Screen. You also see I tracking by the way those 2 circles in the middle is eye tracking. So we will then correlate the brain activity to how strong these red arrows are.
So kind of like the volume if you've will of the of the visuals. How much movement is there?
And what we find is that when people are playing this game, so this is the stimulus response correlation now.
So this is yoke to the stimulus, if you will, specifically optic flow. So if you look at people actively playing this game, you get pretty high correlation.
So these are not small numbers, by the way. With it eg activity and this optic flow.
Now this is the total optic flow anywhere on this screen. And then this is when they're passively viewing.
Now, we also did another thing, which is we told them that they're controlling the video game with their brain activity.
Of course that was not true. They were just told that that's what they're doing. So this is brain computer interface.
They thought, oh my, this works really well. It actually we're playing back a previous game and they had no idea that they're not actually controlling this the screen so we call this sham play and sure enough people that thought they're controlling the brain the video game.
Had brain activity those just as correlated if not more to the optic flow in the screen as the people that were actively playing.
My point being that this metric of correlation really captures how engaged the audience is with the game, even though they're not really controlling the input.
So, just like with the temporal response, approach that you guys are familiar, we extract components.
So rather than doing this one electrode at a time, we do it again with components. So this is the first, second and third component and this are the corresponding filters.
So, so how's this whole thing work actually? And here's a cartoon and this cartoon is already too complicated.
So I'm actually going to, stop screen share. And give people the opportunity to ask and instead I'm gonna show you something on the board to explain what we just did.
You guys see the board?
Yep.
We're still here. Any questions so far by the way?
No? All right. So the approach that maybe some of you are familiar with is that there's some that some stimulus feature.
That's coming in. Let's call it. So this could be if you've been working with sound, this might be the spectrogram over time or it might be the speech volume over time or something like that.
And, that goes into A. We call this a linear system, right? This is the so-called temporal.
Responds. Okay.
More conventionally, by the way, this is called the impulse response. So I, I resent at Lala for calling it what it should not calling it what it should be but at any rate temporal response are signed by me is impulse response and then the output It's another thing, maybe the brain activity.
Okay, and so this is this is essentially the model of the temporary response function and people call this model so why Is age convolve with x of t.
They call this encoding.
This is an encoding model. How does the brain? Signal encode the feature of the stimulus.
So that's that some of you are familiar with. Another approach is very popular in. Also in EG, but also in the FMRI is to decode the brain activity.
So a lot of what the news articles pick up about brain reading works kinda like what I'm just about to draw, which is you have many brain signals.
Maybe like so. And so yeah, at every moment in time you have a whole vector. Let's call this y vector y of t.
Okay. And that vector y of t. Let's multiply with a matrix.
And outcomes the stimulus feature. So the equation here is something like X is, W times.
. Okay. And of course these things can also depend on time This could also be multiple features and this can also depend on time.
And so this is typically called decoding. Brain decoding.
This is a brain reading, right? So all of the news articles you see about, oh my god, we can code speech or music or whatever else.
From brain activity or some flavor of this. Decoding. So what we did is what you just saw.
But we did with that video game and other applications is sort of a hybrid thing. It's a bit above.
So we take this stimulus. And pass it through an encoder with a convolution like so.
So this is again X of T that generates the signal C of T and on the other hand We're going to decode.
So we have the brain activity.
Make sure it here. That's why Okay.
It's another signal, the C of COT and the goal is that there as correlated as possible.
So this measures correlation and we call this stimulus Then. Response. Correlation.
Okay, so that's the approach and and this is sort of a hybrid between encoding and decoding.
All right, so that's that's That's the approach that we have taken with the results that you just saw on video games.
We've done this also with speech and a whole bunch of other things. So let me go back to screen share.
So I'll show you some more results. Any questions so far?
Yeah, there was one in the chat and then another that might want to ask you in person. In the chat, can one move from sensor space to source space with the metric just presented to quantify interpersonal correlation?
Not quite sure.
Yeah, Yeah, so. To go back to this. To this picture.
So this picture here, this would be the, this would be the source.
Right? And this would be the sensor.
And yes, you can go back and forth between the 2. So they. Going backwards is what's called the forward model.
So this is a forward model and this is the backward model. So the algorithm really extracts the backward model, but you can always reconstruct the forward model from it.
And in fact, what I was showing you before was the forward model. So going from source space to sensor space, where you can also go that way.
In fact, that's what the algorithm really does. By the way, the algorithm to find these things is called canonical correlation analysis was developed by Hartling in the fiftys.
So it's a really old algorithm. It's not something we invented. What we invented is this sort of idea of doing both decoding and encoding at the same time.
Alright, other questions?
Yeah.
Yeah, I heard a quick question about the type of stimuli that you can use. So I'm more familiar with auditory evoked responses thinking of like bond die or something repeated over and over.
Can you do this kind of analysis on that using that type of stimuli or does it need to be continuous stimuli?
No, anything works. Now if you have events like the ones you have, the conventional approaches, event related potentials, it turns out that doing something like this or even the temporal response function without by the way this this thing here what I drew here if you get rid of this you basically it back here and this is what
If you get rid of this sort of complication of extracting components, but you do an electrode at a time.
One electrode at a time, you get the conventional temporal response function. So there's a temporal response function or this one work for events like the ones you have and the answer is yes.
In the and the impulse response you find here here the temporary response function you will call an evoke response.
Or an event related potential. So absolutely you can do with either.
Other questions?
Okay. And then my other my other question I had is you're just displaying 3 components is that just because of those are the 3 largest or is it like ICA where you know if you have a hundred 28 channels you get a hundred 28 components out of this.
You get a hundred 28 components here too, but they they're nicely sorted by relevance.
Okay.
The first one captures most of the correlation. The last one captures the least. And mostly you're in the noise at the end.
Okay.
So unlike ICA that doesn't have a preference for one component or another, this decomposition technique gives you a sorting for the one that explains most of the stimulus and then the second and the third and so on and between each other they are actually uncorrelated.
So you can think of this as doing temporary response function and some sort of blanket composition at the same time.
Are they?
Perfect.
In fact, let's see ICA in front because you're doing it here.
Sorry I had a question, but I just totally forgot it when you were saying that last part.
I'll ask it again if I think of it.
Okay. We're good. I'm happy to chat. Not show any more slides.
And Lala in the house. Very nice, hi, Dad.
Yeah.
I have a question, Lucas. Do you think in terms of number of subjects, would there be?
An upper bound what you can get like is it the amount the case that if you keep adding subjects it will keep getting better.
Or which are the 2 techniques, the inter subject correlation, you know, thing?
I suppose, sorry, I'm talking about the CCA style, the inter subject correlation.
Yeah.
The CTA. So, you know, the more subjects you have, the more robust it becomes in a way.
But also it gets blurry. So you might argue that eventually too many is too many. Usually we get nice results.
With this starting at 15 and upwards. You can, by the way, Alan de Javigne, he has done it.
With extracting components for each individual subject used on similar things. So you can mix and match whatever you want, either single set of components for all subjects or each one gets your own.
It's a usual sort of per number of parameters issue. Yeah.
Would you ever mix different types of recordings?
What do you mean by different types?
Even, you know, starting from simple to, you know, data recorded from here in my lab and recorded in your lab.
Still, EG, or even EG, MEG.
If they are, if they are in the same sensor space, so if they have the same location of electrode, I think that's quite all right.
If you are combining activity from different sensors all together, I would have to think a bit about it, but I think you could potentially play games and combine.
Okay.
But I have to think about it, certainly not. Off the shelf code. So let me show you some code for this.
So, into this is stimulus response correlation. Does the same website and the resources you click here it brings you to Yasik's GitHub.
You download the code here, download. I did this already, so let me change to MATLAB to show you.
What that looks like. So.
How you doing that? I remembered my question. Are all the components that come out, are they all nonlinear or do you have like all the linear parts of the relationship in the first component likes.
CCA, I think it is.
So no, this, this is now back to the linear world. So here you're extracting linear relationship between the stimulus feature and the brain activity.
So this is very much like the temporal response function. You're limited to linear response. There's no nothing nonlinear here.
Oh.
So let me demo the code that I hope I answered it. This is the code that I have from Yasik that says demo let's see let's run this thing Okay, demo.
Okay.
That's not the demo, give me some sort of math, that's not the one I want to run.
I have to change here now again.
So these are the correlations in the first component.
So these are the 3 components that they extracted and they correspond corresponding impulse responses. This is on date of a single subject, so these are a little noisy.
If we had run this on more, say, 10 or 15 would have come out a lot smoother, but what do you see here?
The first one at least resembles the figures in in the paper. Which is this figure here where we see that that the the activity so this was again this was the shamplay versus passive viewing we see that the activity around 400 ms was weaker in the past viewing and that was predominantly in the electrodes in the back of the head.
As indicated by this first component.
Can we do this with other stimuli? So, we have done it with a lot with video.
We have done it also with the usual things like speech envelope and even the semantic dissimilarity signal and a whole bunch of things.
So you can use this pretty much where you would usually use it in speech. One example that I thought was kind of cool, we did this with the optic flow in different locations of the screen.
So we looked at how much correlations or stimulus response, so how correlated is optic flow between a location and the screen and the brain.
And so this gives us essentially a map in this video. Where our subjects were responding to. I would argue tending to.
And interestingly enough during active play they're paying attention to the horizon, the upcoming buildings.
Presumably because they're making an attempt not to crash into them. And we don't see that as clearly in the passive viewing.
By the way, the eyes are always peeled to the center of the screen. So that's not related to eye movements.
So let me just summarize what I've told you so far. Is sort of a partial summary of this talk.
Natural stimuli drive the brain, but only when the mind is engaged. So basically when you're not attending, we had seen that with inter subject correlation, the inter subject correlation drops protect precipitously or in this video game we saw that when the stimulus when the brain is no longer correlated to the stimulus then we know that people aren't paying attention and aren't
really engaged and these are basically 2 metrics that we have used. To measure this. So, so this sort of concludes that part.
The main the main title of my talk though is not just about the brain but about the body. So I'm gonna switch topic on us.
Unless there are questions. So far.
So now we're gonna start working with not just the brain signals, but we started looking at the heart, specifically the heart rate.
We capture now respiration. We are going to measure gaze position. The pupil size in addition to the EG and we do this with audio only or with audio visual stimuli.
So videos or continuous speech, audio books, that kind of thing. So this is work that, you know, in the last several years, I would say.
We had some really interesting results. So here I'm gonna show you some. Basic signals, this was collected while people were watching educational videos.
You'll get to see one of them in a minute. So this is EG. The 2 colors are 2 participants.
This is the heart rate. So you see how the heart rate is going up and down here.
This is over several seconds. So this little bar here is 1 s. Our gates position you see these 2 subjects are pretty much I don't know if this vertical horizontal case position pretty much looking at the same place on the screen while watching movie.
Distant pupil sized the pupil size and these 2 people is also very correlated.
And this is respiration. So as we inhale, it goes up and when we excel it goes down.
You see that they seem to go up and down but it's actually they're not very correlated.
So we published this in PNS Nexus. I'll show you the gaze. And here I have audience participation so I need you to all pay attention.
So you're gonna see the same video. And on the left, you will see the red dots that you see here are the case position of people watching a video and on the right the same thing but it's different people in one of the group of people either left or right, were paying attention.
And on the other side, people are weren't paying attention and the task for you. Will be to tell us or guess after you watch this.
Who is paying attention? Is it the people on the left or these red dots on the left or is it the people on the right?
So I'm going to show you the video and then we're gonna take a poll, okay, an informal poll.
Ready?
So which one of the 2 groups are paying attention? When you ask someone to draw something like this or or this, even though you don't worry, you know, this, this, this, this, this, this, this, this, All right, so now comes my poll.
Raise your hand if you think that the people on the left, these people or paying more attention. So everybody that thinks it's the people on the left, please raise your hand.
We'll take an informal count.
Okay, we got about. 32 people raised their hand. Okay, everybody raised a hand down. And down, hit down.
Now we're gonna see who thinks the people on the right. We're paying more attention.
No 1. One.
Oh wow, that's a biased sample. No, it's a totally biased sample.
Alright, well, good. You guys were right. I mean, the majority has the right answer. It's the people on the left that were more attentive.
Essentially, when people move their eyes together, are following the same thing on the screen, it's because they're paying attention.
If they're not following what other people are looking at, then they're probably spaced out.
And this is here, this is what we report here for all this signals for EG, heart rate, gaze, position, and pupil.
We're measuring interceptor correlation, but now we actually also resolved it by frequency.
So this is essentially a coherence measure. So we wanted to know in which frequencies this happen.
And before I was just showing you overall, I'll correlate our people. Here you see that the EG correlation is actually dominant at pretty low frequencies.
Most of you or most of us when we analyze EG, we'll learn activity of 1 s or faster.
Most of the activity that seems to be synchronizing, yes, there's something above one herds, even around 10 hertz.
But most of it is pretty slow. Here's for heart rate. And by the way, how do we measure our heart rate into such a correlation simply was Pierce and Correlation, nothing fancy, except here we first, our bandpass filter the signals.
So you see that there's a nice peak here around sort of 10 s point one hertz is 10 s timescale where the heart rate goes up and down.
So basically when we're watching a movie and this is also true when we're listening to a story.
Our hearts synchronize. So this is kind of a cute message here when you're driving in your car next time and you're listening to a part well not a podcast a radio program that's playing a life then your heart rate if you're really paying attention to the story.
Will be synchronized with out of all other listeners. So we do share something in common.
When we really go to the movies or we listen to a story at the same time. Our hearts are in sync.
The same is for the gaze position is a very strong effect with video. And the same is true for pupil.
So. When we look at this metric of inter subject correlation, it turns out it's very predictive of whether people get content of these stories they saw correct.
So these are educational videos we asked afterwards quiz questions about the content of the video and you see the pretty much all metrics except respiration are predictive of how many questions people got correct.
So these are questions that we're asked right after the video. So whether it's heart rate or EG or G, so beautiful, they all predict.
The level of synchrony in the in how similar you are to a group will tell me if you're paying attention therefore if you remember what was said during the video.
Again, if there are any questions for free to interrupt me.
It's okay, you've got, you've got people here answering questions for you now.
Kens is here.
Okay. And in fact, these, yeah, in fact, these metrics seem to be correlated with one another.
So subjects each.is your person. People that have a I intercept a correlation they I see in the in the sorry in the in the EG will also have a high inter subject correlation the heart rate that is if you if your heart is speeding similarly to somebody else then so is your brain.
And similarly for gays and pupil. So this seems to all capture a common underlying phenomenon.
So the theory we have behind it is that it's really the cognitive processing of the stimulus.
Is what entrains body in mind. So essentially say the video that in this similar thing happens with audio only.
So subject one may be attending to the video and subject 2 and and that will synchronize eyes hard.
It may be other signals and the level of synchrony in one modality will be similar to the level of synchron in the other because body in mind are really all in trained.
Oh, to the stimulus but only again if they pay attention if they're not paying attention none of this is true and this is how this the signals are correlated I mean co-modulated
Now, you see that I've drawn these errors in 2 directions. Generally, we think that the brain controls the eye, maybe the brain controls the heart, but there is also this notion that the body controls the brain to some regard.
So, so perhaps it's a two-way street and so we're not the first to suggest that the the model that we have really is a sort of a simplified model.
We know that when we're listening to a story, somehow that seems to drive also not just the brain, sort of the cortical brain that we pick up with EG, but when we listen to a story, it appears to drive the autonomic system within drives the pupil, the gaze and the heart rate.
But perhaps the opposite is also true. Perhaps the heart rate or the gaze or the pupil also drive the autonomic system and this is sort of has a bottom-up effect.
Does that make sense? So here we ran a couple of experiments where we intentionally control luminance.
Case and breathing I won't be able to tell you all of this we just don't have the time for it today.
But I'll tell you a little bit of the results and it's already complicated enough so here's some results of People just listening to a story.
So this is short stories, maybe 10 min, or 5 to 10 min long. They're kind of fun and interesting to listen to.
And what we first see is that the pupil synchronizes, but again only people are paying attention and the synchronization is pretty stark.
So. Attentive again at this very slow rate the pupil of individual synchronizes but not when they're distracted.
This is a very strong effect. We had seen this with video, but with video there's always the concern that maybe it's just the fluctuations of light that cause the pupil to dilate and contract.
The same happens apparently when we just listening to the story. By the way, people are watching looking at a blank screen we asked them to focus on the screen and There's a fixation cross, but regardless of the fixation cross whether it's there or not.
The pupil and trains. Apparently to the story. So there are moments in the story where everybody's pupils dilated in moments in the story when people's pupils are constricted, but only if they're really attending to the story.
We correlated the pupil with the EG. So this is conventional temporary response functions. So 0 is 0 delay.
This side means that the EG is ahead. Of the pupil and this side means the pupil is ahead of the EG.
So it's not clear which way the castle relationship goes. It seems to be that that a portion of the EG seems to respond the pupils seems to flex to it before they EG does and vice versa.
So we don't have a clear causal story. But the the important thing I want you to notice is that if we look at where this activity is coming from it seems to come from this sort of Ontario.
Central. Portion of the brain which I've seen in many studies that look at the Ontario cingulate so we haven't done source reconstruction here but we highly suspect that that's what might be going on.
So that's the pupil while people listen to narratives. How about the hard?
So the hard, we look at the heart rate, not the Ever beat, but the heart rate.
And we did again this temporal response function business so we take the pupil size and try to predict the heart rate from it.
And we see that there is a significant risk. Just a little bit on this temporal site that corresponds to the heart being ahead of the pupil.
Similarly, if we try to predict the pupil from the heart, we get a more clear story here.
Which again is the site where the heart is ahead of the pupil. So it seems to be that the fluctuations of heart rate are ahead of the pupils fluctuations.
And we do the same thing with the EEG signal, so can the heart rate predict the EG signal.
And by and large, we see positive time lags here, which means that the heart is ahead of the EG.
Now this is quite surprising. This is telling us. That the heart beat is slowing down or speeding up.
Almost half a second to a second ahead of a corresponding what we would call. evoke potential or potential fluctuation and once again same culprit same location on the scalp and this by the way is true with a person attending this track or resting so inherently it seems that the heart rate fluctuations proceed fluctuations of the EG of this sort of slower
timescale. And this is once again activity that to us looks a lot like Ontario Singular.
So the story that emerges here that perhaps the heart and by the way we drive it we can also drive with breeding that confirms this observation that I'm drawing here.
So breathing is known to drive the heart rate up and down. Seems to be driving down the autonomic system, which then affects the pupil size and also the electroencephalography.
So there seems to be evidence here for a cause, for a bottom-up effect of autonomic function of the heart onto a pupil and brain.
So, one last thing that I found cool that we found out is about gays. So it turns out that when we are listening to a story attentive, our eyes are moving around more.
So, the power of case position is basically how much are our eyes balancing around on the screen. So when we're attentive or actually moving our eyes more than when we're distracted.
I don't know if you noticed it, but somebody that's not listening to you, they tend to sort of stare blankly at you.
They're not really following anything. So they attentive listener will actually move their eyes. The disattentive, distracted listener would sort of stare blankly in the middle.
And it turns out that this amount of fluctuations of eye movements Also correlates between subjects.
So the amount of how much you're bouncing your eyes around changes in a story. So moments of the story.
Well, I don't know which ones, but some moments of this story are people move their eyes more than other moments of this story.
And if we look at that case variation signal, that case for issue signal 2 seems to correlate with this sort of anterior activity, though here it's possible that this has to do with the frontal eye field.
So I at any rate, this thought is a kind of interesting observation that I don't think is in the literature in any kind of way.
We move our eyes depending on whether we're attending or not. What we're listening.
So to summarize it all narratives drive the brain and the body at a very slow time scale, but only when the mind is really engaged.
Otherwise the body is not seen to in train at all. This sort of brain body connection may be a two-way street and we can measure these things either with inter subject correlation or stimulus response correlation.
We like to do one result in time because it gives us these sort of Casal interpretations and we like to resolve the other in frequency because it gives us an idea of what time scale this is operating at.
And with this, I would like to thank the people. Jacques C is the guy in Paris, by the way, who works on pages, disorders of consciousness who brought us to pay attention to this heart signal.
And, and these are the students I've been involved in all this work. Code is here.
You can just go to resources and download it on this page. And with that, I'm going to stop and give people opportunity to ask questions.
Well, thanks Lucas. It's really cool to see that. Pupil, you, you were come to fruition after.
Yeah.
The cog here meeting last year. That's really cool.
So if you want to ask questions, now is a great time. Raise your hand and I'll mute you.
Or you can ask a quote here's a question in the chat. If they distractor is a stimulus that is correlated with the stimulus.
How would you think it may affect the results? Would that in turn increase the ISC and is it a linear relationship with distractor strength?
So this business of how to distract people is really subtle problem, right?
The way we have done it and this was a suggestion for by Simon Kelly by the way, who maybe some of you know, Simon Kelly's idea was do not distract people by putting stuff on the screen because then you're not really sure are you analyzing activity associated with the distractor or as you analyzing activity.
That's this distraction related itself. So, so he suggested instead to do a very classic thing which is a mental task.
So you just tell people do something in your head. What's a good mental task? You can do any kind of thing.
You can ask them to think about what you were doing, what you have for breakfast, or you can ask them, well, the task we use is to do math.
So do complicated math in your head, count in steps of 7 backwards starting at say 1,000. So it's 1,000 is 993, 986 and 900 and.
7, 9, so you're really see I'm staring blankly into space And so that's the approach we take.
And so this is all silent. So there's no stimulus to confound your task, but yes, this whole business of distractors and measuring attention with a probe stimulus is a whole complicated story that I don't really have any good answers.
Hey, Lucas, I had a question as well. Sorry I was late for your talk. I was teaching this morning.
I'm still in my classroom here. Actually, I just joined as soon as I could. The question I had was the cool new findings with the I movements and stuff like that as well.
And maybe you explain this and I didn't get it at the time but the I was interested to know the kind of speed of those the size of those are these what we would consider just regular saccades are they microseconds do you need to measure and we're using some super fancy eye tracker or is it coer than that?
Is coarse. So it's literally You know, when I'm looking around, I can be looking around in a ferry, in a fairly narrow sort of area or I can be looking around a fairly big area.
And that's it. We're basically measuring the power. Resolved in time. Think of it as the saccade envelope.
Cool, okay.
Or not psychotic but gaze envelope. That's it. Variation. So there's a paper that we site that's also looked at this is case variation is a term that maybe is going to catch on.
It's not saccades, it's not smooth pursuit. We don't really care.
We're completely agnostic to it. We just like over a period of time how wide was the range that you covered.
Cool. Cool.
That's it. And so that range is what seems to be in moments of this story be bigger and other moments of the story be smaller and why.
I think it's wonderful area of, you know, research now to find out because The the thing that I find that interesting about it is it's it's incidental movement So it's more, it's totally unrelated to the task.
Just like you right now, are you touching your face, right? That's incidental movement, totally unrelated to your listening to me.
Hopefully you're listening. Looks like it. But so we do all of these incidental behaviors are seemingly totally a task unrelated and yet And yet they dominate brain activity.
Perhaps you follow that literature, but also we find that they are linked to what's what we're doing in our head, right?
We're listening to the story and apparently that's driving these, we think it has to do with arousal.
Different moments of the story increase arousal and that's when we move our eyes perhaps more.
That's that's our current hypothesis. We have to still figure it out.
Cool. Thanks.
Here's one from the chat. Do you think it would be interesting or informative to measure inner subject correlation without a shared stimulus.
Lot of research is done to study interpersonal neural synchronization where people are actually trying to exclude the synchronization due to shared sensory input.
So we use, we record people. Sequentially like you know now we're recording you and tomorrow will record another software and so on right so there's no way that be synchronized any kind of way other than through the stimulus.
Now there's a very big literature in in in what's called physiological synchrony PS.
And physiological signal where the interest is sort of when people are sitting together side by side or looking at each other interacting in some kind of way.
That somehow they are in synchronized, the physiology synchronized. Classic example.
Mother and child. Or therapist and and patient or or a performance and audience. So there's a whole sequence of a series of papers along those lines.
Where it's never really quite well controlled. Why are they synchronized? Is it because they're sharing the moment and there's some common stimulus or this is some sort of empathic you know, correlation, but I would content that if you put a screen between the 2 people and they don't see each other, then don't hear each other and they don't smell each other there's nothing
that can possibly synchronize unless there's unless you believe in some sort of you know esoteric kind of thing.
And so I think that the only way that people will synchronize is because they are somehow sharing something that they're perceiving.
And so, and so I think that understanding what it is that synchronizes people in this interpersonal situations is kind of an interesting topic and there's a big literature but let's not pretend that this is some sort of Budu synchronization.
It really is, I believe. The the the sensory person of the other person that will synchronize people even when they are sitting together.
Okay.
Hi, Augustus, Nick Cross here. I have a question. Great talk. I found your work really interesting.
Looking at all the relationships between these different biometrics. And I'm wondering, looking at this correlation you showed between the heart and the EG or heart rate.
Do you think that there are any interesting or useful, markers of cognitive function that could be from like a smart watch heart rate measure, you know, given that we're seeing a lot of companies trying to develop.
Brain computer interfaces now and And also to different NOR technologies.
Absolutely. Absolutely. I mean, the watch has a problem that's very noisy, but if you get a high-end arm band, Absolutely.
I do think that you could do all kinds of in the wild experiments and we want to do it and we're thinking of how to do them and we're doing a lot of online experiments with eye movements already.
Yes. Go for it. I think absolutely is a very rich signal. I mean, I don't know if you caught the scale of these correlations that I was displaying on the screen.
They're much bigger than EG signals. So the correlation of the pupil that we find at the heart rate.
I mean, it's a noisy signal. I give you that. You need something for the heart in particular you need much longer record because you know it's a very slow signal so you need a lot longer records.
Yeah, and presumably for
But the level of correlation is definitely higher than D. For instance. So I think it's a very it's It we're only scratching the surface.
I think there's there's so many experiments you can think of looking at pupil hard.
Yeah.
Now, I should say it's not the heartbeat. It's not like people speed synchronizes.
It's the heart rate. So your heart might be going, it's that speed up and slow down.
That's what synchronize. It's not the actual B 2 B. It's not the actual B 2 B. It's that speed up and slow down. That's what synchronize.
It's not like our hearts are in absolute But that's not the case.
Thanks.
Okay, so for the sake of time, we'll move on. Thank you, Lucas.
Yeah.
Everyone join me in thanking him with our. See there there click that button. Thanks, Lucas. That was a great talk.
I really enjoyed it. We'll move on to Giovanni.