QEEG, Biomarkers, and Neurofeedback in ADHD

This new emerging research with novel insights by Kopańska and Trojniak (2025) takes on a big question: how do the strange, wobbly lines of an EEG recording connect to the very real struggles of living with ADHD? Their critical review pulls together evidence from quantitative EEG (QEEG), candidate biomarkers, and neurofeedback to sketch a multi-level picture of attention difficulties—from genes and synapses all the way up to classroom behaviour and clinical trials.

ADHD sits at the crossroads of genetics, prenatal and perinatal factors, family environment, and broader neurodevelopmental trajectories. It is defined clinically by inattention, hyperactivity, and impulsivity, but underneath those symptoms lies a complex neurobiology: altered dopamine and noradrenaline signalling, delayed cortical maturation, network dysconnectivity between default-mode and task-positive systems, and differences in neural plasticity. In other words, what we call “ADHD” is not a single brain pattern but a whole family of brain development stories.

QEEG offers a non-invasive way to observe some of these patterns in real time. By numerically analysing EEG signals and mapping them across the scalp, clinicians can identify relative excesses of slow waves (like theta) or deficits of faster waves (like beta) that correlate with difficulties in sustained attention, impulse control, and arousal regulation. This forms the basis not only for candidate biomarkers such as the theta/beta ratio (TBR) but also for EEG-based interventions.

Neurofeedback is one such intervention: a specialised form of biofeedback where real-time brain activity—typically EEG—is transformed into visual or auditory signals and used to help individuals learn to self-regulate their brainwaves over repeated training sessions. In theory, this leverages neural plasticity to nudge the brain toward more efficient, task-ready states. The review examines how far this theory holds up in practice, and where expectations may need to be recalibrated.

Methods

As a critical review rather than a single trial, the article synthesises findings across multiple methodological layers: the neurobiology of ADHD, QEEG patterns and subtypes, candidate EEG biomarkers, and the design of neurofeedback protocols and clinical trials.

At the QEEG level, the authors focus on studies that compute spectral power in classic frequency bands (delta, theta, alpha, beta, SMR, gamma) across frontal, central, and parietal regions. Particular attention is given to resting-state recordings at central midline sites (e.g., Cz) and to child and adolescent samples. Data are often visualised as topographic brain maps and, in some work, further analysed using source-localisation approaches such as LORETA to estimate the underlying cortical generators.

The review highlights how these QEEG features are distilled into candidate biomarkers. The most prominent is the theta/beta ratio (TBR), usually defined as frontal or fronto-central theta power divided by beta power during rest. Newer work on cross-frequency coupling (CFC), especially theta–gamma phase–amplitude coupling during cognitive tasks, is discussed as a more network-oriented measure of cortical coordination.

On the neurofeedback side, the review summarises protocols that use QEEG findings to shape training targets. Typical ADHD protocols involve:

• Inhibiting (reducing) excessive frontal theta (4–8 Hz) associated with mind-wandering and distractibility.

• Rewarding (increasing) SMR (12–15 Hz) or low beta (15–18 Hz) at central sites to support calm, sustained focus and improved motor control.

• In some variants, adjusting alpha (8–12 Hz) depending on whether anxiety or internalising symptoms are prominent.

Training is generally delivered over multiple weeks, often 30–40 sessions of around 30–45 minutes each, where EEG is recorded and transformed into conditioning-like feedback. The authors also examine the methodologies of key clinical trials: randomised controlled designs, active/sham neurofeedback control conditions, working-memory training comparators, mobile neurofeedback systems, and combinations with stimulant medication. 

Crucially, the review interrogates how control groups are constructed (e.g., sham feedback vs. alternative cognitive training), whether outcome assessors are blinded, and which measures are used—parent/teacher ratings, clinician ratings, and/or neuropsychological tests—because these methodological details strongly influence how we interpret neurofeedback’s effects.

Results

The QEEG literature reviewed converges on several recurrent findings in ADHD, while also revealing substantial heterogeneity.

First, many children with ADHD show increased slow-wave power, especially theta, in frontal and central regions, sometimes accompanied by elevated delta. At the same time, there is often reduced alpha and beta power, particularly beta activity associated with sustained attention and cortical readiness. This pattern has been interpreted as a form of cortical hypoarousal or maturational lag—essentially, a brain that is idling when it needs to be in “task mode.”

These features are captured by the theta/beta ratio (TBR), which is often elevated in ADHD relative to neurotypical controls. However, the picture is far from uniform. QEEG-based clustering studies have identified multiple subtypes: classic maturational lag patterns, hypoarousal profiles with high theta and high TBR, hyperarousal profiles with elevated beta (sometimes linked with irritability or aggression), and alpha-dominant patterns often associated with anxiety or depression. Some apparent clusters even turn out to be artefacts of measurement, underscoring how sensitive QEEG is to technical factors.

The review highlights that TBR’s status as a biomarker is now controversial. While early studies suggested strong discriminative power, later work shows weaker and inconsistent associations, especially once comorbidities and developmental changes are taken into account. TBR does not consistently correlate with physiological measures of arousal such as skin conductance, and it appears to be more meaningful for certain subtypes (e.g., hypoarousal/maturational delay) than for ADHD as a whole.

More sophisticated measures like theta–gamma cross-frequency coupling show reduced coupling during demanding cognitive tasks in ADHD, hinting at a deeper disruption of network-level coordination and neuroplasticity. These metrics are promising but still in the early stages of validation.

On the neurofeedback side, the story is similarly nuanced. Early open-label studies and less controlled trials reported substantial improvements in inattention and hyperactivity, especially when assessed by parents and teachers. However, more rigorous randomised controlled trials with sham feedback or active comparison treatments (like working-memory training) tend to find that both groups improve to a similar extent. In large multi-centre studies and adult samples, standardised neurofeedback protocols typically do not outperform placebo conditions or alternative psychosocial interventions. Improvements are real, but whether they are specific to EEG-based learning or driven by non-specific factors such as expectation, therapist contact, and structured cognitive engagement remains strongly debated (Brendan's hint: it's both).

Discussion

Taken together, this review reframes QEEG and neurofeedback in ADHD less as magic bullets and more as pieces of a complex puzzle. At the systems level, EEG findings make it very clear that ADHD is not simply “bad behaviour” or poor discipline; it is linked to genuine differences in brain development, neurotransmitter systems, and network dynamics. Excess theta, reduced beta, and altered alpha rhythms reflect deeper issues in arousal regulation, dopaminergic and noradrenergic signalling, and the timing of cortical maturation.

Yet QEEG patterns are also strikingly diverse. The same ADHD diagnosis can be underpinned by relatively different EEG signatures: a dreamy, under-aroused profile with high frontal theta; an anxious, internally focused profile with excess alpha; or an over-aroused, irritable profile with high fast beta. This heterogeneity explains why simple biomarkers such as the theta/beta ratio have struggled to deliver on early promises. TBR may still be useful, but primarily as a marker of specific subgroups (for example, children whose EEG genuinely reflects hypoarousal and maturational delay) rather than as a universal signature of ADHD.

Clinically, this suggests that QEEG is better viewed as a decision-support tool than as a diagnostic test. It can add nuance: differentiating attentional problems rooted in anxiety or mood from those arising from classical hypoarousal; highlighting when developmental delay in cortical rhythms is particularly pronounced; and guiding conversations with families about why one child responds well to stimulants while another experiences more side effects or mixed benefits. QEEG can also help track change over time, as one more lens alongside symptom ratings, academic progress, and everyday functioning.

For neurofeedback, the review’s synthesis is sobering but not nihilistic. Structured, engaging neurofeedback programs clearly can help many people with ADHD feel and function better, but the most rigorous evidence suggests that standard EEG protocols—especially off-the-shelf theta-down, beta-up trainings—do not consistently outperform sham feedback or active control interventions. This strongly implies that non-specific factors play a large role: regular one-on-one sessions, focused practice, motivational coaching, and the sense of “doing something active” about one’s brain.

From a practical standpoint, this argues for integrating neurofeedback within a broader, multimodal treatment plan rather than presenting it as a stand-alone cure. Medication, behavioural and educational interventions, parent training, and psychotherapeutic support remain foundational. Neurofeedback may be most valuable when tailored to a person’s specific QEEG profile and clinical picture: for example, using SMR training at central sites for children with marked motor restlessness and sleep problems, or carefully designed alpha training for individuals where worry and internalising symptoms clearly drive attentional lapses.

The review also pushes us to think beyond individual EEG bands. Network-level markers such as cross-frequency coupling—how slower rhythms like theta organise faster gamma bursts that encode information—may provide a more direct window onto the neuroplasticity mechanisms that are altered in ADHD. Deficits in theta–gamma coupling during working-memory tasks, for instance, might reflect inefficient synaptic processes governed by genes like SNAP25 or BDNF, suggesting a closer bridge between macroscale EEG patterns and microscale biology. While this remains a theoretical link, it is precisely the kind of integrative question that future multimodal studies (combining QEEG with genetics and structural/functional imaging) will need to address.

For people living with ADHD and their families, one reassuring message is that brain rhythms are not fixed. The same plasticity processes that may develop atypically in ADHD also provide ongoing opportunities for change—through medication, learning environments, relationships, sleep, exercise, and targeted neurotherapies. For referring professionals, the key is to use QEEG and neurofeedback judiciously: as tools that can complement, but not replace, careful assessment, evidence-based psychosocial treatments, and shared decision-making.

For neurofeedback practitioners, this review is an invitation to sharpen the science behind clinical intuition. Protocols like SMR enhancement for behavioural inhibition or low-beta training for sustained focus still have a place, but they should be embedded within a framework that acknowledges heterogeneity, limits of current evidence, and the need for ongoing outcome monitoring. Carefully observing who benefits most, under what conditions, and how EEG patterns change (or don’t change) alongside real-world functioning will be essential to moving the field from hopeful anecdotes toward precision interventions.

Brendan’s perspective

When you work with ADHD long enough, you realise there is no single “ADHD brain”—there are many. Some kids walk into the clinic like little tornadoes, bouncing off the walls; others are quiet, dreamy, half-present, as if the world were a slightly distant radio station. The review by Kopańska and Trojniak nicely mirrors that clinical reality on the EEG side: multiple patterns, overlapping subtypes, and very little that is truly one-size-fits-all.

This is where I think neurofeedback really lives: not as a protocol, but as a conversation between a brain, a set of signals, and a human life.

In practice, if I see a classic frontal–central theta excess with low beta and a history of daydreaming, careless errors, and “zoning out,” I’ll usually think in terms of SMR or low beta reward at C3/C4 or Cz, with clear inhibition of 4–7 Hz theta and sometimes 2–4 Hz delta. The goal is not to “flatten” theta altogether (that would be a very boring brain), but to help the system spend more time in a calm, task-ready state: enough activation to focus, not so much that it tips into anxiety or agitation.

When anxiety or internalising symptoms are front and centre—and the QEEG shows high, sometimes unstable alpha—I’m less enthusiastic about hammering away at theta and beta. In those cases, gentle alpha training at posterior sites can be helpful (just as inhibition over frontal sites can), often paired with breathing work, psychoeducation about worry, and explicit emotion-regulation strategies. You’re not just changing a band; you’re helping the person notice the felt difference between “tightly wound” and “allowed to exhale,” and giving them a way to practise that state.

Hyperarousal profiles—kids who are angry, explosive, sleep poorly, and show lots of fast beta—call for yet another approach. First, we have to make sure there are no epileptiform-type discharges (often over temporal sites), as this is quite often an overlooked issue. If this is ruled out, I may target high beta down-training (e.g., 22–30 Hz) while supporting SMR or low beta, often combined with body-based regulation, parent coaching around co-regulation, and very concrete behavioural plans. Neurofeedback in this context is less about improving test scores and more about softening those sharp edges so the child can actually make use of other supports.

A constant theme for me is individualisation. The review makes it clear that TBR is not a sacred number carved in stone, and I would strongly agree. I take TBR as a starting hint, not a verdict. A high ratio in a dreamy, under-aroused child means something very different than a slightly elevated ratio in a teenager who is also highly anxious, barely sleeping, and scrolling late into the night. Clinical history, developmental trajectory, learning profile, and family context decide how much weight I give any particular EEG metric.

Another point I’d underline is that research protocols and real-world practice are playing different games. In rigorous trials, modern research approaches force everyone to get the same thing so you can compare outcomes cleanly: fixed sites, fixed frequencies, fixed number of sessions. In the clinic, doing exactly the same thing for every person is what makes the intervention less effective. I’ll routinely adjust thresholds, feedback sensitivity, and even target bands across sessions based on how someone is responding—both on the screen and in their day-to-day life. If a protocol doesn't work... we change it. We don't keep going 'for the sake of science'. 

This is only one reason why research systematically underestimates what neurofeedback can do in skilled hands, while at the same time reminding clinicians not to overstate what is proven. Both perspectives are useful: the sceptical lens keeps us honest; the clinical lens keeps us creative. The problem is we've given the skeptics the mic a little too often. It's out turn to take it back. Neurofeedback works, combining specific and non-specific elements (just like all psychosocial and behavioural interventions). 

Finally, I increasingly see neurofeedback as one node in a network of interventions rather than a stand-alone star. Combining EEG training with good sleep hygiene, structured routines, movement, and parent guidance, tends to produce far more durable change than any one ingredient alone. Sometimes - as a "band-aid" medication can be of assistance for those in more 'urgent' need. However, medication is NOT a solution for ADHD, not in the short term, and definitely not long-term. If neurofeedback helps a child feel what “focused but not stressed” feels like, and medication helps them get there more easily, and school accommodations let them demonstrate what they actually know—then we’re using neurofeedback in the way this review implicitly suggests: as a tool that amplifies the brain’s plasticity within a bigger learning ecosystem.

If there’s a single takeaway from my side of the fence, it’s this: treat the EEG as a map, not as the territory. Let QEEG-informed protocols guide you, but always keep your eyes on the real-world question: is this person thinking, feeling, and living in a way that feels more efficient to them and the people they love?

Conclusion

The review by Kopańska and Trojniak gives us a rich, multi-level picture of ADHD: a neurodevelopmental condition in which genes, neurotransmitter systems, cortical maturation, and network dynamics all shape the brain’s electrical rhythms. QEEG reveals reliable tendencies—like excess theta and reduced beta—that help explain everyday difficulties with focus and self-regulation, but it also exposes the considerable heterogeneity hidden under the ADHD label.

Simple indices such as the theta/beta ratio, once heralded as potential diagnostic biomarkers, now look more like pieces of a larger stratification puzzle, useful for certain subgroups but far from universal. Neurofeedback protocols built on these patterns can support meaningful change for some individuals, particularly when embedded in a broader, multimodal care plan and adapted to the person’s specific EEG profile and life context. At the same time, rigorous clinical trials caution us that non-specific factors and general cognitive training effects account for a substantial share of the observed benefits.

The most promising path forward lies in integration: combining QEEG with genetic, structural, and functional imaging data to understand how macroscopic brain rhythms emerge from cellular and synaptic processes, and using that knowledge to design more precise, personalised interventions. For now, QEEG and neurofeedback are best used as thoughtful, flexible tools in a larger toolbox—ones that can illuminate, support, and sometimes accelerate change, but that work best when paired with compassionate relationships and evidence-based care.

In short: brainwaves tell an important part of the ADHD story, and when we read them wisely, they can help us write a more hopeful next chapter.

References

Kopańska, M., & Trojniak, J. (2025). From aberrant brainwaves to altered plasticity: A review of QEEG biomarkers and neurofeedback in the neurobiological landscape of ADHD. Cells, 14(17), 1339. https://doi.org/10.3390/cells14171339