Okay, let's see. All right. So we basically let's actually open again the exercise. So

maybe this was the exercise from Raoul, but let's go on Strudon. Let's download the one

that I gave you. So the first task was after you had to load the.mg file, you had to plot

each channel. So let's, this is, I already showed it to you during the lecture, but let's

have a look together. You have two ways. So one way is to do it like Raoul did, and you

can plot all the in subplots. You can use subplots like this. And as you can see, you

can see basically all of the channels, but you can also do superimpose them. You will

lose of course the scale, but you can, you will have all the graph in one plot, and they

will look like this, basically. So this is, both methods are okay. For example, if you

have one channel that has a signal to noise ratio that is very low, you can spot it. You

can spot it by both ways. You will do here. Maybe one suggestion here will be to fix the

Y scale. So you have the same Y level, and then you can spot the channel, but also you

can inspect the signal. So for example, you can zoom in and see how the channel look.

These are monopolar recordings from the muscle, from the surface of the muscle. So it is okay

that there are very highly correlated between each of them, but they should not be the same.

If they are the same, it's because there is a short circuit. And from plotting the signal

in this way, you can understand it. You can understand if there is a short circuit and

if there is something wrong with the recording. And as you can see, I mean, although they

are very similar, you can see that there are differences because this is actual potential

that propagate over the muscle. Okay. And this is why they look similar. And also they

are delayed. If you, for example, will check how these are delayed, you will find that

you will measure the conduction velocity of this action potential. So we did the first

task. So the second task, let's see the second task.

Just one remark. I uploaded my solutions too.

Okay. Okay.

So you don't have to open it.

All right. Very good. Very good. Yeah. I will actually open that. Where did you, you put

them in here? Here. Yes. Okay. Okay. Just okay.

I forgot to upload it and then I just...

That's okay. That's very good. Thank you for letting me know. I will also open this.

Hello, professor. I also have mine, but I did them so late last night that I have them

as only pictures. I didn't put them in there.

It's okay. It's okay. It's okay. It's okay. So here we will go through how it should be.

So if you've done it and not uploaded, you can compare by yourself. So which is...

Yeah.

So yes, exactly. So with the time and then the number of channels here. So something

to always to keep in mind when you plot it like this, that you lose the Y scale. But

that's okay. But that's okay.

So I will close the windows here. So we don't have any more email. And that's great. So

this is great. You see all the channels.

So then we... Now let's move to task number two, which is exercise one. So okay. The root

mean square of an image signal is calculated over a period of time and is the root mean

square of the average power of the signal. In other words, the negative. So this is a

rectification basically of the signal. So here we ask you to compute it in different

time lengths. So 50, 100, 200, 300, up to 500 milliseconds. And then to discuss the

effect of timeframe in a few sentences. Compare the time windows and describe what change.

So let's first look at the exercises. So this is... So, okay. This is, I don't know. So

this is from you, right? I guess. Yes. This is for you. So here we have the window size.

Correct. And so this is a very good way of doing it. Exactly the way that we asked. And

exactly the same. Very good. And so let's plot. So maybe I will plot first what we did