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Episode Summary
This conversation originally aired on the NeuroNoodle neurofeedback podcast, where I joined the panel for a live Q&A. Watch the original conversation. What follows is my own material from that discussion, cleaned up and organized for reading.
What is neurofeedback, really?
All neurofeedback trains a signal inside the central nervous system. That is the working definition. No matter the provider, the style, or the software, you are reinforcing some pattern of electrical or metabolic activity and watching the brain respond.
Most neurofeedback uses the EEG. Everything starts with the raw data. You can't do anything without it. The techniques to set up and run a session in the software are not that complicated. The hard part is knowing what to train and how to adjust it as you go. On my Monday livestream I show the process in real time so people see that the mechanics are approachable. The skill lives in the decisions, not the setup.
If you want the longer primer on the method and the evidence behind it, I keep one here: Is Neurofeedback Legitimate? A Research Overview.
Why does the assessment come before the training?
Not every EEG provider runs an assessment first, and not every assessment is the same. A QEEG brain map is a full-head resting baseline. We compare your EEG to population averages and look at what sticks out.
The EEG is stable data. If I record you today, in a month, and a month after that, you get roughly the same map at a high level. That stability is what makes it a trait marker. The catch is the comparison. We compare your brain to the average, and people are weird. Being different from average is not the problem. The point is to read how things stick out, build a perspective from that, and pair it with performance testing like an ADHD continuous-performance measure to ground the interpretation. The map tells you what to work around, what to leave alone, and where to start.
What is the arousal model in neurofeedback?
The arousal model is one of the core schools of thought in the field. The idea is that each patch of cortex produces a mix of brain waves that set how active that tissue is.
Here is the mechanism in plain terms:
- Alpha (8-12 Hz) puts tissue into neutral. It is both cortical idling and active inhibition. I wrote about its dual role in Decoding Alpha Waves.
- Beta puts tissue into gear. Fast, high-frequency beta puts it into strong gear.
- Theta takes the brakes off and releases the tissue.
Speed matters too. When your alpha runs faster, your processing speed is literally faster. You retrieve and hand off information inside your mind more quickly.
The center of gravity for this model is the sensorimotor rhythm, or SMR. I'd say at least half the field still organizes around this one frequency. SMR carries an inhibitory tone over action and sleep. A clinician working in the arousal model usually runs a QEEG, then trains multiple bands to tune different patches of tissue in the directions you want. The autism world adopted this model heavily because it has a developmental angle. You can read a child's EEG, peg them at a slightly different developmental stage than their chronological age, and use training to push that tissue toward change.
Where did SMR training come from?
We owe SMR to Barry Sterman, who first showed that reinforcing sensorimotor rhythm produces an inhibitory response that helps brains resist seizure. From there we learned SMR is also a sleep phenomenon. During sleep it shows up as the sleep spindle and helps keep you asleep. The same rhythm that resists seizures keeps you asleep and supports sitting still. That convergence built the core of the arousal idea.
The Othmers carried this into clinical practice and commercialization, starting around the 1980s and into the 90s, with a three-band SMR-focused model. The software they used was called Neurocybernetics back then. When I came into the field it took two computers wired together by a parallel cable, one running the game and one running the signals. That software later changed hands, the company that bought it folded, and it reverted to Howard Lightstone, who relaunched it as EEGer. It is still a solid backbone for the field.
What is infra-low and infra-slow training?
Sue Othmer kept finding that working at lower and lower frequencies helped certain populations, especially over-aroused nervous systems. The over-aroused system is the autistic child who is screaming, stimming, and can't self-regulate. She got good results going lower, and trauma-informed therapists started seeing strong subjective effects by chasing what clients noticed during training rather than chasing the QEEG.
This is true of arousal-model work in general, and I work mostly in this space myself. You tune to the individual. One person's SMR frequency is not the next person's. If you train someone's C4 SMR at 11.5 Hz and they don't sleep well, you might move to 12 Hz and get a better sleep effect. You learn to watch the regulatory effects of sleep, stress, and attention in response to where you train.
Sue kept moving down until she was below 1 Hz, and the amplifiers struggled to resolve those signals. Siegfried Othmer did the engineering to push the floor lower and lower, down to about 0.001 Hz, the very long slow waves. That work moved into the infra-low and infra-slow range. There are several people in this space now: the Othmers with their Cygnet and NeuroAmp setup, Mark Smith and Mark Jones with infra-slow approaches, and Niels Birbaumer's work on slow cortical potentials. These are still arousal-model protocols that draw on the regulatory components living in the slow cortical potential and the DC shift of the brain. They settle the nervous system quickly, which is why so many trauma-informed therapists use them.
Can you do infra-low with a Pocket Aerobics amp?
Yes, with some tricks. The Q-wiz is a DC-coupled amp when you use it in DC-coupled mode, and it resolves frequencies pretty low. The bottleneck is usually the software. BioExplorer only resolves down to about 0.3 Hz before its built-in filters give out. To go lower you roll your own stack, often in BioEra, which is more powerful but less configured out of the box. Cygnet itself started as a heavily configured version of BioEra running on NeuroAmp.
The hardware side has hard requirements. You need centered silver/silver chloride electrodes. A flat silver or gold pasted electrode leaves the fluctuating voltage on the outside of the scalp because the resistance is too high for the DC signal to get in. With a silver/silver chloride electrode and a driven-right-leg ground, which the Q-wiz provides, you can get down to the DC-coupled level. You still need software that filters and stabilizes signals at that level. EEGer recently added a sub-Hertz module, so EEGer users can now go below 1 Hz without piecing together their own stack.
Does the feedback game need to be engaging?
No. Neurofeedback is mostly involuntary. It was discovered in cats, and cats are terrible instruction-followers. The brain extracts rules from patterns and gets applause contingent on what it just did. There is no expectation requirement and no need for conscious effort.
That changes how I think about gamification. Games matter when someone is bored, won't sit still, or has a sensory reason to want one. I use ZukorAir and like it. The reward-learning expectation does not do much here because the conditioning is involuntary. The feedback can be discreet and even a little boring, and the information may get in more cleanly.
I have watched heavy video feedback blunt the impact of the same protocols, again and again. My working theory is that neurofeedback depends on implicit learning. Your brain runs background pattern extraction, and you can't run that math while social and narrative information floods the same tissue. There is research showing social loading abolishes implicit learning. The feedback event is the brain responding to the specific applause for the specific change it just made. Drown that out with rich social stimulus and you risk killing the effect. I haven't formally tested it, so I hold this as clinical observation backed by adjacent research, not settled fact.
When a child with significant autism will only sit still for one movie watched 75 times, we watch the movie 75 times. Seventy percent of an effect beats zero percent of an effect. I have never used "watch movies and train your brain" as a selling point.
Do clients have to focus during training?
No. The process trains the brain whether or not attention is engaged. Margaret Ayers spent the last part of her career training comatose patients at the bedside to be more resilient and reduce seizures. Auditory-only training works almost as well as multimodal. I'd rather train auditory alone than visual alone. For a profoundly deaf client I'd add a tactile strip. I have trained autistic kids who were deaf and blind using nothing but a vibrating teddy bear in their lap, and that single tactile stimulus produced seizure reduction and sleep improvement. Plenty of teenagers ignore the screen and sit on their phones the whole session, and it still works.
What is Beta Reset and what does it do?
Beta Reset was developed by Jaclyn Gisburne and her partner. I was trained in it at UCLA around 2006 or 2007. It is a two-channel, bilateral, low-on-the-head protocol, roughly P3/P4 and IO1/IO2, forming a bracket across the back of the head. EEGer ships a Beta Reset session plan by default. It runs through 15 to 17 short segments, sweeping down through the beta band and then back up, going fairly high. People often report a physical sense of release during it.
It differs from standard band training and seems to work by disrupting hyper-coherence. The sweep up and back through beta breaks up that pattern. Because the back of the brain represents the outside world and the body, the body-based effects make some sense. The developers have used it for physical injuries, cerebral palsy with limb wasting, and parkinsonian phenomena. It is more of a recipe than a well-understood protocol, predictable in its effects but hard to fully explain.
How do you get a client to commit to the process?
I start with the brain map. I show people their own brain and validate what they already feel. You can see your rumination. You can see your tic. You can see your ADHD pattern in the data. When brain mapping is done well it is straightforward. People say "oh my god, that's me," and that recognition does the work.
Seeing your brain gives you permission to work with it. I got an email from a father whose 18- or 19-year-old daughter had a QEEG review with me. A couple of weeks later she had started studying, organizing her time, and preparing for college. Nothing about her physiology changed in two weeks. What changed is that anxiety and ADHD became harder to be overwhelmed by once she saw them as mechanical and trainable rather than a diagnosis happening to her.
That agency-through-education approach drives compliance. When you tell people why something is happening, their behavior changes faster than when you just tell them what to do. The expectation lives in ownership of the process: set your goals, notice what shifts, repeat. This is close to how good personal training works, an iterative process guided by assessment and your own feedback. There is more on that mechanism in Biohacking Plasticity.
Why isn't neurofeedback everywhere yet?
I get the question at least once a month. Someone says they feel great, their attention scores jumped, their sleep improved, and they want to know why everyone isn't doing this.
The honest answer is that the process is a little complex, a little expensive, and takes skill and knowledge to run. Those barriers stack up. The QEEG side is messy, expensive, and slow to acquire and clean. The neurofeedback software is locked into a small, niche industry with little market pressure to improve. Most of the tools were designed by engineers who love using them, and the interfaces still feel like late-1980s technology. EEGer looks dated, and it gets out of my way and has the tools I use, which is what I care about.
Two things will move the field. Computational methods will get good enough to build training plans and analyze maps without a specialist in the loop. Within a few years I expect we'll have mathematical models of an individual's brain, sleep, mood, and addiction patterns, so we can model an intervention's likely impact before we run it and tailor training without people like me as the bottleneck. Separately, the cost of QEEG acquisition has to drop. When a couple-hundred-dollar device can passively gather clean data instead of several thousand dollars of equipment and hours of clinician time, the field will open up. My current setup runs a few thousand dollars of gear per person, and getting started costs a client a couple thousand dollars. That is a real barrier.
Can machine learning read brain maps?
Phenotypes are subtle, and you can carry the same phenotype in different forms, which is exactly why all-in-one consumer devices struggle. After looking at thousands of maps, even noisy data starts to reveal sleep-maintenance issues or brain fog, as long as you have the raw signal to check against. I keep a primer on reading these patterns in Biohacking with EEG Phenotypes.
A modern QEEG works backward from pictures. A better approach decomposes the raw signal into its values and interprets those directly. The newer models add reinforcement learning and a reflective step before answering rather than producing a single predictive output. Lisa Tataryn built a custom tool that reads raw EEG files, EDF and MATLAB formats rather than PDFs, references a curated article database, and runs a signal analysis that flags things like alpha slowing in a given region. I am building something for myself: take a 30-minute video of me identifying patterns and explaining goals to a client and distill it into a clean written summary they can review later. I hold open whether the models handle subtle phenotype work well, and they summarize explanations reliably.
Can you train from a single electrode?
For training, you can. You can run two-channel z-score training at a location using a z-score DLL without a full-head or LORETTA analysis. For assessment, a single electrode carries too much variability to judge unless you have enormous context. Coherence and phase require simultaneous measurements across multiple electrodes, so a couple of sensors won't give you the real picture. Some products use language that sounds like QEEG while running three or four electrodes or sequential pairs over a minute or two of recording, and they lose most of the value of a real QEEG because they miss the simultaneous inter-electrode patterns.
I do think dry-electrode and low-contact systems will arrive, possibly through breakthroughs in material science rather than neurofeedback itself. Apple's AirPods Pro patents included language about embedded biosensors picking up EEG, so in-ear or glasses-based sensing may eventually be possible. The harder problem is the surrounding infrastructure: the referential databases, the QEEG transforms, the matched montages. You can't judge the same EEG without the same montage, and mapping low-density sensors into a higher-density space is genuinely difficult.
A word of caution about training blind on one spot at home. SMR-style training is fine for roughly two-thirds to three-quarters of people, and the remaining portion can have adverse reactions. A full brain map tells you what to avoid and where to start. Get the map. It costs 10 to 15 minutes with a cap and some gel. Messy hair is a small price for knowing what your brain is doing.
How does neurofeedback help Parkinson's disease?
Lisa Tataryn is the person to talk to about Parkinson's and neurofeedback. She has been funded by the Michael J. Fox Foundation and run pilot studies in this area.
On the O1/O2 question, I don't usually need posterior occipital sites for parkinsonian complaints. With O1/O2 you're mostly in visual tissue. You won't get much cerebellar signal because most cerebellar EEG points the wrong direction and can't be measured from outside the head. What you can find is beta hyper-coherence across the back of the head in various degenerative conditions, and a difference montage there with low-beta reward will break that up.
My strongest impacts in Parkinson's come from SMR training, usually a mix of C4 and CZ. I have seen this make medication work dramatically better, so a client doesn't have to wake mid-night to redose. In younger people with parkinsonian phenomena it seems to slow or stall progression. The biggest gains show up in the negative symptoms: rigidity, stiffness, woodenness, facial masking, and micrographia. The dopaminergic medication becomes more sensitized, the same way an Adderall effect strengthens when you pair it with neurofeedback. The theta/beta tone issues at the central sites are usually visible on a QEEG when parkinsonian complaints are present. Look at the map first.
Getting a brain map
If you want a QEEG and a session of neurofeedback, you can see us in our physical offices in New York City, Los Angeles, Costa Mesa, St. Louis, London, Stockholm, or Nova Scotia, or you can do remote work in the US by borrowing equipment with a coach walking you through it. Details are at Peak Brain Institute. I also run a livestream on Monday nights where I answer questions and walk through protocols in real time.
Get the full picture before you train, treat the process as agency through education rather than a diagnosis handed to you, and let the iterative feedback guide each adjustment. The technology will get cheaper and simpler over the next several years. The principle stays the same: assess first, train to the individual, and track what changes.