Ongoing Assessment

Learning Objectives

After completing this section, you will be able to:

Overview

This unit demonstrates assessment for neurofeedback planning and shows how the assessments you use at intake, streamlined abbreviated qEEG recordings and comprehensive 19-channel evaluations, become tools for ongoing assessment when you repeat them during training.

It covers the rationale for ongoing assessment, including why it matters clinically, how it connects to core ethical principles, and how it supports the therapeutic relationship. Intake and initial EEG assessment provide a baseline against which you can gauge subsequent progress or its absence. This baseline is essential because benefits are never guaranteed for any individual client, despite group-level evidence supporting the efficacy and effectiveness of neurofeedback across a range of conditions, evidence whose strength varies considerably from one application to the next (Khazan et al., 2023; Schwartz & Andrasik, 2016).

Recent meta-analytic work underscores the point. For ADHD, the most extensively studied application, a 2025 meta-analysis of 38 randomized trials found clear improvement on ratings by people closest to treatment but no significant benefit on probably blinded ratings, although the subgroup of trials using standard protocols showed a small effect (Westwood et al., 2025). Critics note that many sham-controlled neurofeedback trials used procedures that depart from learning-theory principles, such as automatic threshold adjustment that holds reward rates constant regardless of performance, rather than the specific, stable reward contingencies that operant conditioning requires (Pigott et al., 2021; Sherlin et al., 2011). This may help explain why the small benefit was confined to trials using standard protocols. Whatever the group-level verdict for a given condition, only careful measurement tells you whether training is helping the client in front of you.

The primary purpose of ongoing assessment, the continuous evaluation of client progress throughout training, is to verify that expected outcomes are occurring and, when they are not, to use that data to adjust training protocols. In other words, ongoing assessment transforms neurofeedback from a fixed procedure into an adaptive, responsive treatment. Graphic courtesy of Mitsar.

The ethical foundations of ongoing assessment map directly onto the four principles of biomedical ethics set out by Beauchamp and Childress (2019) and summarized by Beauchamp (2003). Beneficence asks whether training is helping, while nonmaleficence asks whether it is causing harm, including the indirect harm of ongoing costs in time and money for an ineffective treatment. Autonomy requires that the client remain well-informed about outcomes so they can provide meaningful consent to continue, and justice asks whether training is efficient enough that it does not unreasonably deny access to other clients who need services.

Beyond ethics, ongoing assessment strengthens the therapeutic alliance by giving the client and practitioner shared data from which to make collaborative, evidence-based decisions. Seeing measurable progress motivates clients to persist with training and to apply emerging self-regulation skills outside the session. Reviewing data together can also help clients recognize connections between their internal states and real-world behavior, for example, noticing that the calm, focused state they achieve during neurofeedback can be intentionally recreated before a stressful meeting or exam.

Ultimately, because the goal of neurofeedback extends beyond changing EEG activity to improving emotion, cognition, physiological function, and real-life behavior, ongoing assessment must also include non-EEG measures that track whether brain-level changes are generalizing to the domains that matter most to the client.

BCIA Blueprint Coverage

This unit covers VI. Patient/Client Assessment - C. Ongoing Assessment.

This unit covers Measures and Methods and Decision-Making.

Listen to Full-Length Lecture

Measures and Methods

EEG Variables

This section covers how EEG data serve as the most direct index of neurofeedback training effects, including session-to-session tracking, within-session monitoring, and network-level assessment. The most natural measure for ongoing assessment is the EEG variable you have targeted for training. One example is sensorimotor rhythm (SMR) amplitude, the strength of brain activity in the 12–15 Hz range recorded over the sensorimotor cortex and expressed in microvolts. Amplitude and power are not interchangeable: power is amplitude squared and is reported in microvolts squared, so thresholds and ratios do not carry over between them. The underlying logic is straightforward: if you are rewarding SMR production, you should see SMR amplitude increase over time.

Neurofeedback at a single electrode site may also produce changes that generalize to other sites and broader brain networks. A sham-controlled trial of alpha training at a single site (Pz) in posttraumatic stress disorder reported evidence of restored default mode and salience network connectivity on fMRI, in what its authors called a preliminary investigation (Nicholson et al., 2020). Practitioners who have access to 19-channel quantitative EEG (qEEG) hardware and software (systems that record from multiple scalp locations and apply digital analysis to amplitude, power, coherence, and phase) can therefore assess whether single-channel training has influenced network-level function (Thatcher, 2020). This capacity to look beyond the trained site is especially valuable when treating complex conditions that involve widespread dysregulation.

The movie below is a 19-channel BioTrace+ /NeXus-32 display of SMR activity © John S. Anderson. Brighter colors represent higher SMR amplitudes. Frequency histograms are displayed for each channel. Notice the runs of high-amplitude SMR activity.

In practice, you typically measure the targeted EEG variable during a pre-training baseline, a brief resting recording at the start of each session, and then graph that value from session to session, using the intake assessment as the reference point (see Figure 1).

You can also record a post-training baseline at the end of each session and compare it to that session's pre-training value, which reveals whether the session itself produced a change in the intended direction. Plotting pre- and post-training data together, or graphing a series of within-session change scores, gives you a richer picture of how training effects accumulate over time (see Figure 2).

For even finer resolution, you can graph EEG data across individual training blocks within a session. For example, you might plot ten consecutive 3-minute trials to see whether amplitude shifts emerge during a single sitting (see Figure 3).

Many neurofeedback platforms make it easy to collect and graph EEG variables of interest, whether derived from single or multiple channels. Some platforms include built-in graphing capabilities, while others require you to export data into a spreadsheet for visualization.

Keep baselines comparable. Session-to-session EEG change is interpretable only when recording conditions are held constant: the same sites and montage, the same eyes-open or eyes-closed condition, a similar time of day, the same amplifier and filter settings, and the same artifact-rejection criteria. Vigilance is a particular confound. Alpha and theta amplitudes shift as a client becomes drowsy, so a post-training baseline recorded from a sleepier client can show a "change" that has nothing to do with training. Resting EEG also varies from day to day, so look for trends across several sessions rather than reacting to any single value.

Distinguish learning from outcome. Consensus guidelines for neurofeedback research separate three questions about the EEG (Ros et al., 2020). Did the client shift the target in the intended direction while receiving feedback (within-session regulation)? Did that ability improve across sessions (neurofeedback learning)? And did the change persist when feedback was absent, as during rest or no-feedback trials (transfer)?

Answering them requires graphing the trained parameter itself, not only symptom measures. The questions matter because not everyone learns: reviews estimate that between 16% and 57% of trainees never achieve stable control of the target parameter (Weber et al., 2020), and in several ADHD studies only about half of participants qualified as learners (Kuznetsova et al., 2023). Learning and symptom change can also diverge, so neither alone tells you whether training is working (Kuznetsova et al., 2023).

Match the measure to the training montage. Many practitioners train with bipolar (two-channel difference) montages, in which the feedback signal is the difference between two sites. Because activity that is in phase at both sites cancels in that difference, the bipolar signal can change either because amplitudes at the two sites diverge or because the phase relationship between the sites changes. If learning is judged from single-site amplitude alone, a client who is changing phase synchrony between the sites can appear to be making no progress when they are in fact responding to training (Thompson & Thompson, 2015).

Track the bipolar derivation that was actually trained, along with each site's referential amplitude and the coherence or phase between the sites. More generally, understand the training goal and how the training mechanism acts on the EEG, and measure progress in the same terms.

Qualitative Anecdotal Report

This section covers how narrative, open-ended reports from clients and informants complement EEG data by capturing real-world changes in experience and behavior. One product of intake assessment is the identification of a goal with practical consequences for the client, such as feeling less depressed, experiencing fewer headaches, concentrating better, or avoiding social situations less often. Goals may be framed positively or negatively (more of A, or less of B) and may reference either internal states like thoughts and emotions or observable behaviors like pages written or hours studied (Hurn et al., 2006).

A qualitative anecdotal report is a narrative description in which the client recounts a specific personal situation, together with the internal experiences and behavioral details surrounding it, to represent the current state of their presenting problem or progress toward their goal. At the beginning of each session, you can ask the client or a collateral informant (someone such as a parent, spouse, or teacher who can provide detailed background information or a different perspective on the client's functioning) to describe changes they have noticed. For example, both a child receiving neurofeedback and their parent can be asked how distractible the child has been over the past few days, and their responses can be compared for consistency.

Sometimes the most informative feedback comes from spontaneous comments by a teacher, guidance counselor, co-worker, or seldom-seen relative or friend. These comments are usually unsolicited and reflect the less biased perspective of someone who knows the client but speaks up only after noticing a clear change. The pattern resembles "Aunt Mary," who sees a child only once a year and remarks on how much the child has grown, while parents and others who see the child daily may not notice such incremental change. Changes during neurofeedback training can be similarly incremental, so independent observers may provide more objectivity than those whose daily interaction builds in bias. Such comments are selective, however: people tend to remark on improvement more readily than on its absence, so treat them as one valuable data point among several.

To sharpen these reports, you might ask the client to describe the specific situation in which they were most distractible, anchoring the anecdote to a particular time and place rather than relying on vague generalizations. Practitioner notes from these conversations can then be compared from session to session to build an impressionistic but informative picture of change over time.

Qualitative assessment is also essential during the first several training sessions for monitoring unwanted side effects of training, such as fatigue, excessive arousal, or headache. Catching these early allows you to adjust protocols before minor complaints become significant obstacles to progress. A brief structured side-effect checklist completed before each session makes this monitoring systematic.

In a sham-controlled study, most participants in every group, including sham, reported some transient adverse effects, and SMR training produced the most protocol-specific complaints (Rogel et al., 2015). Tracking these reports session by session helps you separate nonspecific reactions to the training situation from effects of a particular protocol, and documents that you acted on them (Hammond & Kirk, 2008). Graphic courtesy of Migraine Buddy.

Quantitative Self-Report

This section covers how numerical rating tools transform subjective experiences into trackable data, and how structured goal frameworks improve the reliability of ongoing assessment. Although clients may initially describe their problems in imprecise, everyday language, it is helpful to define those concerns more formally so that progress can be quantified. The process of refining goals may also surface concerns that matter more to the client than the one first presented. Quantification makes it possible to graph changes over time and more easily detect meaningful trends.

An early review found little evidence that simply collecting patient-based measures improved outcomes (Greenhalgh & Meadows, 1999). Later meta-analyses of routine outcome monitoring are more encouraging: when progress data are fed back to the practitioner and discussed with the client, outcomes improve modestly and dropout falls, with benefits most evident for clients who are not on track (de Jong et al., 2021; Lambert et al., 2018). This approach, known as measurement-based care (Fortney et al., 2017), depends on reviewing the data with the client and acting on them, not merely collecting them.

Internal subjective states can be measured using visual analog scales (VAS), continuous lines on which the client marks a point between two extremes, or Likert scales, which offer a set of labeled response options (e.g., 1 = "not at all" to 5 = "extremely"). Strictly, one such question is a Likert item; a Likert scale is the composite formed by summing several such items. Clearly defining each scale item and labeling the anchor points helps make these tools more reliable and valid (Joshi et al., 2015; Wewers & Lowe, 1990). At the beginning of a session, the client can mark their typical state over the previous few days, or their state at that moment, and the resulting values can be graphed across sessions for collaborative review.

We adapted the pain rating scale graphic below from © Overearth/Shutterstock.com. Note that a 0-to-10 scale with discrete numbers and faces is a numeric rating scale rather than a true VAS, which uses an unmarked continuous line.

Behavioral goals can be operationally defined, meaning specified in terms of the concrete operations used to measure them (Martin & Pear, 2019). For instance, if "improved concentration" is the goal, you might define it as the number of minutes the client spent studying the previous day. A more structured approach is the SMART goals framework, which calls for goals that are specific, measurable, achievable, relevant, and time-bound (Bovend'Eerdt et al., 2009, who write the last two letters as "realistic/relevant" and "timed"; the expansion varies by author). Behavior analytic tactics (Cooper et al., 2020) can further sharpen these goals by specifying frequency, intensity, duration, and context, for example, "During a typical week with ordinary stressors, a headache will occur not more than twice, with an average duration of not more than 1 hour and an average severity of not more than 2 on a 0-to-5 scale." We adapted the SMART goals graphic from © Appleing/Shutterstock.com.

After intake assessment and SMART goal definition, the practitioner can efficiently assess progress at the start of each training session. Because SMART goals are readily quantifiable, baseline and session-by-session values can be graphed for visual inspection and joint interpretation. Applied Neuroscience's NeuroLink™ application illustrates this integration by combining patient self-assessment with NeuroGuide™ to help clinicians target dysfunctional networks.

Self-Monitoring

This section covers how extending assessment beyond the clinic into clients' daily routines captures real-world functioning and strengthens self-regulation skills. Like the within-session reports described in previous sections, clients can also be asked to make ratings at assigned times and in specified situations outside the neurofeedback session. This practice is called self-monitoring, the client's own systematic tracking of symptoms (e.g., stress, distractibility) or abilities (e.g., sustained attention, time on task, accuracy) relevant to their presenting concerns. Self-monitoring can begin immediately after intake assessment and continue throughout training (Bakker & Rickard, 2018; Melbye et al., 2020).

Self-monitoring benefits clients in two important ways. First, it generates data from naturalistic settings that complement in-session measures. Second, it increases the client's self-awareness in day-to-day situations, which can strengthen their ability to self-regulate internal states (emotion, cognition, and physiological arousal) in the service of more effective behavior. Graphic courtesy of the Optimism family of mental health applications.

Structuring self-monitoring around specific situations or times improves reliability and validity. A situation-based approach might ask the client to rate their concentration level during a particular classroom activity, such as completing algebra problems; selecting one consistent context reduces variability from situation to situation. A time-based approach might ask the client to rate their calmness at a fixed point in the day, such as after their first hour of work or at day's end.

Whichever approach you choose, planning is critical. Clients need clear situational or temporal cues to remember their ratings, such as a sticky note in an algebra notebook, an electronic alarm, or a smartphone app that prompts completion at the right moment (Bakker & Rickard, 2018; Melbye et al., 2020). Building self-monitoring into the client's routine, rather than relying on memory alone, is what makes the data useful over time.

Behavioral Observation

This section covers how direct observation of behavior provides objective, quantifiable data that complement self-report methods. Direct observation of behavior (Cooper et al., 2020; Kamphaus & Dever, 2016) is widely used in both research and clinical settings to establish how frequently, how long, or under what conditions a target behavior occurs. For example, a parent might count the number of times a child interrupts during a set period and calculate a rate per hour, or a teacher might record at defined intervals whether a student is on-task during class.

These observations are readily quantifiable and can be graphed to track change over the course of neurofeedback training. Because they capture behavior as it actually happens rather than relying on retrospective self-report, they provide an especially valuable complement to the methods described in earlier sections. Graphic © Photographee.eu/Shutterstock.com.

Observation by less-invested informants also offers some protection against expectancy effects. Clients and parents who know that training is under way, and who have invested time and money in it, may rate improvement generously. In the 2025 ADHD meta-analysis, apparent benefits were largest on ratings by those closest to treatment and disappeared on probably blinded ratings (Westwood et al., 2025). Where you can, add an informant who is less invested in the outcome, such as a teacher, or an objective measure such as an observed behavior count or a continuous performance test, alongside client and parent reports (Thibault & Raz, 2017).

Rating Scales

This section covers how standardized, psychometrically validated instruments offer a rigorous complement to informal self-report and observation. Unlike the informal scales described in the Quantitative Self-Report section, standardized rating scales have been systematically evaluated for their psychometric properties, that is, their reliability (consistency of measurement) and validity (accuracy in measuring what they claim to measure; Baer & Blais, 2010; Tate, 2010). They provide a quick, well-validated method for assessing progress during neurofeedback training, especially when the same instruments were used during intake to establish baseline scores.

Rating scales sample a wide range of domains, including emotional experience, cognitive function, physiological symptoms, and everyday functional abilities. They can be completed periodically before training sessions or at home between sessions. Common examples include the Beck Depression Inventory-II (Beck et al., 1996) for tracking depressive symptoms and the Depression Anxiety Stress Scales (DASS; Lovibond & Lovibond, 1995) for monitoring broader emotional distress. We adapted this graphic from © PsychCorp.

Brief instruments that take only a minute or two, such as the Patient Health Questionnaire-9 for depression (PHQ-9; Kroenke et al., 2001) and the Generalized Anxiety Disorder-7 for anxiety (GAD-7; Spitzer et al., 2006), can be completed before every session or every few sessions and graphed alongside EEG data. Longer instruments are better suited to intake, scheduled review points, and discharge. Many published scales report reliable-change values and clinical cutoffs, which you can use to judge whether a change in score is meaningful (see Decision-Making).

Many practitioners combine neurofeedback and biofeedback with mindfulness meditation training (Khazan, 2013), a practice associated with physiological, cognitive, and emotional benefits. Clients taught to apply mindfulness during neurofeedback sessions may become more aware of the mind-body state associated with successful feedback, and this heightened awareness can become the basis for voluntary self-regulation, intentionally recreating that beneficial state during relevant real-world situations. If you incorporate mindfulness into your practice, several psychometrically sound instruments can measure the client's developing skill, including the Toronto Mindfulness Scale (Lau et al., 2006) and the Mindful Attention Awareness Scale (Brown & Ryan, 2003; further validated by MacKillop & Anderson, 2007).

Cognitive Tests

This section covers how standardized tests of thinking and cognition can track neurofeedback-related gains while managing practical challenges like practice effects. Cognitive tests can be readministered periodically during neurofeedback training to assess whether changes in brain activity are translating into improvements in attention, memory, processing speed, or executive function. Two practical challenges arise, however: the practitioner's time required for administration, and the risk of spuriously inflated scores from practice effects, improvements that result from familiarity with the test rather than genuine cognitive change.

Computerized testing platforms, such as CNS Vital Signs (Gualtieri & Johnson, 2006), address the time challenge by allowing clients to complete assessments independently after brief instructions. Practice effects can be managed by using alternate forms, which are different versions of the same test designed to be equivalent in difficulty. The Repeatable Battery for the Assessment of Neuropsychological Status (RBANS; Randolph et al., 1998) is one example, and its current Update edition offers four equivalent forms (Randolph, 2012). Graphic © Pearson.

Psychophysiological Assessment

This section covers how peripheral physiological measures complement EEG tracking and reveal whether neurofeedback effects are generalizing across body systems. Only a small minority of neurofeedback systems include built-in peripheral biofeedback channels. Those that do make it straightforward to record psychophysiological measures, such as skin conductance, skin temperature, respiration rate, heart rate variability, and electromyography (EMG), alongside EEG data during each session. Clinicians whose neurofeedback systems lack peripheral channels can add stand-alone monitoring devices, such as those from Thought Technology, HeartMath, and Optimal HRV.

When the neurofeedback system includes peripheral channels, these measures can be recorded throughout training; otherwise, record them during pre- or post-training baselines, just as you would with EEG variables. When peripheral biofeedback training is not part of the treatment plan, changes in these measures serve as evidence that neurofeedback effects are generalizing beyond the brain to other relevant physiological domains. Cross-system change of this kind is encouraging, but it is suggestive rather than conclusive: peripheral measures also respond to posture, breathing, medication, and the passage of time.

Decision-Making

This section covers how the data collected through ongoing assessment feed into an evidence-based decision-making framework that guides protocol adjustments and treatment planning. Ongoing assessment during neurofeedback operates within the framework of evidence-based practice, an approach that integrates scientific evidence, the client's condition, values and preferences, and available resources, all synthesized through practitioner expertise (McMaster University Health Sciences Library, 2021; Melnyk et al., 2010). In practical terms, the data you collect through the measures and methods described above inform collaborative decisions about whether to continue the current protocol, change it, or discontinue training altogether.

When assessment results show improvement, the natural course is to continue neurofeedback until the client's goals are met or a point of diminishing returns is reached, at which time training may be discontinued or redirected toward new goals. When results show insufficient change, the client and practitioner can consider switching to a different evidence-based protocol (Khazan et al., 2023). If 19-channel qEEG methods were not used during the initial assessment, conducting one at this point may identify sites and networks that earlier training did not address.

Is the change meaningful? Before acting on a change, ask whether it exceeds what measurement error alone would produce. The reliable change index (RCI) divides a client's pre-post difference by the standard error of the difference, which is derived from the measure's test-retest reliability; values beyond ±1.96 indicate change unlikely to reflect measurement error (Jacobson & Truax, 1991). For session-by-session graphs, the visual-analysis conventions of single-case research, which examine level, trend, variability, and the immediacy of change between phases, give you a disciplined way to read the data rather than eyeballing them (Kratochwill et al., 2013).

Schedule review points. Rather than deciding ad hoc, set review points in advance, for example every 10 sessions, at which you and the client formally compare current data with baseline and with the SMART goals. Early sessions carry information: in some studies, eventual training success could be predicted partway through training from performance up to that point (Weber et al., 2020). A client who shows neither learning of the trained parameter nor symptom change by the first review point warrants a conversation about the plan rather than more of the same.

Read EEG and clinical data together. Crossing the two kinds of data points to different next steps. If the client is learning to control the trained parameter and symptoms are improving, continue. If the client is not learning, review the feedback itself first (thresholds, reward rate, feedback modality, session length, and artifact control) before concluding that the protocol has failed. With bipolar montages, first confirm that you are measuring what was trained (see Match the measure to the training montage, under EEG Variables).

If the client is learning but symptoms are not changing, the target may not suit this client, and reassessment, including 19-channel qEEG where available, can guide the choice of a new target. If symptoms improve with no change in the trained parameter, nonspecific factors such as expectancy, the therapeutic alliance, relaxation, and practice may be contributing (Thibault & Raz, 2017). Those gains matter to the client, but they do not show that the protocol is working as intended, and they should be discussed openly.

For many clients, neurofeedback alone, however helpful, may not be sufficient to fully resolve a presenting problem or reach a goal. Integrating neurofeedback with complementary interventions is common in practice, although the added value of any particular combination has to be judged case by case. Thompson and Thompson (2015) describe methods such as heart rate variability training and transcranial electrical stimulation that pair well with neurofeedback, while Weiner (2016) and Fisher et al. (2016) describe how neurofeedback and psychotherapy can be conducted together for synergistic effects.

Finally, some clients experience significant developments during training (a new medical diagnosis, major family disruption, or another life crisis) that make successful treatment temporarily impractical. In these cases, it may be more appropriate to pause training until the new concerns have been addressed, rather than continuing sessions that are unlikely to produce meaningful gains under the circumstances.

Plan follow-up. When training ends, schedule follow-up assessment, for example at 3 and 6 months, using the same measures. In a meta-analysis of ADHD studies, neurofeedback effects relative to non-active control conditions were maintained for at least 6 months, and within-group effects on inattention grew between the end of treatment and follow-up (Van Doren et al., 2019). Follow-up data show whether gains are holding and whether booster sessions are needed.

Summary

This unit has reviewed the full range of approaches to ongoing assessment, from EEG variable tracking and qualitative anecdotal reports to quantitative self-report, behavioral observation, standardized rating scales, cognitive tests, and psychophysiological measures, and situated them within ethical and evidence-based healthcare frameworks. Decision-making options based on observed training results have also been outlined.

Assignment

Choose one current or hypothetical client. Write one SMART goal, select one EEG variable and two non-EEG measures to track progress toward it, and describe what results at your first scheduled review point would lead you to continue, modify, or discontinue training.

Glossary

References

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