Markdown lines 339–473 · Part 3 of 6
Another way to test this would be objectively with some sort of external measurement device in
order to determine whether or not they are in fact, in a different state of consciousness, are
they performing differently? So there have been various tests done on performance level
things like let's point to gym lanes, for example. And he measured people's ability to pay
attention whether they were exposed to Delta binaural beats or beta binaural beats. And he
found they didn't pay attention so well, when they were in Delta binaural beats, but they were
pretty crisp with beta binaural beats. That was an outside measuring device, object objective to
see whether or not performance level changed. And there are researchers sitting right here in
this room who have done that kind of an experiment as well. A third way to look at this is to get
back to the roots of brainwaves themselves, where these actually changing brainwave arousal
levels. Well, a way I could do this, live it work really well. The way I could do this intentionality
is to measure a baseline EEG recording here. Before we even turned on the Hemi sync
sounds. Let's measure with the regular brainwaves we're doing before we turn on the Hemi
sync sounds, then at regular predetermined intervals along the way. We'll measure their
brainwaves. And then after we turn off the sound, we'll measure their brainwaves again to see
if we're getting back to somewhere near normal again. So we have this 45 minutes of changing
sound patterns happening. And we have a before and after, and six recordings during that
period of time. Now what we're chasing after here is the overall brainwave patterns, changing
the overall pattern, not the frequency following response. So these recordings are done in
what's called a free running EEG real time free running EEG. So I said, I have a good way to
do this. Let's let's do that. We could get the ultimate answer. And so we hook up the subject to
this. And we run and find that the brainwaves down here, low and delta is progressing along
here. And whoa, that delta is going up at the same time the Delta stimulus is going up, and
then it drops off. Well, gee, that's really good. What about if we look at the alpha, what
happened to the alpha at the same time because that was important? Well, here's his Alpha
brainwaves measured in the back of the head here. And they dropped off, down here when the
Alpha dropped down. And they came back up here when the sound turned off. So Okay, time
to go home. We've proved it. Right. It works. Well, it was certainly promising to find somebody
that responded this way. But what if I just got lucky, you know, me? What if this was the right
guy? You know, what if I just got lucky, I was sharing some of these statistics with Steve here.
And he said he likes to see things that are really robust. And we shouldn't depend upon
mounting our numbers of subjects to make something good happens statistically that he likes
to see something really powerful and robust that we can count on it happening. I like that too.
When I take my aspirin because I have a headache, I don't want to know that only 57% of the
time this is going to work. I'd like to know for sure this is gonna work. So what do I do? Then
I've got you know, all these people that want to volunteer for this. So I say okay, let's run some
more people through the protocol and see what happens. Let's try running a bunch of people
and I won't tell them what's supposed to happen. Okay, I won't tell them. Okay, make good
brainwaves. Now, I let the computer generate the brainwaves and I tell them all the same
thing. I want you to relax, pay attention to the sounds and don't move. Because if you blink
your eyes and stuff, it boosts up the brainwaves and I got thrown away. So I made sure that
nobody knew what sounds they were hearing what was expected to happen. Just to listen to
the sounds, I didn't say I'm testing your concentration tapes or stay awake. I'm testing asleep
tape. So go to sleep, you know kind of thing. I didn't do any of that. Now, there is one
overriding characteristic. I mean, they are coming to the Monroe Institute and they're going in
the booth. And that kind of has something good is gonna happen. I bet it really go to a deep
level or something, you know, and I understood that that was there even if I didn't say it. Okay.
We'll address that in a minute. But What happens if I run 20 subjects through? Well, it turns
out, a couple of them blink their eyes and I had to throw them away. So I wound up with 18
subjects with 18 subjects, the curve isn't quite as dramatic with 18 subjects, I see that it goes
up a little bit here, and then rises up here goes across, doesn't rise up like that one subject did,
but then it does drop back down again. So I am getting a little bit of an effect anyway, it seems
like at least the things are going in the right direction, the dots didn't go the other way. Over
here on alpha, the alpha swings down here, jumps back up, turn it off here. So that looks pretty
good. That looks pretty interesting. It's moving in the right direction. But I have to ask myself
some questions now about these movements and about these directions. You know, are these
things going to happen? Anyway? Are they any way related to the stimuli at all? Then if I
played Beethoven's Fifth in there, they would have done the same thing? Are these in any way
related to the various stimuli that we're getting? There's lots of ways to ask that question in lots
of ways to seek an answer to that question. I was ready to say real question is that they went
loose, I played these terms, this is what happened. What other conclusion can you draw? Well,
unfortunately, we've been given some more reserved ways to look at this Pearson correlation
coefficient is a pretty easy thing to understand. We're looking for the number one or the
number minus one Pearson correlation coefficient, meaning if I had two things changing, and
they were totally different than each other, they would have a low correlation. But if they were
exactly the same, then it would have a one to one Pearson correlation. On the other hand, if
they were exactly opposite, they would have a minus one Pearson correlation. So one way to
ask yourself the question, do these things have anything to do with each other? Let's look at
the Pearson correlation, we see it's point eight, nine, almost point nine zero. So we have about
a 90% Pearson correlation, so that's fairly good. On the other hand, there's another kind of
statistic you can do over here regression relationship between the two. And that takes a loop
and looks back takes a loop and looks back takes a loop and look back and tries to tell you in
numbers is one causing the other? Is there a probability that one is causing the other and we
see here with the regression relation between the stimulus and the response here was at a p
value of point o one. So we're looking for things below point o five. So that's fairly low. What
this is telling us is that probably probably, the stimulus is causing the response. It's just a
mathematical probability thing, probably, the stimulus is causing the response. If we look at the
same situation in alpha, however, it isn't so favorable down here, we're only at 75% in the
Pearson correlation, and this is point O eight. What this says to me is taking away the Alpha
stimulus is not causing the reduction in alpha. It's just happening with taking away the alpha is
not causing the reduction in alpha. Actually, I don't have the chart of it, but I worked the
regression of reduction in alpha with the Delta stimulus and found that there was a positive
relationship. So it might be a little bit more appropriate to say that as the Delta increased,
because we're playing with percentages, just by mathematical fact, the alpha went down as the
Delta increased. But we cannot say that taking away the Alpha caused the reduction in alpha.
So remember, we're chasing after did these changes make any sense?
39:35
You know, and delta went up. But did one was one causative, are they correlated and taking
that question to the next deeper level, is to ask yourself, were those movements statistically
significant, meaning that they don't show up anyway? Or maybe their alpha would go down
anyway. Was there a statistically significant change more than would be expected just by
chance alone that they might? So the next thing we have to look at then is is that possible?
Was there actually a change? Are they considered significant changes? Or do these things just
happen by chance anyway, just by chance he was in the booth, just by chance I was playing
Delta, just by chance, his Delta went up, just by chance at all? Well, there's a way to test that
mathematically to see whether that's really what was going on. Look at all these numbers.
Them, here's the Thank you. But now what we have to pay attention to on this chart is, this
bottom set of numbers here have little asterix by them, which means that they are statistically
significantly different individually. Of the stimuli. The last four, I'll show you the chart in a
minute, were different, statistically, they didn't just happen by chance. And the fact that four of
them happened out of the six was very significant, statistically. Now, what that looks like in a
chart, it's a little easier to see is that, that as the stimulus went up, these four changes were
different, they were really different. They didn't just happen by chance. They were really
different. And because of the regression relation, not only were they really different, they were
probably caused by this. They were probably caused by the stimulus and they were really
statistically different. Let's play a game now with this and say, why wasn't different up here?
You know, what was the deal, I turned the tape on, how come it took the guy about 10 minutes
before he got up to this level. While remember, we had the opposite stimulus happening during
here, maybe Alpha stimulus kept him from going to that level. Maybe it takes about 10 minutes
before the Hemi sync really kicks in and really has an effect. So kind of as a sideline, we can
kind of second guess what's going on here. And wonder what the deal is, maybe we can say,
until the stimulus reaches 60% or 40%. Here. It's not effective, it has to reach a threshold of
stimulus effectiveness before it kicks in, or maybe it's a matter of time, Steve, and his numbers
wanted to look at this as a analysis of something happening over time, rather than being
independent variables. And that's certainly a way to look at this as well.
42:55
Again, this is just to remind us where we are, we're trying to determine whether or not the
changes that have happened here. Do they just happen anyways, when you put someone in
the booth, I mean, I was really concerned about them. This maybe this was some sort of, we're
going to invent something here, a Pavlovian vestibular response, you know, you put the
headphones on, you lay down, you're out of there. Was that what this was, it didn't have
anything to do with the sounds that were going in the years here. So it was very important to
look at this whole situation. Let's look at the Alpha side of the house here. In the opposite side,
it was even more dramatic, we have five of the six stimulus conditions, statistically significant,
and again, highly significant that they should all come together like that graphically, as the
Alpha dropped off. Remember, that's when back to the Delta slide that when the Alpha
dropped off is when the Delta actually kicked in. But the reduction in alpha here, five out of the
six times were statistically significant changes, they were not expected to happen by chance.
So it looks like I was onto something was a very interesting kind of study. And I spent many
weeks poring over this data and patting myself on the back about how this seemed to work out