Assessment Demonstration
What You Will Learn in This Chapter
This unit demonstrates how the assessments you use at intake, streamlined abbreviated Q recordings and comprehensive 19-channel evaluations, become tools for ongoing assessment when you repeat them during training. You will see how clinicians collect, process, and interpret EEG data, and then how they record and compare a second assessment so that progress, or the lack of it, can be shown to the client with evidence rather than impression. Whether you work in a VA medical center, a hospital neurology department, or a private clinic, these skills form the foundation of effective neurofeedback practice.
Whether you are beginning your neurofeedback training or refining your assessment expertise, these demonstrations will guide you through the step-by-step process of transforming raw EEG recordings into clinically actionable information. By the end, you will know how to plan a reassessment, match its recording conditions to the baseline, and decide whether a difference between the two reflects real change.
BCIA Blueprint Coverage: This unit addresses VI. Patient/Client Assessment - D. Assessment Demonstration: Perform a basic EEG assessment, an abbreviated Q recording and/or attaching electrode cap and completing an abbreviated Q or 19-channel QEEG recording.
Learning Objectives
After completing this section, you will be able to:
Describe the purpose and procedure of an abbreviated Q recording.
Explain the difference between clinical databases and normative databases for EEG assessment.
Identify the key steps in conducting a 19-channel EEG recording with normative database comparison.
Describe the process of visual inspection, artifact rejection, and data extraction from EEG recordings.
Plan a repeat abbreviated Q or 19-channel assessment whose recording conditions match the baseline recording.
Distinguish meaningful pre-post EEG change from measurement error, session-related state effects, and regression to the mean.
Interpret EEG reassessment findings alongside symptom and functional measures when deciding whether to continue, modify, or end training.
Overview
This section covers the practical side of EEG assessment, building on the foundational concepts introduced in earlier units of Neurofeedback Tutor. You will encounter two broad approaches: abbreviated Q recordings that sample a subset of scalp sites, and full 19-channel evaluations that provide a comprehensive picture of brain electrical activity. Understanding when to use each approach, and what each can and cannot tell you, is a core clinical skill. Just as important is what happens next. The baseline assessment is the first data point in the ongoing assessment process described in the previous unit, and repeating the same assessment at planned intervals is the most direct way to show a client whether their brain activity is changing in the direction training intended.
Dr. Ronald Swatzyna has generously permitted the authors to share the Houston Neuroscience Brain Center's client qEEG cap orientation video.
As discussed in previous units, every EEG assessment begins with a visual inspection of the raw recording. Clinicians use various montages, specific groupings of electrode comparisons (Thomas, 2007), to identify the EEG's basic characteristics and flag any abnormal patterns that may warrant a neurological referral. Once visual inspection is complete, the clinician may pursue a quantitative analysis, and this section demonstrates several examples of that process. This progression from visual inspection to quantitative analysis mirrors the way experienced clinicians think: first look at the data with your own eyes, then let the numbers refine your interpretation.
Visual inspection is not a formality. In a study of 1,233 EEGs from psychiatric patients whose symptoms had not responded to treatment, Swatzyna et al. (2025) identified four findings on the visual read that were associated with medication failure: focal slowing, spindling excessive beta, encephalopathy, and isolated epileptiform discharges. Findings like these can be missed by a clinician who goes straight to z-score maps, which is why qEEG supplements, and never replaces, the visual read (Hughes & John, 1999; Nuwer & Coutin-Churchman, 2014; Tatum, 2021).
EEG assessment follows a logical progression: visual inspection first, then quantitative analysis. This unit demonstrates both abbreviated Q recordings, which sample selected scalp sites, and full 19-channel assessments, which provide comprehensive coverage. Repeating either assessment during training turns it into a measure of progress. You will see examples from multiple manufacturers, reflecting the equipment-agnostic approach of Neurofeedback Tutor.
Abbreviated Q Recordings
NewQ
This section introduces abbreviated Q recordings, streamlined EEG assessments that sample a subset of scalp locations rather than the full 19-channel array. These tools offer clinicians a faster, more automated path from data collection to clinical interpretation. We will examine how they work, what distinguishes their databases from normative approaches, and why they have become popular in diverse practice settings.
The first example is the NewQ, a six-location assessment that samples selected sites from the International 10-20 system, the standardized electrode placement grid used across clinical EEG practice. Similar tools include New Mind Maps, The Learning Curve (TLC), the Clinical Q, and others. Some are designed for specific hardware and software platforms, while others work across multiple systems. As a class, these tools offer a degree of automation in collecting, processing, and interpreting EEG information that benefits both beginners and experienced practitioners by incorporating expert decision-support systems into the assessment process.
Disclosure: J. S. Anderson developed the NewQ, which is shown here as one example of this category of tools.
A shared feature of these abbreviated approaches is their reliance on a clinical database, a collection of EEG metrics linked to known clinical findings rather than to the statistical norms of a healthy population (Swingle, 2014, 2015). Swingle argues that clinical databases are superior to normative databases (for a survey of database use in qEEG evaluation, see Johnstone & Gunkelman, 2003) because they connect EEG patterns directly to clinical observations, such as the typical voltage of alpha activity (8–12 Hz rhythms associated with relaxed wakefulness) in a particular region during specific tasks. Each clinical database reflects its developer's years of education, training, and clinical experience, allowing other practitioners to leverage that accumulated expertise.
Importantly, a clinical database assessment does not produce a formal diagnosis like those found in the Diagnostic and Statistical Manual of Mental Disorders (5th ed., text rev.; DSM-5-TR; American Psychiatric Association, 2022). Instead, it flags possible clinically relevant findings related to the client's presenting symptoms. This distinction matters in practice: the assessment guides your clinical reasoning and protocol selection without overstepping the boundaries of what the EEG data alone can establish.
Current research supports this caution. A meta-analysis of 417 children with ADHD found that EEG subtypes based on the theta/beta ratio did not map onto behavioral traits, although the ratio may still help in choosing a neurofeedback protocol (Boxum et al., 2025), a more cautious picture than the early validation work described in the EEG Assessment unit. More broadly, reviews describe resting EEG findings as a useful complement to symptom-based diagnosis rather than a substitute for it (Amico et al., 2026; Newson & Thiagarajan, 2019).
The following video describes the NewQ as an example of an abbreviated Q assessment. A subsequent demonstration walks through the complete process of collecting, processing, and interpreting the data. While this video features one specific tool, the workflow is representative of the broader category. Some tools assess additional sites, some collect data simultaneously rather than sequentially, and some use individual sensors while others employ an EEG "cap," an elastic fabric cap that holds sensors at fixed 10-20 positions, as shown in the image below.
Video © J. S. Anderson.
In summary, abbreviated Q assessments provide clinically useful information in a time-efficient format. They help validate and explain client symptoms, support training protocol selection, and can be readily repeated to monitor progress, making them practical tools for busy clinical settings. No published study has yet compared abbreviated Q findings with a full 19-channel qEEG in the same clients, so how closely the two approaches agree remains an open question. Two additional report examples appear below.
Clinical Q Assessment Report
Repeating an Abbreviated Q to Track Progress
Because an abbreviated Q is brief, inexpensive, and uses the same few sites and tasks every time, it is well suited to repeated use. Many clinicians repeat it at planned review points, for example every 10 to 20 sessions, and again at discharge. Each reassessment then answers a specific question: is the pattern that guided protocol selection changing?
Compare like with like. Review the same metrics that drove the original protocol decision. Look at the raw values and the percent changes between conditions, not only the flags in the report, because a flag can switch on or off when a value crosses a cutoff by a trivial amount.
Return to the client described earlier whose posterior alpha failed to rise when the eyes closed. If a reassessment after 20 sessions shows a clear eyes-closed alpha increase, and the client also reports falling asleep more easily and feeling less on edge, you have converging evidence of progress to share. If the alpha response and the symptoms are both unchanged, you have equally useful evidence that the current plan is not working and should be reviewed (see the Ongoing Assessment unit).
An unchanged reassessment is not a failed assessment. Documenting the absence of change protects the client from continuing an ineffective protocol, and it is exactly the kind of evidence the ethical principles of beneficence and nonmaleficence call for.
Abbreviated Q recordings like the NewQ use a subset of 10-20 scalp locations to produce clinically useful analyses in less time than a full 19-channel assessment. Their reliance on clinical databases, which link EEG patterns to observed clinical findings rather than population norms, allows clinicians to identify relevant patterns and select appropriate training protocols. These tools do not produce formal diagnoses but instead guide clinical reasoning and support treatment planning. Repeated at planned review points under the same conditions, they also offer a quick way to show whether the pattern that guided protocol selection is changing.
19-Channel Recordings
This section explores the full 19-channel EEG recording with normative database comparison, the most comprehensive of the assessment approaches covered in this unit. Where abbreviated Q recordings offer speed and clinical specificity, 19-channel assessments provide a detailed, whole-brain picture of electrical activity. We will walk through the recording process, the critical steps of artifact rejection and visual inspection, and how the resulting data are compared to normative values to guide treatment planning.
The primary alternative to abbreviated Q assessments is the full 19-channel recording, which captures data from all standard International 10-20 system scalp locations simultaneously. The data are then compared to a normative database, described in the EEG Assessment unit, which expresses the client's values relative to age-matched healthy individuals as z-scores (John et al., 1980; Thatcher, 1998; Thatcher et al., 2019). A normative database is only as good as its construction and validation (Thatcher et al., 2003).
Sex as well as age shapes EEG norms (Kang et al., 2021), and differences between recording devices and sites can shift z-scores unless the data are harmonized (Li et al., 2022). Your recording should therefore match the acquisition standards used to build the database (Keizer, 2021). The following video demonstrates the complete recording process. Video © J. S. Anderson.
Minimum technical standards for clinical qEEG are now set out in an International QEEG Certification Board guideline (Collura et al., 2025). It covers acquisition to American Clinical Neurophysiology Society standards, artifact management, visual inspection for paroxysmal events, epoch selection, and quality reporting, and it urges caution with LORETA and automated classifiers. Current electrode nomenclature follows the same neurophysiology standards, using T7/T8 and P7/P8 in place of the older T3/T4 and T5/T6 (Acharya et al., 2016). Dry-electrode headsets are increasingly marketed for assessment. In a direct comparison they tracked eyes-open and eyes-closed changes but fell short of gel electrodes in signal quality (Kleeva et al., 2024).
EEG Recording, Testing, and Protocol Development
We encourage you to view the following two-hour video in stages, as it provides a comprehensive overview of the full assessment-to-treatment workflow. The recording covers 19-channel EEG data collection, protocol selection, treatment implementation, and qEEG-guided neurofeedback. This end-to-end demonstration shows how assessment findings translate directly into clinical decisions, the bridge from data to practice that defines effective neurofeedback. Video © J. S. Anderson.
As you watch, keep the evidence in view. Choosing protocols from each client's qEEG has multicenter support in ADHD, where 55% of 136 patients reached remission (Krepel et al., 2020). By contrast, a systematic review found no controlled evidence that 19-channel z-score training is effective or superior to other approaches, and only early evidence for LORETA neurofeedback (Coben et al., 2019). Assessing with 19 channels and training with 19 channels are separate claims, and each needs its own evidence.
Clean Data Extraction and Comparison to a Normative Database
Before quantitative analysis can begin, the raw EEG must be carefully cleaned, a process that connects directly to the artifact rejection skills covered in earlier units. Visual inspection starts with the standard 19-channel recording referenced to linked ears. The clinician then extracts segments for each recording condition (for example, eyes open and eyes closed), inspects the data using multiple montages, different electrode comparison configurations that reveal different aspects of brain activity, and selects only clean, artifact-free epochs. These artifact-free segments are the foundation of a reliable quantitative comparison to the normative database. Video © J. S. Anderson.
The rigor of this data-cleaning process directly affects the quality of your assessment results. Artifacts, false signals from sources like muscle tension, eye blinks, or electrical interference, can distort quantitative values and lead to inaccurate comparisons with the normative database. Taking time to carefully reject contaminated epochs is what separates a clinically trustworthy assessment from a misleading one.
Clean data are also what make results repeatable. Resting spectral measures are highly reliable across weeks when recording conditions are controlled (Fein et al., 1983; Salinsky et al., 1991), although coherence reliability varies widely from one electrode pair to another (Roberts et al., 2016). Automated tools, such as classifiers that label independent components as eye, muscle, or cardiac artifact, can speed up cleaning (Pion-Tonachini et al., 2019), but their output still needs review by a trained eye.
Pre- and Post-Training qEEG Comparisons
A full 19-channel reassessment is the most detailed way to show whether training has changed brain activity, both at the trained sites and across the rest of the scalp. It is also the easiest comparison to get wrong, because many things besides training can change a qEEG between two recordings. Few published neurofeedback studies report pre-post qEEG measures at all (Wigton & Krigbaum, 2015), so careful clinical comparisons are valuable in their own right.
Match the recording conditions. Record the reassessment the way you recorded the baseline: the same cap size, montage, and reference; the same amplifier, sampling rate, and filter settings; the same order and length of eyes-open, eyes-closed, and task conditions; a similar time of day; and the same artifact-rejection criteria, database, and database version. Record any change since baseline in the state factors described in the EEG Assessment unit, such as sleep, caffeine, and medication. Z-scores are age-referenced, so a child who has aged a year between recordings can show a shifted z-score with little change in raw amplitude. Compare raw values (µV) as well as z-scores.
Separate lasting change from session effects. A single session of neurofeedback can change cortical excitability for at least 20 minutes afterward (Ros et al., 2010). A qEEG recorded right after training therefore mixes that short-term effect with any lasting change. Schedule reassessments before a training session or on a non-training day, as you did at baseline.
Decide what counts as real change. Resting qEEG spectral measures are highly repeatable. Reliability exceeds .90 with as little as 40 seconds of clean data (Thatcher, 2010), and a person's spectral profile remains recognizable over one to three years (Näpflin et al., 2007). Because the measure is so stable, a consistent difference between two carefully matched recordings is more likely to be real, with an important exception for extreme values described below.
Coherence is far less stable than power (Roberts et al., 2016), so a change in a single coherence value deserves more caution. Where test-retest data are available, the reliable change index described in the Ongoing Assessment unit gives a formal threshold (Jacobson & Truax, 1991).
Watch for regression to the mean. The most extreme values in any measurement tend to be less extreme when measured again, even with no treatment at all. A z-score of +3.5 at baseline is likely to be somewhat closer to zero on retest simply because part of that extreme reading was measurement error. This effect can be larger than reliability figures suggest.
Many EEG measures are not normally distributed but fat-tailed, with more extreme values than a bell curve predicts. Wood et al. (2024) simulated normative data with the modest skew and kurtosis reported for published qEEG databases. In their simulations, extreme baseline scores crossed the threshold for real change by chance far more often than a normal distribution would allow, and the problem grew rather than shrank as test-retest reliability rose. The same distribution shape inflated the number of values flagged as abnormal by roughly 30% to 100% at a |z| ≥ 2 cutoff and more than tenfold at |z| ≥ 3.
A single extreme z-score that "normalizes" at reassessment is therefore weak evidence on its own. These are simulations rather than clinical data, but they are a good reason to ask database providers for the distribution properties behind their norms. Look for changes that are consistent across neighboring sites, related frequency bands, and both eyes-open and eyes-closed conditions.
Use a point of comparison. A change that appears in every group is not a treatment effect. In a randomized trial, Janssen et al. (2016) recorded event-related potentials (ERPs) during a stop-signal task before and after 30 sessions of theta/beta neurofeedback at Cz, methylphenidate, or a physical-activity control in children with ADHD. The N2 component grew in all three groups, which the authors attributed to practice or development rather than treatment.
Only methylphenidate increased the P3, and in that group the size of the P3 increase tracked improvement in response inhibition. The neurofeedback and physical-activity groups did not differ from each other. A clinic rarely has a control group, so untrained sites, bands, and conditions have to serve that purpose. Change at trained sites and bands that exceeds change at untrained ones, and that moves together with a behavioral or clinical measure, is far more convincing than change of the same size everywhere.
Summarize the change systematically. Krigbaum and Wigton (2015) describe a single-subject approach: identify the deviant z-scores at baseline, then test whether they moved toward zero at reassessment. In their ten cases of 19-channel z-score neurofeedback, 90% of pre-post comparisons moved in the targeted direction. Whatever method you use, show the client side-by-side maps on the same color scale, next to graphs of their symptom ratings.
Read EEG change together with clinical change. EEG change is evidence of progress only when it travels with improvement toward the client's goals. A map that moves toward the norm while symptoms stay the same is not a treatment success, and moving z-scores toward zero has not been shown to be the mechanism by which neurofeedback helps (Coben et al., 2019). Symptom improvement with little map change can still be real progress, but it calls for the closer look at learning and nonspecific factors described in the Ongoing Assessment unit. When neither the EEG nor the symptoms have changed after an adequate trial, the reassessment has done its job: it shows that the plan needs to change.
Full 19-channel recordings with normative database comparisons offer the most comprehensive of the EEG assessment approaches covered in this unit. The process follows a structured sequence: recording from all standard scalp locations, visual inspection using multiple montages, careful artifact rejection, extraction of clean data, and statistical comparison to age-matched norms. The quality of the final assessment depends directly on the rigor of each preceding step, making meticulous data cleaning essential to accurate clinical interpretation. Repeated under matched conditions and away from training sessions, the same assessment becomes the most detailed evidence you have of whether training is changing brain activity, and it must always be read alongside the client's symptom and functional outcomes.
Check Your Understanding
- What is the difference between a clinical database and a normative database in EEG assessment?
- In the Janssen et al. (2016) trial, why was the increase in N2 amplitude not treated as evidence that any of the treatments worked?
- What are the key steps involved in processing a 19-channel EEG recording for normative database comparison?
- How can abbreviated Q assessments like the NewQ assist clinicians in selecting training protocols?
- Why is visual inspection of the raw EEG an important first step before quantitative analysis?
- Which recording conditions must match between a baseline and a reassessment qEEG, and why?
- Why should a reassessment be recorded before a training session or on a non-training day?
- How can regression to the mean make a client's progress look better than it is, and how can you guard against it?
- What would you conclude if a client's qEEG moved toward the norm but their symptoms did not change?
Assignment
Choose a client with a baseline abbreviated Q or 19-channel assessment, or use a hypothetical one. Write a reassessment plan that states when you will repeat the assessment, which recording conditions you will hold constant, which EEG metrics and symptom measures you will compare, and what results would lead you to continue, modify, or end training.
Glossary
A (auricular): the International 10-20 system letter designating an earlobe reference placement, as in A1 and A2.
abbreviated Q recording: an EEG assessment that samples a subset of International 10-20 system scalp sites, usually against a clinical database, in place of a full 19-channel recording.
alpha rhythm: an 8 to <13 Hz EEG rhythm dominant posteriorly during relaxed wakefulness with eyes closed and attenuated by eye opening.
amplitude: the magnitude of a signal's excursion from a specified reference level, reported using a stated convention such as peak, peak-to-peak, or RMS.
artifact: an unwanted signal component introduced by movement, environmental interference, hardware, processing, or biological activity outside the signal of interest.
artifact rejection: the removal of epochs contaminated by noncerebral signals before quantitative analysis.
beta rhythm: a 13 to <30 Hz EEG rhythm, usually low amplitude, associated with alert wakefulness and active cognitive or motor processing.
bipolar montage: an EEG montage composed of bipolar derivations, often arranged in longitudinal or transverse chains to emphasize spatial voltage gradients.
bridging artifact: electrical coupling between adjacent electrodes caused by conductive gel, sweat, or moisture, reducing their independence and distorting recorded voltage differences.
channel: one differential amplifier input, formed by a pair of electrodes whose voltage difference is recorded. A single ground electrode is shared across all channels rather than belonging to any one of them.
clinical database: a reference set built from a clinician's accumulated experience with clinical populations that maps EEG findings onto probable client symptoms and suggests training approaches.
delta rhythm: a 0.5 to <4 Hz EEG rhythm in the tutorial convention, prominent during N3 sleep and potentially abnormal when focal or excessive in awake adults.
drowsiness artifact: the intrusion of drowsiness or light sleep into a recording intended to sample wakefulness, marked by attenuation of the posterior dominant rhythm, slow rolling eye movements of opposite polarity at F7 and F8, and 1-2 Hz alpha slowing.
EEG artifacts: noncerebral electrical activity in an EEG recording, which can be divided into physiological and exogenous artifacts.
EEG power: the squared amplitude of EEG activity quantified over time or frequency, expressed as µV² for band power or µV²/Hz for power spectral density.
electro-ocular artifact: contamination of EEG recordings by potentials generated by eye blinks, eye flutter, and eye movements.
electrode impedance meter: an instrument that applies a small test signal to estimate electrode-skin impedance.
electrode-pop artifact: an abrupt high-amplitude transient caused by a sudden change in electrode-skin contact potential or impedance.
epoch: a defined time segment of a physiological signal selected for recording, averaging, artifact review, or analysis.
event-related potential (ERP): a voltage change in the EEG that is time-locked to a stimulus or response and extracted by averaging many trials; components such as the N2 and P3 are named for their polarity and order.
exogenous artifact: signal contamination originating outside the organism, such as electromagnetic interference, vibration, or recording-equipment noise.
F (frontal): the International 10-20 system letter designating sites that detect frontal lobe EEG activity.
fat-tailed distribution: a distribution with more extreme values than the normal (Gaussian) curve predicts, so cutoffs such as ±2 SD flag more cases than expected by chance.
Fp electrode sites: frontopolar scalp locations designated Fp1, Fpz, and Fp2 in the international 10–10 system; the 10–20 system uses Fp1 and Fp2.
hertz (Hz): the SI unit of frequency equal to one cycle per second.
impedance (Z): frequency-dependent opposition to alternating current, comprising resistance and reactance and measured in ohms.
impedance test: measurement of electrode-skin impedance before or during biopotential recording.
inion: the most prominent point of the external occipital protuberance at the posterior skull.
international 10–20 system: a standardized method for locating EEG electrodes at intervals of 10% or 20% of measured head distances; the original array includes 19 scalp and two auricular sites, with ground specified separately.
low-resolution electromagnetic tomography (LORETA): the inverse solution of Pascual-Marqui and colleagues (1994) that estimates three-dimensional cortical sources of scalp-recorded EEG at low spatial resolution.
mastoid bone: the bony prominence behind the ear.
microvolt (µV): a unit of electric potential equal to 10⁻⁶ volt.
montage: the specified arrangement of electrode derivations or channel pairings used to display a physiological recording.
movement artifact: signal distortion caused by movement of the person, sensor, electrode, cable, or tissue interface; its appearance and effects depend on recording modality.
nasion: the midline depression at the junction of the frontal bone and nasal bones.
normative database: a reference set of EEG measures collected from healthy subjects across age ranges, against which an individual client's values are compared as z-scores.
notch filter: a band-stop filter that strongly attenuates a narrow frequency range, commonly centered on 50 or 60 Hz.
O (occipital): the International 10-20 system letter designating sites that detect occipital lobe EEG activity.
ohm (Ω): the unit of impedance or resistance.
parietal EEG site (P): an electrode location over the parietal scalp designated P in international EEG-placement systems.
physiological artifact: contamination generated by biological activity other than the target signal, such as eye, muscle, cardiac, respiratory, or sweat activity.
posterior dominant rhythm (PDR): the dominant occipital rhythm during relaxed wakefulness with eyes closed, usually in the alpha range in healthy adults and attenuated by eye opening.
preauricular point: a landmark immediately anterior to the tragus used in cranial and EEG measurements.
protocol: a rigorously organized plan for training.
pulse artifacts: noncerebral voltages due to the mechanical movement of an electrode in relation to the skin surface caused by the pressure wave of each heartbeat.
quantitative EEG (qEEG): the numerical analysis of digitized EEG features, such as spectral power, asymmetry, and connectivity, sometimes compared with normative databases; no universal minimum channel count defines it.
reference electrode: an electrode whose potential is used as the comparison for an active recording electrode; it is distinct from the ground electrode.
regression to the mean: the tendency of extreme scores to move toward the average when measured again, even without any intervention, because part of the original extreme value reflected measurement error.
reliable change index (RCI): a client's pre-post score difference divided by the standard error of the difference; values beyond ±1.96 suggest change greater than measurement error.
rhythmic midtemporal theta of drowsiness (RMTD): a benign drowsiness variant, not an epileptiform pattern, consisting of notched or sharply contoured rhythmic theta waveforms localized to the midtemporal regions. It may be seen over either hemisphere, and its asymmetry shifts within and between recordings.
sensorimotor rhythm (SMR): a 12 to 15 Hz rhythm recorded over sensorimotor cortex, enhanced during physical stillness and attenuated by movement; it is defined topographically and behaviorally, so its range overlaps the mu and beta bands.
standardized LORETA (sLORETA): a refinement of LORETA that produces images of standardized current density, partitioning the intracerebral volume into 6,239 voxels at 5-mm spatial resolution, with exact localization for test point sources and no localization bias under noise.
surface Laplacian (SL) analysis: a family of mathematical algorithms that provide two-dimensional images of radial current flow from cortical dipoles to the scalp.
swLORETA: a version of sLORETA that adds a singular-value-decomposition-based lead-field weighting, improving localization under realistic noise and for deep sources (Palmero-Soler et al., 2007).
test-retest reliability: the consistency of a measure when the same person is assessed again under the same conditions, usually expressed as a correlation between the two sessions.
theta rhythm: a 4 to <8 Hz EEG rhythm associated with drowsiness and sleep transitions and, depending on location and task, memory and cognitive processing.
theta/beta ratio (TBR): EEG theta-band power divided by beta-band power using explicitly stated bands, electrodes, reference, and processing methods; it is not a diagnostic test for ADHD.
tragus: the small cartilaginous flap projecting backward over the opening of the external acoustic meatus; the preauricular point lies immediately anterior to it.
transient (EEG): an isolated waveform or brief sequence that is distinguishable from the ongoing EEG background.
vertex and Cz: the vertex is the highest point of the skull; Cz is the midline EEG position at the intersection of nasion-inion and left-right preauricular measurements.
z score: a standardized value expressing how far a measurement falls from a reference mean in standard-deviation units; it is not the probability of a diagnosis or evidence that a feature requires treatment.
z-score training: a neurofeedback approach using standardized deviations from a reference database to guide feedback; thresholds such as ±2 SD are protocol choices, not universal criteria.
Further Reading
Budzynski, T. H., Budzynski, H. K., Evans, J. R., & Abarbanel, A. (Eds.). (2009). Introduction to quantitative EEG and neurofeedback: Advanced theory and applications (2nd ed.). Academic Press.
Collura, T. F., & Frederick, J. A. (Eds.). (2016). Handbook of clinical QEEG and neurotherapy. Routledge.
Kropotov, J. D. (2016). Functional neuromarkers for psychiatry: Applications for diagnosis and treatment. Academic Press.
Thatcher, R. W. (2020). Handbook of quantitative electroencephalography and EEG biofeedback (2nd ed.). ANI Publishing.
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