Reading the Brain: Five Decades of EEG and Neurofeedback

WEBVTT
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Welcome to Brain Signals, a podcast series powered by the International Society for Neuroregulation and Research, ISNR for short.
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This space is about how you can help your brain work better and why that matters.
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I'm your host, David Owens, a licensed mental health counselor and neuroregulation specialist.
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In each episode, you will hear friendly, down-to-earth conversations with folks in the field.
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With the right tools, you can tap into a better version of yourself.
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Together, we'll discuss why it is worth your while to explore these tools that can help you rewire your brain and enhance your mental health and wellness.
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Thanks for listening.
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Let's get started.
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Today, we're delighted to welcome Jay Gunkelman.
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Jay has been working with EEG in biofeedback since the 1970s and is widely respected as one of the most experienced voices in the field.
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He's processed hundreds of thousands of EEGs and helped shape neurofeedback research and practice.
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He continues to teach, consult, and explore what brain activity can tell us about the mind.
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Jay brings deep expertise, decades of perspective, and a genuine curiosity that makes this conversation such a treat.
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Jay, thank you for being here.
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Well, thanks for keeping the intro short.
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I just get self-conscious on those things.
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To risk speaking for you a little bit.
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So how'd you get here?
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Well, you have to start with the assumption, the valid assumption that I'm a spoiled brat.
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Uh I I I I come from a well-to-do family in the Midwest, uh a major agricultural uh family, and uh I can basically do whatever I want.
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So my father wanted me to be a surgeon.
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I was in uh honors program at the University of North Dakota in Grand Forks and pre-med, three years uh straight from there it was to University of Minnesota Medical School.
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So I didn't want to be a doctor.
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And at the end of my third year, when it was the it would be the the next thing would be basically to go to University of Minnesota, I told my dad, you know, listen, I don't want to do that.
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And he said, Well, you know, here's a credit card.
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Go to Europe and sow your wild oats, whatever the hell that means, you know, and and you'll come back and be okay in the head and you'll go to medical school and become a surgeon.
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He wanted me to be a surgeon.
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And I said, you know, uh, it doesn't impress me as a as something to do.
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It's like an auto mechanic because you swap out parts.
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Uh it's like a seamstress because you have some fancy stitchery, and a butcher because you work out of this lab.
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But uh none of those are gonna task my creativity at all.
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I'll be doing the same surgery over and over again.
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I would be bored to death.
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And I know I'm no fun to be with myself when I'm bored to death.
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So I'm gonna do something else.
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Uh I did get a credit card, the Bank Americard, the first credit card to come out.
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I went to Europe, bought a car with a credit card.
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The car lot didn't know what a credit card was.
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Uh-huh.
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I had to have the family that I was uh staying with, which are business associates of my father's, go to the bank and the bank write a little letter, yeah, sell them the car, we'll figure out the stuff with the credit card.
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I actually made it through almost a year goofing around in Europe and coming back before the credit card, made it through all the European banking system back to the US banking system, back to finally getting to the bank and getting paid.
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Luckily for me, the German mark revalued upward 25%, and I held a whole bunch of German mark.
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The currency transaction paid for my year in Europe.
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So what was the car?
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I gotta ask.
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What was the car?
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Uh it was just a Volkswagen.
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It wasn't a fancy one.
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Uh a Volkswagen 1300, and yeah, and we ran all over Europe with it and and and sold it for basically what we bought it for.
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Nice.
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So that's great.
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Um yeah.
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Anyway, I came back and I went to the North Dakota State University in Fargo because I wanted to go back to study, and there was a physiological psychology professor who was really fantastic, and neuroanatomy, neurophysiology really lit my tail on fire.
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And before I graduated, I had way more than enough credits, you know, but no course of study.
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I studied everything.
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And I wrote a grant and had a lab built for me at the state hospital.
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And in 1972, there was no other state hospital that had an applied psychophysiology lab anywhere.
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So we were the very first.
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And there was no IRB.
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Okay.
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What the hell were they thinking?
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You know, that there's these kids running around the state hospital working with any patient they wanted to do whatever they thought of.
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You know, we came up, what did you have to do with neurofeedback at that time in 1972?
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Alpha training from Camilla and SMR from Sturman.
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And beyond that, really nothing much.
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You know, there's a little talk about theta with Barbara Brown and Elmer Green and stuff, but really the stuff was meditative and not really the applied stuff.
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So, you know, we worked with you know alpha training, but we also did temperature training and elect and electrodermal training, uh uh with you know, the the regular biofeedback uh type stuff that was available at that time.
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We did that for three years.
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We were actually written up in 1974 in the local newspaper and big color uh uh print above the line.
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Nixon uh economy going bad was below the line, you know, and uh uh we're being uh held up as uh a novel new approach to and we could effectively treat migraine.
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And that was in the newspaper headlines.
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It was, you know, multiple pages after the front cover big article uh uh uh following up on it.
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So uh, you know, we made it one more year, and the head of the hospital said, Well, you know, you've been taking your entire department with you to the national meeting, and uh from now forward your budget's not gonna be as liberal, and you you can send one and they'll come back and share what they learned.
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And I said, You're starting to nitpick my budget.
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I quit.
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I'm not gonna sit around here while you you cut pieces off the lab.
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So uh um good luck.
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And they put an ad out for somebody to run the lab, and there wasn't anybody who would know how to run all the equipment we had to build ourselves in order to have equipment.
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So that was it.
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I started uh working with clinician intent uh with very few tools.
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The EG tools at the time were trash equipment, really.
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And we had to build our own EG amps.
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And uh luckily I I did run into uh uh some old equipment that was good, the Ofner T type, the first transistorized EEG machine, okay, which I actually have.
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So wow.
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And so I mean, there you are.
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You you've been there, you've been been in in that part of the world in the Midwest, which by the way, my my dad was from rural southwestern Minnesota, several hours south there.
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Um, but yeah, I love that part of the the country.
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But anyway, so there you are, you've been in this lab building this equipment, running this equipment, doing what you want.
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No IRB.
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And uh spoiled Brat with working with patients.
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But doing real good.
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We had very, very good success for six months.
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We had we had uh an experimental trial period of six months, and they wanted to see what we could do.
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And if we couldn't give them some benefit, we would have been history.
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Right.
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Um but we had success with patients that they couldn't work with.
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And our success was successful to the point where they were dismissed into halfway homes and outpatient facilities, which are a lot less expensive.
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And they thought that was a gigantic financial success.
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And so they gave us the rest of the funding for the next for the last three years.
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And again, I ran that for three years, moved to California and goofed around for a few months just hanging out at Lake Anza in Berkeley and um and having fun because I had worked solid for a few years, you know.
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And yeah, you're finding your feet.
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We started a little company to make EG devices and whatnot.
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We in 1976 we showed a pocket-sized brainwave analyzer at the California Biofeedback Society.
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Uh we actually had a little company that made equipment.
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We had an FDA registered uh uh temperature device and FDA registered two-channel EMG device for flexor extensor pair rehab, um, and then this pocket-sized EG device and had a little tiny company, and we're making stuff and we're selling a few, but you know, you're selling into a small niche market.
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And the pocket-sized EEG machine, if it would have been two years earlier, we would have sold some because the you could still get grant money for EEG stuff at the time, but the EEG uh neurofeedback dried up because of a bad piece of research from Pashkowitz and Orn.
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And uh they showed alpha training didn't have anything to do with affect, and uh their training showed no alpha learning curve, by the way, but then they concluded what alpha training would do, so killed the the marketplace for EEG devices.
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And so I thought, you know, how do you make a small fortune in neural feedback?
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You start with a big one.
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I've heard, yeah.
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I I've got rid of about as much as I want to get rid of.
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You know, so I gave the company to the engineer who successfully managed that company and he's retired now and doing nicely.
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He's made EG devices, uh, OEM into the field of neurofeedback.
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All of Jim Hart's devices, filters, everything, he's made all of those.
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Al Brown's equipment, he's made himself a fortune.
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He's got a big compound in the Philippines, and yeah, he's he's retired and in style.
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But that was my lab partner at the beginning.
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And uh after I started in the lab, then it was called Telemedics, uh, the EG transmission company, uh, we received from 400 hospitals, they had broadcast off satellite into us in San Francisco, and there was a competing group in Texas, Dr.
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Proler in Texas and Dr.
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Shear in California, and they competed.
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And eventually they decided to talk to each other and they they decided not to compete anymore, and they both raised their prices, you know.
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So uh and then they merged the company and and called a Telemedics.
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But Larry Woodard, the engineer, um actually was hired into the company after I was in the company doing the EG stuff.
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And the engineer that they had was supposed to bring it down from a Samsonite suitcase container into a regular medical device box, you know, and upgrade the engineering to more solid state uh than crude old device.
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I mean, that needed a major upgrade.
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And their engineer really couldn't get it.
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He was trying to get a filter, and he he couldn't get the filter to behave like, you know, filters can ring, they they can be bad filters, and he was having trouble.
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I said, Well, tell me exactly what specs you want on the filter, and I'll bring in a breadboarded filter for you tomorrow morning.
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You know, he said, it would take a lot longer than that to design.
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I said, shut up and give me the specs.
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You know, don't don't argue with me that it can't be done.
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Give me the spec.
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Tell me what you want to have.
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Okay.
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And uh I brought walked in the next morning with the breadboard with the circuit all stuffed in it with a spot to put the plus and minus voltage and the input and the output.
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And uh they tested it and it was a perfectly good filter.
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And I said, My goodness, uh who did this?
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You know, uh, this friend of mine, you know, the Larry Woodard, said they hired him as a bench tech.
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Two months later, he was the head of engineering.
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Right.
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And upgraded their level of engineering.
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Uh uh, he's kind of a wizard.
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He he invents things that you that don't exist, uh real-time data compression expansion algorithms, so you can squeeze really 32-bit stuff through a small little voice grade phone line.
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How do you carry 10 EG channels on a voice grade phone line?
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Well, you have center frequencies.
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EG goes up and down, and you change an amplitude modulation to a frequency modulation, and you have center frequencies and you lock onto the frequencies of the phase lock loop, and you just decode the oscillations back into an amplitude.
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So it's amplitude to frequency and frequency to amplitude, and the frequency modulation is the funniest damn noise you ever hear on the phone.
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And it's it's much worse than the old computer sign on.
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Yeah, I was thinking of the dial-up.
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Yeah, yeah.
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Oh my gosh.
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Yeah.
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Well, this is this is a imagine ten non-harmonic, like if there'd be harmonics of each other, they'd be interaction between channels.
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You know, you it was very fancy engineering.
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The phase lock loop filtering had to be very restricted, otherwise the adjacent frequency going a little bit bigger would start to interfere.
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And yeah, anyway, as an engineering you know person, he he whipped those together and uh upgraded the quality of everything in the company.
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So when Dr.
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Scheer passed, he bought the company and manufactured the equipment going forward for the doctors in the United States.
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There were a few centers at that point that had uh sprung up.
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It wasn't just the two big centers, there were local neurologists popping up that needed to have the ability to service like five hospitals so they didn't have to drive so much, you know.
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And so the EG could come to them instead of them to the hospitals.
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So there Nampa, Idaho, Orlando, Florida, there they're little spots that popped up that uh had to be serviced as well.
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So crazy business.
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Uh uh receiving a hundred EGs in a day on a routine day and two hundred on a big day, and yeah, uh the and the two hundred was brutal.
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But it was good training, you know.
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I mean, you know, you're it's pattern recognition, and the more you see, the more you can see.
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And the more reliable it is.
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You know, what's that pattern?
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Well, I've seen it before, so I have some idea what I'm seeing.
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How do you know anything unless you have a memory trace to compare it to?
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So, you know, that how do you have conscious awareness?
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Well, you have the first memory trace and you're comparing your perception to the memory trace, now you're consciously aware.
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Yeah.
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That's why it takes 500 milliseconds to be consciously aware of something.
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The P300 plus the first 200 milliseconds of the second cycle.
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The the second time it hits the frontal lobe, you have a memory trace, and you compare the perception to the recollection.
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Now you're consciously aware of what it is.
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Yeah.
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Now we're getting somewhere.
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Yeah.
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I've got this big complex model of consciousness, and it's a little piece of it.
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So yeah, yeah, yeah.
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Just the tip of the iceberg.
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So I mean with that equipment and I mean there there's how you got into it.
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And then, you know, you you mentioned why early on it was you needed something that also, like, yes, it tapped into all the intellect and all the, you know, the the tech, the engineering, the science, the physiology, neurophysiology, but then you also needed that creativity.
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You named that creativity early on.
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You wanted to be able to expand into this.
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And I mean, I'm thinking about the pattern recognition and the visual art that that creativity is when looking at all these waveforms and noticing, yes, have I seen this before?
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Have I not seen this before?
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Where have I seen this before?
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What does it mean?
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All of this.
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And I'm just imagining how you blended those two.
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You may think your data is speaking to you.
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If you see the data speaking to you, you are either crazy or you're getting an insight from your data.
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Yes.
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And the only way you're gonna know whether it's you being nuts or the data speaking to you about something important is to actually write it up.
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And the 2005 paper on endophenotypes, there are two known EG endophenotypes, and I proposed there are nine others, we just don't know the genetics of them yet.
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And this turned out to be exactly correct.
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All of that's been validated.
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But it was looking at EG patterns, and people are pretty good pattern detectors, and if you look back in time at 500,000 EGs and that's speaking to you, it's time to speak about it.
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So in 2005, that's when we wrote about it.
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And I had been talking about it before that as common failure modes, but not linking it to the endophenotypic genetics.
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Aaron Ross Powell, How'd that link come?
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That jump from what you were seeing to then like eventually drawing the link to yeah, the genetics and the phenotypes and I was seeing these 11 patterns real commonly, and I just noticed one day that two of them had known genetic correlates.
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So they were endophenotypic, and the others I saw so reliably I thought they must be too.
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So I had to write the fool's article and tell people what a nut I was and what I thought the data was saying.
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And as soon as I published it, there were some people that got real excited because it was a way to look at the EG without the DSM.
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Sure.
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It transcended the DSM entirely.
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There was no point in it where it said if it's a this or a that diagnostic category and it has this pattern, then you do this.
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There was none of that.
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It was all this EG pattern predicts this treatment, whether it was neurofeedback or medication.
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And again, when you see two of them that are known genetically linked, and you see nine that are just as reliable in your mind, and again, in your mind, you know, uh, so I thought it was well worth the risk of uh putting it out there.
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And uh in Europe, Martin Arnes uh got excited about it.
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He uh he invited me to come over and lecture.
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And I walked in the door.
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The first time I walked in the door, he had a 49 ADDs and 49 match controls, uh the EEGs I had to endophenotype all of that.
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And that's like a normal day's work.
00:19:50.400 --> 00:19:56.079
You know, it was a it was a few hours, a couple hours of my time, dedicated time to go through all of that.
00:19:56.240 --> 00:19:59.759
Two hours in his office phenotyping 100 EGs.
00:20:00.000 --> 00:20:02.960
Boom, boom, boom, boom, boom, boom, boom, ended, done.
00:20:03.279 --> 00:20:09.599
And if you ever saw the original paper, there's no sample of an EG, no waveform of any sort.
00:20:09.920 --> 00:20:12.799
And how well do word describe EG?
00:20:13.920 --> 00:20:17.759
It's a little limited up and down and up and down as it goes across the page, you know.
00:20:17.920 --> 00:20:28.880
So you know, the inter rate of reliability, Mark Kine had already read the paper and he he sorted them according to what he thought they were, and I sorted them according to what the author thought.
00:20:29.119 --> 00:20:31.759
So, you know, he thought, well, I must be right.
00:20:32.400 --> 00:20:41.200
You know, so you know, maybe most of the time you never know, you know, but it matched with his better than 90% of the time.
00:20:41.680 --> 00:20:51.359
Uh uh Cohen's lambda of 0.94, 0.96, uh 0.92, you know, the least common pattern was a 0.89 or something.
00:20:51.519 --> 00:20:53.680
So it was really highly reliable.
00:20:53.839 --> 00:20:54.160
Yeah.
00:20:54.240 --> 00:20:54.400
Yeah.
00:20:54.640 --> 00:21:00.000
Interrater reliability in EEG right now isn't what it was historically.
00:21:00.240 --> 00:21:13.759
When Gibbs and Gibbs published in 1940, you could get between the laboratories that there were worldwide, if you showed an EEG to them, you would get about 90% to 80% agreement between the labs.
00:21:14.319 --> 00:21:18.079
Now you get 30-40% agreement.
00:21:51.559 --> 00:21:51.960
Wow.
00:21:52.440 --> 00:21:53.559
What the hell happened?
00:21:53.960 --> 00:21:54.440
Good question.
00:21:54.839 --> 00:21:55.799
The DSM.
00:21:56.519 --> 00:21:56.839
Okay.
00:21:57.720 --> 00:22:03.000
Back in the day, the diagnostic specificity was what they were starting to demand.
00:22:03.160 --> 00:22:07.400
And in DSM two, they started to say, well, you have to be more specific.
00:22:08.200 --> 00:22:11.480
DSM three tripled the number of diagnostic categories.
00:22:12.279 --> 00:22:17.480
So you just agree to play football on the field and you jump on the field and you move the goalpost.
00:22:17.799 --> 00:22:21.480
You know, now you have three times as many yards to make.
00:22:22.200 --> 00:22:26.599
The DSM is just a failed system and they know it.
00:22:26.759 --> 00:22:28.759
There's no predictive validity.
00:22:29.079 --> 00:22:32.359
It's a great categorizing system for billing.
00:22:33.160 --> 00:22:46.839
And you've got to put a a label on it to end up billing for it and use those stupid labels to communicate with other people, either derogatory, he's a this or he's a that, you know, she's a right, she's got this, she's got that, you know.
00:22:47.000 --> 00:22:50.519
I mean, but it shouldn't really be a pejorative.
00:22:50.759 --> 00:22:53.400
You know, this you've got what you've got.
00:22:53.640 --> 00:22:59.079
We need to figure out how to to work with it or uh improve it or uh fix it somehow.
00:22:59.240 --> 00:23:00.200
And yeah.
00:23:00.519 --> 00:23:02.680
It it feels like we get to peer through it.
00:23:02.759 --> 00:23:10.839
Uh like like there's some shared language in there, and there's yes, you know, codes and things that are helpful for in billing and insurance reimbursement and all this stuff.
00:23:10.920 --> 00:23:26.359
And you know, you could list off the alphabet soup acronyms of the various you know, diagnostic categories that are highlighting certain symptoms, and you say that from one professional to another, you've told some meaningful information and yet you've said a whole lot of nothing at the same time because it tells you very little about the actual person.
00:23:26.920 --> 00:23:33.480
You can be diagnosed with things that will be on your job uh uh description in some place, and they won't hire you.
00:23:33.559 --> 00:23:34.279
You know, right.
00:23:34.599 --> 00:23:41.799
Uh you have ventractable epilepsy the uh that's as partially controlled, then that that's very general, or you're bipolar.
00:23:41.960 --> 00:23:43.799
Um that's a risk, isn't it?
00:23:43.880 --> 00:23:48.359
You know, and you know, that these are categories and they don't really mean anything.
00:23:48.519 --> 00:23:52.440
I've seen people diagnosed with bipolar, and I look at the eugen and I say, no, they're not.
00:23:52.599 --> 00:23:54.680
It's a DSM failure.
00:23:54.920 --> 00:24:01.960
And uh you know, there's better ways to categorize that actually have predictive validity, like endophenotypes.
00:24:02.200 --> 00:24:11.079
Uh genetics can make some predictions, but most of the genetic testing that makes predictions right now are just looking at liver metabolic pathways.
00:24:11.160 --> 00:24:16.839
They're not really looking so much at uh neurotransmitter densities and so forth in the brain.
00:24:17.079 --> 00:24:24.119
So it's better than not looking, but genetics hasn't yielded the solution that they expected by now.
00:24:24.599 --> 00:24:33.000
One thing that uh might be helpful just for uh, I guess, information, is just could you throw out a rough definition of endophenotype?
00:24:33.319 --> 00:24:33.960
Sure, sure.
00:24:34.200 --> 00:24:43.559
So you have genetics and you have behavior, and there's a step in between them, because not every gene expresses itself.
00:24:44.119 --> 00:24:47.319
You could have the gene for breast cancer and never get breast cancer.
00:24:47.400 --> 00:24:50.200
You have gene for Alzheimer's and never get Alzheimer's.
00:24:50.359 --> 00:24:58.519
The gene has to express itself as an endophenotypic pattern, and that is going to directly relate to the behavior.
00:24:58.839 --> 00:25:04.920
We come up with a diagnosis, and then we pick a medication that we think is going to help with that diagnosis.
00:25:05.240 --> 00:25:11.799
And, you know, it has neurotransmitter deals and so forth, and it's gonna have an effect on the brain.
00:25:12.119 --> 00:25:21.319
If you actually look at the brain instead of the diagnosis, you'll have a much better idea of what it needs, because the diagnosis might point you to the location.
00:25:22.039 --> 00:25:25.240
We have OCD, mm-hmm, we're gonna have a singular tissue.
00:25:25.559 --> 00:25:27.720
It doesn't tell you what you're gonna see when you get there.
00:25:28.039 --> 00:25:30.759
It could be alpha, which is the most common.
00:25:31.319 --> 00:25:39.000
And and if you have a slightly slower than 10 hertz alpha, you have an 85% probability of an SSRI giving you a success.
00:25:39.880 --> 00:25:45.160
What study do you see with an 85% success for an SSRI?
00:25:45.720 --> 00:25:49.799
Unless it's biologically indicated with a biomarker, you don't.
00:25:50.039 --> 00:25:55.000
If it's indicated with a DSM, 45-50% if you're lucky.
00:25:55.640 --> 00:25:58.039
You know, because it's not being targeted.
00:25:58.359 --> 00:26:02.599
It could be theta instead of alpha, and it could be beta instead of alpha or theta.
00:26:03.160 --> 00:26:06.920
A TMS machine they pointed at the front of the head trying to activate it.
00:26:07.480 --> 00:26:11.640
That's okay if it's alpha or theta, but if it's beta, they just made a big mistake.
00:26:11.960 --> 00:26:17.400
You know, if you don't look first, you don't know what you're doing because you don't know what you're shooting at.
00:26:17.720 --> 00:26:23.799
You know what you've got as a tool to shoot, but you don't know what you're shooting at unless you actually measure it first.
00:26:24.519 --> 00:26:38.359
So years ago, before QEG really uh penetrated the field of neurofeedback when there was still one and two channel devices primarily, it was some people looked at the EEG, but it wasn't that common.
00:26:38.759 --> 00:26:42.039
It was mostly symptom-based protocols.
00:26:42.359 --> 00:26:52.119
So if you had this diagnostic category, you got this kind of a protocol and it might work and they might have to adjust some things, but the field moved forward based on that basis.
00:26:52.680 --> 00:27:03.720
And I I had a my first customer that wanted EG guided recommendations, basically the endophenotype model without the label yet.
00:27:03.960 --> 00:27:04.359
Sure.
00:27:05.160 --> 00:27:15.960
And we looked at his outcomes after two years of QG guided training, and he compared that to three years.
00:27:16.119 --> 00:27:24.359
They had four years of of of data before they added the Qs, but the first year they thought that was them getting better at the treatment end of it.
00:27:24.519 --> 00:27:27.319
And they had been trained under Othmars and LUBARs.
00:27:27.559 --> 00:27:37.000
So, you know, they had C3, uh uh C4, uh C3FZ, C4PZ, CZ SMR, FC beta.
00:27:37.319 --> 00:27:41.160
I mean, the kind of protocols that were available at that time weren't Q-driven.
00:27:41.559 --> 00:27:48.680
LUBAR wasn't going uh temporal to the uh center of the temporal lobe because it was all midline stuff for uh for them at that point.
00:27:48.920 --> 00:27:52.839
So you know, CZ theta beta ratio.
00:27:53.000 --> 00:27:55.000
Yeah, it was all awfully simple.
00:27:55.240 --> 00:28:03.799
But being guided by the EGQEG, uh they had two years of outcome data and they compared it to three years of standard protocol data.
00:28:04.119 --> 00:28:08.759
And again, throwing away their first year, they had three full years of collection.
00:28:08.920 --> 00:28:14.119
And they they had an 80-20 success failure if you use a soft criteria.
00:28:14.359 --> 00:28:19.319
That soft criteria is you asking your client, did you get something out of the therapy?
00:28:19.559 --> 00:28:20.519
I'm your therapist.
00:28:20.599 --> 00:28:25.720
I'm asking you if you got something out of the therapy I just did with you for the last year.
00:28:26.119 --> 00:28:30.599
You know, that's a you know, don't tell me you didn't get better, you know.
00:28:31.000 --> 00:28:37.559
This is a coercive question situation, and you know, you get 80 20 as a success rate.
00:28:37.880 --> 00:28:49.079
But let's not just use that soft criteria everybody uses because that we got a 90 ten, but it's still a s it's too soft a criteria.
00:28:49.319 --> 00:28:53.960
And you know, a little difference there is not that big a difference, really.
00:28:54.119 --> 00:28:58.759
So let's do you got rid of the diagnostic category.
00:28:59.079 --> 00:29:03.720
Your CPT task showed you had attention problems, you don't have them anymore.
00:29:04.039 --> 00:29:06.920
And also you were trained something.
00:29:07.079 --> 00:29:08.599
Did you actually get it trained?
00:29:08.759 --> 00:29:10.759
Did if you trained something up, did it go up?
00:29:11.079 --> 00:29:16.680
You know, so there was a consumption to the training change and the diagnosis went away.
00:29:17.559 --> 00:29:19.160
That's a strict criteria.
00:29:19.640 --> 00:29:22.119
The standard protocols didn't do so well.
00:29:22.440 --> 00:29:27.559
They had a 30 plus percent success rate using a super strict criteria.
00:29:28.039 --> 00:29:32.519
You know, nobody uses that stricter criteria unless you're some stupid researcher, you know.
00:29:32.759 --> 00:29:36.359
And and so we got sixty plus percent.
00:29:37.160 --> 00:29:42.920
We doubled our success rate or cut our losses in half if you use the old sloppy success definition.
00:29:43.480 --> 00:29:55.880
So it looked like the QEG guided was a big step up, and it it gave you uh more reliable, good outcomes than the standard pro goal did.
00:29:56.279 --> 00:29:58.839
But yeah, you know, there's a lot of lot to be said.
00:29:59.079 --> 00:30:08.119
I mean, we got where we s we were at that time with standard protocols being our guide, as opposed to everything had to be EG QEG.
00:30:08.599 --> 00:30:14.200
You couldn't be a snob about it because you'd always see there's success clinically.
00:30:14.440 --> 00:30:18.759
There were people that came up with protocols that weren't based on anything but their best guess.
00:30:19.000 --> 00:30:24.759
You know, and uh FPO2, a sticking electrode where nobody would think to stick a freaking electrode.
00:30:24.920 --> 00:30:32.359
And they watched the the outcomes in and the fact that the orbital frontal surface could actually influence the amygdala.
00:30:32.680 --> 00:30:45.400
So that the symptom-based, intuitive-based approaches gave us successes, but I think the precision medicine, personalized medicine of E.G.
00:30:46.119 --> 00:30:52.839
baseline guiding you to pick the best approach ends up giving you the best approach.
00:30:53.079 --> 00:30:55.160
We had really good outcomes at that point.
00:30:55.559 --> 00:31:26.200
Yeah, and you like you used the word biomarker earlier, and and and getting this idea of how's the biology, the physiology, the the individuals themselves, like how are they really fitting in and complementing this overall picture with that information and even the idea of the endophenotypes, and and you know, it with that we it feels like we even get to pull and begin talking about and thinking about environmental influence, as in just you know, what what's our state, as in what, you know, how are we living and and the inf influence of environment on genetic expression and and just all of this.
00:31:26.440 --> 00:31:32.839
It just gets so much it gets so much larger, it gets so much more comprehensive, uh, and and with that more precise.
00:31:33.079 --> 00:31:34.039
It's just so meaningful.
00:31:34.200 --> 00:31:43.319
And now to then augment this entire perspective that yes, it's been very guided by the DSM and it's all you know been working a certain direction.
00:31:43.480 --> 00:31:49.000
But now to really dive into the physiology in the ways that we can now, it's a whole other level.
00:31:49.319 --> 00:31:52.680
Well, I make quite a few statements really strongly against the DSM.
00:31:52.759 --> 00:31:57.799
And once in a while I get a psychologist or a neuropsychologist who'll come on and say, oh, no, no, no, no.
00:31:57.880 --> 00:31:58.680
It's meaningful.
00:31:58.839 --> 00:32:02.440
When I get a diagnosis of this, I I know how to treat them.
00:32:02.680 --> 00:32:06.599
And my answer is, so you say OCD tells you what they've got?
00:32:06.839 --> 00:32:08.200
Okay, there's three kinds.
00:32:08.359 --> 00:32:09.240
Which one do you have?
00:32:09.400 --> 00:32:11.240
You don't know till you look at the EG.
00:32:11.880 --> 00:32:13.799
Oh, you've got depression?
00:32:14.039 --> 00:32:15.400
Well, what does that mean?
00:32:15.640 --> 00:32:19.480
You know, is that like boohoo's sad depression with a left frontal alpha?
00:32:19.559 --> 00:32:21.640
Is it amotivation, lack of initiation?
00:32:21.799 --> 00:32:22.759
Is it singular issue?
00:32:22.839 --> 00:32:25.480
Is it right frontal, agitated depression, anxiety?
00:32:25.640 --> 00:32:27.400
I mean, what do you mean?
00:32:27.720 --> 00:32:31.079
Our ability to describe internal states is terrible.
00:32:31.400 --> 00:32:39.079
My old philosophy instructor, world religion and philosophy, said, you know, Sanskrit's got a world of words for internal states.
00:32:39.240 --> 00:32:41.000
You just need to learn Sanskrit.
00:32:41.319 --> 00:32:42.039
I'm sorry.
00:32:42.200 --> 00:32:46.839
I never really was that good with languages, and I just I'm bad with English.
00:32:47.000 --> 00:32:48.920
So I didn't learn Sanskrit.
00:32:49.079 --> 00:32:51.640
So I can't describe internal states very well.
00:32:51.720 --> 00:32:53.400
I can see them in the EG.
00:32:53.880 --> 00:33:06.680
You know, you can see uh body and brain, and you kind of put together the matrix of the body arousal and brain arousal and figure out over aroused body and underaroused brain, or you know, how that matrix goes.
00:33:07.000 --> 00:33:15.000
You can kind of describe people with some data, but there are people that have described uh consciousness as the matrix between brain arousal and body arousal.
00:33:15.079 --> 00:33:17.480
I'd point you to Anna Weiss's work as an example of that.
00:33:17.880 --> 00:33:18.440
Fantastic work.
00:33:18.519 --> 00:33:18.599
Yeah.
00:33:18.759 --> 00:33:19.160
Yeah, yeah.
00:33:19.319 --> 00:33:25.240
And you uh you could have learned Sanskrit, but you learned electrophysiology instead.
00:33:25.640 --> 00:33:26.039
Yeah.
00:33:27.000 --> 00:33:29.240
Mine doesn't have characters, it has wiggles.
00:33:29.640 --> 00:33:31.079
Uh yeah.
00:33:31.319 --> 00:33:35.160
My grandfather, um, actually he's he had uh Parkinson's.
00:33:35.240 --> 00:33:39.799
He was diagnosed when I was about 10, and that's really what sparked my interest in the brain.
00:33:39.960 --> 00:33:47.799
Somewhere along the way, I told him I was doing EEG, I was doing brainwave studies and uh recording them and with sensors and things like that.
00:33:47.880 --> 00:33:52.200
And he uh he made the joke, I don't have brain waves, I have brain surf.
00:33:52.359 --> 00:33:57.960
So uh, you know, just like all the but yes, you went in and dove into all those squiggles, different characters, let's say.
00:33:58.519 --> 00:34:00.039
Absolutely different characters.
00:34:00.279 --> 00:34:27.960
So I imagine over the years, uh and yes, there's there's probably those moments where like a lightning rod, you're realizing you're confronted with the reality of how effective the interventions are, uh, you know, the biofeedback and neurofeedback, how how effective the the precision of QEG, EG, QEG, you know, combination plus the the intervention and training methods.
00:34:28.119 --> 00:34:42.920
So um there's probably too many to name, but I guess I'm I'm curious like the maybe one or two moments that stand out where it just hit you how how real this is and and why this really mattered to you and why you kept going with it.
00:34:43.320 --> 00:34:46.920
It's because of the clinical successes I've seen.
00:34:47.239 --> 00:34:51.400
You know, uh we've taken on cases of all kinds.
00:34:51.639 --> 00:34:56.920
Uh you didn't have to be ADD kid to be uh walking through the door in our lab.
00:34:57.159 --> 00:35:05.239
Um we worked with intractable epilepsy, we worked with strokes, we worked with, you know, psychosis, we worked at everything.
00:35:05.400 --> 00:35:06.119
You name it.
00:35:06.280 --> 00:35:09.719
If they were able to walk into the lab and get into the lab, we worked with them.
00:35:09.960 --> 00:35:10.920
If they had brain wings.
00:35:11.400 --> 00:35:21.880
Um yeah, well, uh uh I've recorded some that that uh were flatline EEG recordings as well, uh, which is an another uh thing entirely onto itself.
00:35:22.360 --> 00:35:23.960
I've I've I've been in that position as well.
00:35:24.119 --> 00:35:24.280
Yes.
00:35:24.599 --> 00:35:26.199
Flat lines aren't flat lines.
00:35:26.360 --> 00:35:31.960
Uh they're isoelectric for EEG, but they've got all sorts of other crap in them.
00:35:32.280 --> 00:35:38.119
EKG, respiratory sway, and that you know, various kinds of things.
00:35:38.280 --> 00:35:45.320
And you know, it's an art to read EEG, uh flat lines aren't flat, and it changes so dramatically.
00:35:45.480 --> 00:35:50.920
If you think you mastered EG, you're a fool.
00:35:51.880 --> 00:35:53.480
You can't master it.
00:35:53.639 --> 00:35:58.760
How can a brain hold within a brain a full definition of a brain?
00:35:59.079 --> 00:35:59.559
Sure.
00:35:59.800 --> 00:36:00.440
Come on.
00:36:00.679 --> 00:36:00.840
Yeah.
00:36:01.079 --> 00:36:08.119
You know, but the solution to that quandary is a network of brains can hold in the network an understanding of a brain.
00:36:08.599 --> 00:36:16.519
You just have to hold a piece of it and network with people that have other pieces, and maybe hold a little piece of what you hold as well.
00:36:16.840 --> 00:36:22.199
It's just one of the things that meetings are a great spot to cross-pollinate.
00:36:22.360 --> 00:36:22.920
Yeah.
00:36:23.159 --> 00:36:30.280
I'm from Fargo, so I use prop analogies, you know, cross-pollination, hybridization, you know, there's you better.
00:36:30.440 --> 00:36:33.800
And I'm from Fargo, we use wood chippers too, so you gotta watch out.
00:36:34.119 --> 00:36:36.039
Yeah, I I so I've seen.
00:36:39.239 --> 00:36:40.360
My goodness.
00:36:40.840 --> 00:36:42.519
The field has been very good to me.
00:36:42.679 --> 00:36:49.079
If the field wasn't dramatically successful, I would have gone on to do something else.
00:36:49.320 --> 00:36:51.800
I can do whatever I feel like.
00:36:52.119 --> 00:36:59.800
And I still feel like this is a a field that I can deliver more progress and more success in.
00:37:00.039 --> 00:37:02.440
I haven't plumbed the depth of it.
00:37:02.599 --> 00:37:08.039
I know it goes way deeper than anything I'll ever get to, but I I'm happy to dive, you know.
00:37:08.280 --> 00:37:08.440
Yeah.
00:37:08.599 --> 00:37:09.880
What keeps you going?
00:37:10.360 --> 00:37:10.920
Yeah.
00:37:11.159 --> 00:37:13.320
Well, I'm eternally curious.
00:37:13.480 --> 00:37:15.639
I have a curiosity and a happiness.
00:37:15.719 --> 00:37:17.320
Those are the two things that drive me.
00:37:17.480 --> 00:37:21.159
And some people would say, well, the happiness is just not rational.
00:37:21.559 --> 00:37:22.679
I don't care.
00:37:24.679 --> 00:37:26.440
So I'm happy.
00:37:26.599 --> 00:37:29.159
You know, why why should I care if it's rational or not?
00:37:29.239 --> 00:37:29.480
You know?
00:37:29.639 --> 00:37:29.719
Yeah.
00:37:30.039 --> 00:37:31.639
Well, you've got everything wrong with you.
00:37:31.800 --> 00:37:32.119
So?
00:37:32.360 --> 00:37:33.719
Doesn't mean I can't be happy.
00:37:33.800 --> 00:37:34.039
Yeah.
00:37:34.199 --> 00:37:34.679
You know?
00:37:35.320 --> 00:37:39.639
Some people look and they see, there's a scar here right frontally.
00:37:39.719 --> 00:37:45.559
Uh, oh, his happiness is because of a brain injury when he was young, you know.
00:37:45.960 --> 00:37:46.280
No.
00:37:46.679 --> 00:37:49.000
I was a happy kid before the brain injury.
00:37:49.159 --> 00:37:51.320
I'm sure I'll be laughing all the way to the end.
00:37:51.480 --> 00:38:07.639
So as you keep being endlessly curious and creative and are you know, the and appreciating the art of this very scientific process, you just can continue laughing along the way and keep looking at all the squiggly lines.
00:38:08.119 --> 00:38:15.960
It's been a fabulous career because I've been able to travel internationally to lecture about EG QEG all around the world.
00:38:16.039 --> 00:38:26.599
And I helped start the Australian Society, I helped start the European Society, uh, they call me the father of the European Society, and I said, well, if I'm the father, where was all the fun?
00:38:29.719 --> 00:38:37.400
So but you know, the they've got a successful group, the Society for Applied Neuroscience in in Europe, ANSA.
00:38:37.800 --> 00:38:41.960
I've got a lecture coming up uh for ANSA sometime in another week or so.
00:38:42.199 --> 00:38:50.360
And um basically it's about DSM 1 through DSM five and how it's ruined the EG for medical and uh and psychiatric use.
00:38:50.760 --> 00:38:54.840
You know, we really did have a lot better idea of what was going on.
00:38:55.000 --> 00:39:05.800
And Gibbs and Gibbs, for instance, in the 1940 Atlas, which was resummarized in 1967, uh, the book Medical Electrocephalography, which is actually scanned online.
00:39:06.039 --> 00:39:09.559
It's it's a freebie download online now, 1967.
00:39:10.039 --> 00:39:11.719
They talk about the classical E.G.
00:39:11.880 --> 00:39:13.800
patterns that are in their 1940 E.G.
00:39:13.960 --> 00:39:17.480
Atlas, which is all landscape, a big thick landscape book with E.G.
00:39:17.559 --> 00:39:18.039
in it, you know.
00:39:18.119 --> 00:39:18.280
Yeah.
00:39:18.440 --> 00:39:19.800
Don't try and buy one of them.
00:39:19.880 --> 00:39:23.000
Yeah, you gotta have a second job to afford the damn thing, you know.
00:39:23.159 --> 00:39:27.960
It's it's out of print and it's hundreds and hundreds and hundreds of dollars if you can find one.
00:39:28.199 --> 00:39:31.639
And uh but the 1967 is scanned.
00:39:31.800 --> 00:39:50.920
And and for every pattern, they have a page and they say the incidents in epilepsy, the incidents in headache, the incidents in psychiatric in personality disturbances, and they don't have all the diagnostic categories we have now because this was before the DSM 1940.
00:39:51.079 --> 00:39:59.880
You know, DSM started in 48, year I was before I was born, and DSM one ended a a year after I was uh out of high school, you know.
00:40:00.119 --> 00:40:04.199
So uh uh I I didn't pay any attention to the DSM one at all.
00:40:04.440 --> 00:40:05.960
But you really didn't need to.
00:40:06.599 --> 00:40:14.599
It was basically uh uh post-war stress uh stuff, and it it it wasn't you know, it was thrown out with DSM II.
00:40:14.920 --> 00:40:16.760
Uh just ignore the DSM one.
00:40:17.079 --> 00:40:19.320
We're starting from scratch here on DSM2.
00:40:19.639 --> 00:40:29.719
And then they were asking for diagnostic specificity, and and DSM3 tripled the number of categories, and it's and uh they they didn't get any better at anything.
00:40:29.800 --> 00:40:40.280
Uh just a finer splitting up of small categories and uh but no added validity, just more diagnostic specificity.
00:40:40.760 --> 00:40:55.079
You're reminding me of that uh hilarious George Carlin bit where he talks through the different diagnoses and labels for PTSD, starting with I think it was battle fatigue and shell shock and all that, and just laying down the the history of the language.
00:40:55.159 --> 00:41:44.119
And yeah, so it you know, uh one thing I'm I'm wondering from you, uh the position in which all of this you have found yourself, uh the perspectives, the decades, uh all of it, you know, the idea behind even talking like this and exploring the the why behind all of this, the neuroregulation, the brain activity, QEG, uh, you know, wanting to bring accessibility about all of this to someone who's wondering about this field, whether that person is a clinician or a lay person looking for answers and and and hopefully some improvements, some options that help quality of life or whatever it might be for them or their family member, but to people wondering about this, you know, why they might want to consider this, what sorts of things would you say to those individuals?
00:41:44.440 --> 00:41:53.320
So if you want to know your patient and you want to know your patient in depth, you're gonna need to to know a lot about your patient.
00:41:53.400 --> 00:42:02.119
And one of the things that you're going to end up needing to do is think in terms of something that's not obvious behavior, the realm of the mind.
00:42:02.440 --> 00:42:04.840
Internal states are covert states.
00:42:05.320 --> 00:42:06.760
How you're gonna see them?
00:42:07.719 --> 00:42:14.840
Well, you can't ask questions about them because you get verbal answers, not really insight into internal states.
00:42:15.639 --> 00:42:23.639
If you actually measure the physiology, you get insights into the patient's behavior that's covert behavior.
00:42:23.880 --> 00:42:26.199
You can see when they're intending.
00:42:26.760 --> 00:42:29.320
You can see when they're attending.
00:42:30.280 --> 00:42:34.920
Yeah, that there's objective measurements of mind and brain.
00:42:35.320 --> 00:42:48.920
And I can hold a finger here and I can intend to move it and never move it one bit, and I can quit intending to move it, and if you're measuring the right spot, you can see when I was intending and when I was not intending.
00:42:49.639 --> 00:42:51.320
And when I finally move it.
00:42:51.639 --> 00:42:56.039
You know, uh the Bereitschaft's potential, and that's one word in German.
00:42:56.519 --> 00:42:59.639
Bereitschaft's potential is the intention to move.
00:42:59.960 --> 00:43:07.880
But the same electronegative potential frontally in a sensory area is the intention to perceive.
00:43:08.039 --> 00:43:09.559
That's attention.
00:43:10.199 --> 00:43:13.079
Attention is simply the intention to perceive.
00:43:13.320 --> 00:43:21.639
If you're looking at a dot on the wall and you don't move your eyes from the dot on the wall, you focus on that freaking dot on the wall and never move your eyes.
00:43:21.800 --> 00:43:25.000
But your attention is over on that half of the room.
00:43:25.480 --> 00:43:32.360
The right hand of the back of your head is going to light up with electronegativity, intending to perceive things from that side of the room.
00:43:32.599 --> 00:43:38.760
You're lowering the detection threshold for sensory input from that side of the room, but you never move your damn eyes.
00:43:39.000 --> 00:43:41.800
Now you move your attention to the other side of the room.
00:43:41.960 --> 00:43:47.719
The electronegativity is shift to the left side of the back of your head, because you know you're intending to perceive on the right side.
00:43:48.039 --> 00:43:50.199
We sculpt our perception.
00:43:50.679 --> 00:43:52.519
We sculpt our perception.
00:43:52.840 --> 00:43:57.800
And, you know, perceptual bias isn't just a little light thing.
00:43:57.960 --> 00:44:01.960
I mean, it's it's the nature of your damn perception.
00:44:02.199 --> 00:44:05.480
You see what you intend to see, you see what you want to see.
00:44:05.639 --> 00:44:07.239
Now you might not admit it.
00:44:07.480 --> 00:44:12.360
You know, you you might be in denial, but it's not just a river in Egypt.
00:44:12.519 --> 00:44:13.639
So it's there.
00:44:14.039 --> 00:44:18.679
So if you really want to know your patient, you have to learn these internal signals.
00:44:18.920 --> 00:44:25.320
And if you learn the internal signals and you want to really help your patient, you can teach them how to control those internal signals.
00:44:26.039 --> 00:44:27.320
That's our field.
00:44:27.559 --> 00:44:32.360
Our field looks inside at signals you can't see easily.
00:44:33.239 --> 00:44:35.159
With equipment that's very sensitive.
00:44:35.239 --> 00:44:38.199
It's like a stethoscope with electronics, you know?
00:44:38.360 --> 00:44:40.280
Uh we're not just listening for sound.
00:44:40.360 --> 00:44:48.440
We're we're looking for subtle electrical signals, and the analysis of those electrical signals gives us insight into the function.
00:44:48.760 --> 00:44:55.639
The brain is one of the more complex patterns, but you can see complexity in the heart, the muscles.
00:44:56.519 --> 00:45:02.679
Everybody thinks that relaxation is just the loss of the command to contract.
00:45:03.239 --> 00:45:07.320
I'm contracting, and now when I quit contracting, now I'm relaxed.
00:45:07.559 --> 00:45:08.599
Bullshit.
00:45:08.920 --> 00:45:09.559
Bullshit.
00:45:09.639 --> 00:45:10.920
That's not relaxation.
00:45:11.159 --> 00:45:14.440
Relaxation is when your muscle spindles relax.
00:45:14.599 --> 00:45:21.880
The muscles, cells themselves are controlled by the motor neuron, but the sympathetic nervous system controls the muscle spindles.
00:45:22.280 --> 00:45:25.880
If you really want to relax, learn some SMR.
00:45:26.599 --> 00:45:31.480
SMR gets you control over the cortical sensory motor rhythm.
00:45:31.639 --> 00:45:33.000
What does that do you?
00:45:33.159 --> 00:45:35.239
Well, it quiets the red nucleus.
00:45:35.480 --> 00:45:39.079
Red nucleus controls the sympathetic control of muscle tone.
00:45:39.239 --> 00:45:48.119
That quiets down the feedback back into the brain, all sorts of noise that your body is sending back into confuse your brain with shit that's coming back into it.
00:45:48.440 --> 00:45:51.239
So you can calm all of that down.
00:45:51.559 --> 00:45:59.400
And you can teach that if you know what you're seeing, and you can teach the person how to see the same thing and learn to control it.
00:45:59.800 --> 00:46:04.840
Well, isn't autonomic self-naming it's the involuntary nervous system?
00:46:04.920 --> 00:46:07.960
How the hell can you get voluntary control of the involuntary nervous system?
00:46:08.119 --> 00:46:13.000
Well, Neil Miller was a professor in Yale back in the early 70s.
00:46:13.159 --> 00:46:20.119
I met him at the early 1972 for the first Biofeedback Research Society meeting that I attended.
00:46:20.280 --> 00:46:28.039
And he was brilliant, but he was really challenging all of psychology to think in out of the classical conditioning.
00:46:28.199 --> 00:46:33.400
He was trying to operately condition things that were classically conditioned historically.
00:46:34.119 --> 00:46:45.239
And, you know, all the people that had classical education looked at him and said, You fool, all of those things are being controlled by subtle muscle tension.
00:46:45.480 --> 00:46:53.639
And you're controlling blood pressure, you think, but you're really doing it with subtle EMG, covert muscle contractions.
00:46:54.199 --> 00:46:59.000
So it's really not controlling the involuntary nervous system.
00:46:59.079 --> 00:47:03.880
You're controlling the voluntary nervous system in a way that you don't really realize, you fool.
00:47:09.960 --> 00:47:12.519
He had a student, Leo DiCara.
00:47:13.559 --> 00:47:21.400
Now Leo really was adamant that this was really his control of his blood pressure that he had learned.
00:47:21.719 --> 00:47:30.840
And when all these academics were saying bologna is not, he got a hold of some Karare and paralyzed himself.
00:47:31.000 --> 00:47:36.519
So he had no muscle tension of any sort to control subtly.
00:47:37.320 --> 00:47:43.800
And he demonstrated voluntary control over the over blood pressure while people were bagging him, you know.
00:47:43.960 --> 00:47:45.960
Try to get this through an IRB.
00:47:46.920 --> 00:47:47.960
You know, come on.
00:47:48.440 --> 00:47:48.679
You know.
00:47:49.000 --> 00:47:50.360
Oh yeah, yeah.
00:47:50.599 --> 00:47:55.719
You know, I have no pituitary from a brain tumor, and I have no HPA access.
00:47:55.880 --> 00:48:03.719
So, you know, there are people that wanted to study me under stress, some extreme stress.
00:48:03.960 --> 00:48:07.880
And I was fine with that, but they couldn't get it through the IRB.
00:48:08.840 --> 00:48:15.800
IRB said, You're proposing torturing this guy to see what it has see what happens?
00:48:16.039 --> 00:48:18.679
And they said, Yeah, and he's okay with it.
00:48:18.920 --> 00:48:21.079
And they said, we don't care if he's okay with it.
00:48:21.159 --> 00:48:24.840
We're here to protect the people that don't know what they're stepping into.
00:48:25.079 --> 00:48:29.639
And how do we know that he's really knowledgeable enough to say it's okay?
00:48:29.800 --> 00:48:31.079
Yeah, you're gonna torture him.
00:48:31.159 --> 00:48:34.360
He'll have permanent PTSD from the experiment.
00:48:34.760 --> 00:48:35.559
Eh, come on.
00:48:35.800 --> 00:48:36.599
They don't know me.
00:48:36.679 --> 00:48:40.039
So anyway, they couldn't get permission to to do the study.
00:48:40.199 --> 00:48:45.880
But um, you know, I don't really have a normal fear response in the first place.
00:48:46.039 --> 00:48:51.239
So I would have been kind of a unique person with no pituitary, but also frontal midline theta.
00:48:51.639 --> 00:48:53.159
No worry, no fear.
00:48:53.320 --> 00:48:56.519
It's quite something to say that there's no worry and there's no fear.
00:48:56.679 --> 00:48:57.960
Where's the evidence?
00:48:58.199 --> 00:49:06.519
I can show you a picture of me diving off a 75 foot cliff into a glacial ice water in Glacier Park.
00:49:06.920 --> 00:49:08.039
Pretty good evidence.
00:49:08.280 --> 00:49:10.440
That was a dive, not a jump.
00:49:10.679 --> 00:49:16.760
I can show you a picture of somebody jumping from there, a nice high-resolution picture, modern camera.
00:49:16.920 --> 00:49:24.760
Uh mine was a 110 throwaway, those little cameras that you turn in and they develop the film and eat the camera.
00:49:25.000 --> 00:49:27.159
What the camera get the camera back, you know.
00:49:27.400 --> 00:49:30.840
And I can show you my motorcycle with a sidecar.
00:49:31.000 --> 00:49:32.599
I did sidecar races.
00:49:32.760 --> 00:49:39.000
Uh, you know, I rode motorcycle 18 years until I get hit head head-on with a car in it.
00:49:39.159 --> 00:49:42.360
Uh, I don't have enough strength in my left hand to pick up a motorcycle.
00:49:42.519 --> 00:49:45.639
It ruined the wrist uh pretty bad when I got hit by the car.
00:49:46.360 --> 00:49:52.119
It broke my back and fractured my wrist and um but it broke the block on the car.
00:49:53.239 --> 00:49:54.119
My gosh.
00:49:54.440 --> 00:49:54.840
Yeah.
00:49:55.079 --> 00:49:57.480
So I got a little nick up on the top of my head.
00:49:57.559 --> 00:50:00.199
I wasn't wearing a helmet that day or I would have broke my neck.
00:50:00.440 --> 00:50:05.719
Uh I mean, you know, you you talked about carving your perception, and here you are happy, right?
00:50:05.800 --> 00:50:16.840
You're carving your happiness, you know, and just and doing what you love, you know, and doing doing what matters and and continuing to dive deeper and and explore as as far as you can go and just contribute to the floor.
00:50:17.159 --> 00:50:19.880
Absolutely, there's no bottom to it, so you gotta swim deeper.
00:50:20.119 --> 00:50:22.199
So if you were to guess it, I'm here.
00:50:22.360 --> 00:50:33.159
But I'm sitting here happy and uh and I'm I'm online looking at EEG, QEGs with friends and people I've met over the years, and some people that are brand new to me.
00:50:33.320 --> 00:50:40.039
I'm 76 years old, retired, and I still get to look at EGs, the things that make me happiest of all, you know.
00:50:40.679 --> 00:50:45.000
Uh it it it hijacks my cortex when EG goes by.
00:50:45.159 --> 00:50:46.760
It tickles my fancy.
00:50:47.000 --> 00:50:48.840
So it's remarkable.
00:50:49.239 --> 00:51:08.360
Remarkable story, and your your passion shines and your your dedication shines, and you you contribute so much, and and you still contribute so much, and you you carve that happy experience and and you keep going and and and doing what you truly enjoy to do, and you you help and serve others.
00:51:08.440 --> 00:51:13.320
And it's so much fun to see the societies growing and doing well.
00:51:13.559 --> 00:51:19.400
Some of the presidents of ISNR uh have been presidents uh a couple of times now, I think.
00:51:19.559 --> 00:51:20.360
Leslie has been.
00:51:20.679 --> 00:51:20.920
Leslie.
00:51:21.400 --> 00:51:27.320
But we've treated the students so well that they come back to serve the society that they had such a good time in.
00:51:27.559 --> 00:51:37.159
I would plan a really good party and then invite good neuroscientists that might not know a damn thing about neurofeedback, but they could educate about the brain and the brain's function.
00:51:37.400 --> 00:51:44.360
So we exposed them to the virus of neurofeedback, and they went back to their labs and contaminated their lab with the thought of neurofeedback.
00:51:45.400 --> 00:51:51.880
So Klemish's lag in Salzburg, Austria, world-famous laboratory of consciousness and sleep.
00:51:52.519 --> 00:51:59.000
In 2000, 2001, he couldn't come, but he sent Michael Doppelmeier, his second in charge, over.
00:51:59.239 --> 00:52:10.119
And we exposed him to Sturman and the Thompsons, and we we tried to expose him to the best of the field, and uh they kept me away from him best they could, you know.
00:52:10.199 --> 00:52:16.679
But he goes back to Europe and uh uh Klemish picks him up at Frankfurt and they drive back to Salzburg.
00:52:16.760 --> 00:52:25.400
And on the way back, they come up with three years of experimentation to do with this odd idea that the brain can be taught to change itself.
00:52:26.360 --> 00:52:32.280
So they had slow alpha with poor memory and fast alpha superior semantic and declarative memory.
00:52:32.440 --> 00:52:37.400
Let's take the slow memory people and teach them to speed up their EEG and see if it improves their memory.
00:52:37.559 --> 00:52:40.280
Is it a correlation study or is this causal?
00:52:40.519 --> 00:52:41.800
Well, it's causal.
00:52:42.039 --> 00:52:43.079
They did that study.
00:52:43.320 --> 00:52:48.599
If you speed up the alpha, superior memory, semantic and declarative memory performance.
00:52:49.079 --> 00:52:53.079
So there's alpha associated with memory, but there's theta associated with memory as well.
00:52:53.320 --> 00:52:58.039
Uh frontal uh theta is associated with recall, memory recall.
00:52:58.199 --> 00:53:03.000
And uh frontal midline theta is an extreme case for memory recall.
00:53:03.400 --> 00:53:16.199
But if the anterior cingulate lights up with theta, when the nucleus accumbens provides acetylcholine to the temporal limbic structures, and they have to be rhythmic in order to, and they have to have acetylcholine to be rhythmic.
00:53:16.360 --> 00:53:23.000
Like the thalamus access have acetylcholine to have the alpha rhythm, the limbic structures need acetylcholine to have the theta rhythm.
00:53:23.400 --> 00:53:29.960
And that theta rhythm lights up briefly in the hippocampus uh to retrieve the memory.
00:53:30.440 --> 00:53:32.760
Now, once it's retrieved, you got it.
00:53:32.840 --> 00:53:34.280
You don't need to keep making that shit.
00:53:34.440 --> 00:53:35.880
I mean, it's just a brief little burst.
00:53:36.440 --> 00:53:37.960
It's uh less than a second long.
00:53:38.199 --> 00:53:38.360
Yeah.
00:53:38.519 --> 00:53:41.400
It's just the retrieval, you know, but it's theta.
00:53:41.800 --> 00:53:46.360
And it's the good kind of theta, phasic, brief theta, less than a second long.
00:53:46.599 --> 00:53:53.719
If you have tonic theta that's always going there, that's the pathology of theta up front of ADD and other things as well.
00:53:54.119 --> 00:54:06.679
And there you are illustrating the reality of these biomarkers of the electrophysiology and how that really hones in on what's actually happening for an individual.
00:54:06.920 --> 00:54:09.079
But there are many different ways you can show up.
00:54:09.400 --> 00:54:11.559
Yeah, you can just measure how well they recall.
00:54:11.639 --> 00:54:13.719
You know, you can look at the brain at all.
00:54:13.800 --> 00:54:15.079
You know, novel idea.
00:54:15.400 --> 00:54:18.440
If you really want to know what's going on, you better be looking inside the brain.
00:54:18.599 --> 00:54:22.280
And now you're gonna know, oh, your memory's slipping with aging?
00:54:22.440 --> 00:54:24.920
Well, we can do brain brightening and speed it up.
00:54:25.159 --> 00:54:36.440
Tom Budzinski did brain brightening in uh in Florida and the Ponte Leon project, looking for the Fountain of Youth, Ponte Leon, in case you don't know the history of the fountain of youth stuff.
00:54:36.599 --> 00:54:36.760
Right.
00:54:37.079 --> 00:54:39.079
But apparently they're still looking for it in Florida.
00:54:39.400 --> 00:54:43.239
Uh but the he taught people to speed up the background alpha.
00:54:43.559 --> 00:54:46.679
And they coined the term brain brightening.
00:54:46.920 --> 00:54:49.800
Your alpha frequency is your resolution.
00:54:50.039 --> 00:54:55.000
A higher frequency is more samples per second, and you have a higher resolution.
00:54:55.159 --> 00:55:04.519
So crisper perception, sharper vision, everything's sharper and better, and the tip of the tongue memory problem is gone.
00:55:04.840 --> 00:55:06.519
So brain brightening.
00:55:06.679 --> 00:55:06.840
Yeah.
00:55:07.079 --> 00:55:17.480
You know, if if you want a permanent job, get an EG neurofeedback device and get an office at a Dell Web somewhere for brain brightening.
00:55:17.719 --> 00:55:21.159
There's a million seniors in Dell Webs across the United States.
00:55:21.239 --> 00:55:23.800
They're all well-to-do or they wouldn't be there.
00:55:23.880 --> 00:55:25.480
And they're all playing golf.
00:55:25.639 --> 00:55:30.280
You know, they'll pay thousands of dollars for one stroke off their freaking golf game.
00:55:30.519 --> 00:55:37.159
And uh so they will pay for a better memory and pay happily, and it works.
00:55:37.400 --> 00:55:40.679
So uh you'll have a line out the door that won't quit.
00:55:41.079 --> 00:55:43.719
Um, it's applicable.
00:55:43.880 --> 00:55:44.840
It's applicable.
00:55:45.239 --> 00:56:01.239
Well, Jay, I want to I want to thank you for being here and speaking with me about all of this and why it matters to you, you know, what keeps you going, and and why it's important to those of us who do it, as as well as those who are yet to learn about its existence.
00:56:01.400 --> 00:56:07.880
And uh it's such a meaningful list of experiences and and life's work that you provide.
00:56:08.119 --> 00:56:16.199
And uh just thank you for all that you do and and and thank you for sharing all of it with us here and uh explaining why it matters to you.
00:56:17.000 --> 00:56:18.519
My thanks to the field.
00:56:18.679 --> 00:56:24.519
I mean, I can't travel anymore once a year for my birthday.
00:56:24.679 --> 00:56:28.280
People from around the world come here to the little town I live in.
00:56:28.519 --> 00:56:35.239
29,000 people, two little tiny hotels, none of them have a room big enough for the meeting.
00:56:35.480 --> 00:56:43.239
We take over the yacht club, and uh we have a few days of uh high-level EEG and then a party.
00:56:43.480 --> 00:56:46.920
And my birthday party draws people from all over the world.
00:56:47.079 --> 00:56:48.599
It's it's a crazy event.
00:56:48.760 --> 00:57:01.559
We keep the cost as low as we can because we're not trying to make money, but we do raise money for students, and the ISNR receives funds uh through FNNR and the QG certification board.
00:57:01.800 --> 00:57:04.119
APB receives funds through FERB.
00:57:04.199 --> 00:57:08.519
Uh we send some funds directly to some professors to uh dole out to their students.
00:57:08.840 --> 00:57:23.639
If the professor uh wants to take 15%, we find a different person to I mean most universities want to keep 15% of anything you donate to them, and um I want to give 100% of it to the student.
00:57:24.039 --> 00:57:30.360
Uh so it's upset some professor types uh that we give money directly to students instead of through the universities.
00:57:30.599 --> 00:57:35.079
But if you want to be upset with me, take a number and stand in line, you know.
00:57:35.480 --> 00:57:38.519
So I don't serve the line, but you can stand there, you know.
00:57:38.760 --> 00:57:39.159
Yeah, yeah.
00:57:39.400 --> 00:57:39.559
Yeah.
00:57:39.880 --> 00:57:44.760
The students are those the next generation, the people that will continue it on.
00:57:45.239 --> 00:57:48.280
They're not just our future, they're also our past.
00:57:48.440 --> 00:57:49.800
Remember Leo Dakara.
00:57:51.159 --> 00:57:54.599
Without students, we wouldn't be where we are.
00:57:54.840 --> 00:57:57.239
Who publishes most of the papers?
00:57:58.119 --> 00:57:59.000
Students.
00:58:01.000 --> 00:58:06.280
There are a few that are the lead author, one author only papers that are out there.
00:58:06.440 --> 00:58:08.840
Clemens has published some papers on E.G.
00:58:09.400 --> 00:58:12.280
that are single-author papers that are fabulous.
00:58:12.519 --> 00:58:18.760
But most of the time it's the student and a couple of other people, and the professor is the last name on the list, you know.
00:58:19.079 --> 00:58:22.760
But without students, we wouldn't have the field we have.
00:58:22.920 --> 00:58:24.840
It's not that our students are our future.
00:58:24.920 --> 00:58:28.519
We're standing on the shoulders of giants and their students.
00:58:28.920 --> 00:58:29.639
Absolutely.
00:58:29.880 --> 00:58:30.440
Absolutely.
00:58:30.679 --> 00:58:36.920
Yeah, to start somewhere, you know, running around in uh in Fargo with your equipment and who needs that IRB.
00:58:38.519 --> 00:58:41.400
You know, you can become an EG tech.
00:58:41.880 --> 00:58:49.159
You can become a neurofeedback tech under the supervision of somebody's license and have a fabulous career.
00:58:49.400 --> 00:58:55.480
As a tech, I've always had doctors over me, and it's always been easy enough to find a doctor that wants to work me.
00:58:55.559 --> 00:58:58.119
So uh, you know, being a tech isn't a problem.
00:58:58.280 --> 00:59:00.119
Uh you've got a good life in front of you.
00:59:00.440 --> 00:59:01.000
Run with it.
00:59:01.239 --> 00:59:02.360
It's a it's a wonderful place.
00:59:02.440 --> 00:59:03.719
That's where I started as well.
00:59:03.800 --> 00:59:04.119
Yeah.
00:59:04.280 --> 00:59:05.719
It's a good place to be.
00:59:06.280 --> 00:59:07.639
Well, thank you, Jay.
00:59:07.880 --> 00:59:09.000
I really appreciate this.
00:59:09.159 --> 00:59:11.719
And uh it's been a true pleasure to speak with you.
00:59:11.880 --> 00:59:12.920
So thank you very much.
00:59:13.000 --> 00:59:15.960
And uh enjoy your birthday party.
00:59:16.440 --> 00:59:19.079
Well, party on, as they say.
00:59:20.199 --> 00:59:23.880
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00:59:24.039 --> 00:59:30.840
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00:59:34.679 --> 00:59:39.639
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00:59:39.880 --> 00:59:44.760
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00:59:45.000 --> 00:59:48.760
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01:00:04.679 --> 01:00:05.639
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