI ERP" width="450" height="357" class="img-responsive atto_image_button_text-bottom" style="font-size: 1rem;"></p>
<p></p>
<p><a name="_Toc35343008"></a></p>
<h2><a name="_Toc35343019">Image from HBI Database</a></h2>
<p style="text-align: center; "><a name="_Toc35343019">&nbsp;<img src="@@PLUGINFILE@@/HBI3.png" alt="Image from HBI Database" width="595" height="527" style="text-align: center; " class="img-responsive atto_image_button_middle"></a></p><br><br><br>
<p>The HBI database includes the average spectra, average coherence, average event related potentials (ERPs) and their variance computed for three different referents: linked ears referent, average referent and weighted average referent.
</p>
<h2><a name="_Toc35343020">Image from HBI Database</a></h2>
<p style="text-align: center; "><a name="_Toc35343020"><img src="@@PLUGINFILE@@/hbi2.png" alt="Deviations from Normality" width="443" height="496" class="img-responsive atto_image_button_middle"><br></a></p><br>
<h2><a name="_Toc35343021">BrainDx</a></h2>
<iframe src="https://player.vimeo.com/video/410218678?h=3a387513bc&amp;color=13979b&amp;title=0&amp;portrait=0" width="640" height="376" frameborder="0" allow="autoplay; fullscreen; picture-in-picture" allowfullscreen=""></iframe>
<p><br></p>

<p>BrainDx was formed by internationally recognized Neurophysiological researchers and clinicians via research conducted at the Brain Research Laboratories of New York University School of Medicine. </p>

<p>BrainDx is a modular system that allows evaluation of brain function on many levels of analysis which can assist in the classification of an individual in terms of psychiatric disorders and neurological conditions. BrainDx is an annual subscription that
    expires one year from purchase and needs to be renewed.</p>

<p>BrainDx uses 3-D source localization methods, the mathematically most probable underlying sources of the scalp recorded data can be identified, and produces an interpretive report that can be used by clinicians to document neurofunctional status related
    to an individual's behavior and performance capacities.</p>

<p>Part of the report generation process includes comparing the results of a patient's brain functional profile to a database of age matched normal individuals. Then, incorporating information from the patient's history, statistical comparisons are made
    to the appropriate clinical databases comparing the QEEG profile of the individual patient to those with known disorders, assessing the probability that the patient statistically resembled such patients, and thereby assisting the diagnostic process.
</p>

<p>For example, if a patient presents with general complaints about memory loss or says that their work is not going well, the BrainDx system can objectively evaluate their brain function relative to age expected normal values, and suggest the statistical
    probability that the EEG profile seen in this patient is consistent with that of a patient with dementia, rather than a patient with clinical depression. </p>

<p>A non-invasive, 20-30 minute QEEG brain evaluation can generate with statistical accuracy the probability that a patient with particular symptoms matches others in the database with similar symptoms and an objective diagnosis. For example, in the case
    of pediatric concerns, differentiate Learning Disorder (LD) from Normal or Attention-Deficit/Hyperactivity Disorder (ADHD) from Autism Spectrum Disorder (ASD). ,etc. In addition BrainDx provides the ability to evaluate brain function, when the patient
    is in two different states; a quantitative assessment of the change in brain function between the two states, for example, on and off a medication.</p>

<p>The BDx Report generator software produces an interpretive report based upon Neurometric Analysis as well as sLORETA analysis. This tool for quantitative electroencephalography (qEEG ) is a software-based, healthcare technology that purports to facilitate
    the diagnosis and treatment monitoring of a number of psychiatric, developmental, and neurological brain disorders.</p>

<p>BrainDx accepts raw 19 channel EEG data from most major manufacturers. It has automatic artifact rejection, allows for various montages, and creates maps for all age ranges.&nbsp; The metrics of analysis include Absolute Power, Relative Power, Phase,
    Asymmetry, Coherence and Frequency.</p>

<p>It also has a unique 3D, rotational volumetric visualization of the brain as mapped from the qEEG interpretation through the high resolution z-score sLORETA algorithm.</p>

<h2><a name="_Toc35343022">BrainDx LORETA Imaging</a></h2>
<p style="text-align: center;"><img src="@@PLUGINFILE@@/BrainDx%20LORETA%20Imaging.png" alt="BrainDx LORETA IMAGING" width="748" height="573" class="img-responsive atto_image_button_text-bottom" style="font-size: 1rem;"></p>
<h3><a name="_Toc35343023">An Image of Raw Data in BrainDx</a></h3>
<p style="text-align: center; "><a name="_Toc35343023">&nbsp;<img src="@@PLUGINFILE@@/Raw%20Data%20in%20Brain%20Dx.png" alt="Raw Data in BrainDx" width="896" height="682" style="text-align: center; " class="img-responsive atto_image_button_middle">&nbsp;</a></p>
<p style="text-align: center;"><a name="_Toc35343023"><br></a></p>
<p></p><br><a name="_Toc33954361" style="color: rgb(55, 71, 79); font-family: &quot;Open Sans&quot;; font-size: 1.715rem; background-color: rgb(255, 255, 255);">Image of Summary Page from BrainDx</a><br>
<p></p>
<p style="text-align: center;"><a name="_Toc33954361" style="background-color: rgb(255, 255, 255); color: rgb(82, 96, 105); font-size: 1rem;">&nbsp;</a></p><a name="_Toc33954361"></a>
<p style="text-align: center;"><a name="_Toc33954361"></a><a name="_Toc33954361" style="color: rgb(82, 96, 105); font-size: 1rem;">&nbsp;<img src="@@PLUGINFILE@@/Summary%20Page%20Brain%20Dx.png" alt="Summary Page BrainDx" width="851" height="602" style="text-align: center; " class="img-responsive atto_image_button_middle"></a></p><br><br><br><br>
<h2><a name="_Toc35343025"></a><a name="_Toc33954362">Image from BrainDx</a></h2>
<p style="text-align: center;"><span style="font-size: 1rem;">&nbsp;&nbsp;</span></p>
<p style="text-align: center;">&nbsp;<img src="@@PLUGINFILE@@/Image%20from%20BrainDx.png" alt="Z-score in Brain Dx" width="1021" height="733" style="font-size: 1rem; text-align: center;" class="img-responsive atto_image_button_middle"></p>
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