Yeah.
Let me, so Giovanni will give us some updates, about CNSB, what he's been doing.
Over the last year or so. What we've been doing and then he'll introduce a his visiting student, Giorgio, who will give us an update on the project.
So Giovanni, please take over.
I'm stealing the screen now.
Please.
Is this the right? Can you see my slides?
Not yet.
Oh, okay. What can you see?
Sorry, 2 pictures in your group.
Oh, great. So these are the slides. And Lucas, thanks a million. That was great.
And also the format. I like this interactive. 4 months for online presentations. I'll have to switch back to the old format.
And But it's very brief. So before before moving on with the next invite the speaker who's Giorgio Piazza, I'll just give you this brief update.
On the work we have been doing but also at the end I'll give a brief mention on the travel grant supported by sponsored by a and train.
Let me just prepare a slide. One of the embarrassing slides, a lot of faces and nobody looks at, but it's it's a necessary to thank all the people that helped in the last 3 years, in, in all the work we did with the workshop and also with the resources.
So Lucas has shown this picture in different flavors on the board on the slides. But the idea is yes, we have in the different type of experiments that we deal with that the CSP.
Initiative and work for care about. We talk about, you know, some neural recordings and some Sounds, they're continuous or some videos or everything that Lucas has mentioned.
And ultimately we have an X. And the why in machine learning sense. So and next where we have if you want some stimulus features and the why where we have the neural, signal.
And want to relate them somehow. So when we have, and again, the way that Lucas described when we have multiple features, imagine and this is something that happened to be anyway we had recorded the data set thinking about phonology and then that same data set has been reanalyzed in I don't know how many different ways.
So so many different papers from different labs. Can come out from the same data. And so this this is an opportunity.
It's an opportunity to reuse data from, you know, rather than having to record a thousand times.
The same kind of experiment. And then the type of task that we're talking about, like listening to speech and music is just more pleasant.
So, it gives you better access to particular cohorts that wouldn't be as willing to take long experiments where they have to look at, you know, flashing lights or individual syllables.
So just more pleasant. Now the challenges are that we want to tackle. One challenge is that You know the correct use and interpretation of the methodologies we are talking about Right, it's very important that if we start sharing resources, we start sharing code or toolboxes.
That we also you know make sure that there is no misuse of these tools. And so that's why we try to publish papers and and and also organize this workshop where we try to provide this.
Resources for for guiding towards the interpretation and how not to use these methods.
Then the one thing that is very practical and even within the same lab. What happens is that is that we just end up using different versions of the same code.
For doing the same thing, which is problematic. It causes it increased the risk for errors and also makes everything slower.
So code reusability.
So with here CNSP at the center of this slide, what I'm trying to say is what's CNSP is is about.
So one element is Trying to share some standardized analysis code. For running different types of analysis. And of course we started from a kind of analysis we were doing already and by we I mean Aaron and I and Nick and so on of the organizers.
To share standardized code, we need to agree on some standardized format for the data. That's also another activity we have been working on.
And then trying to take some existing data sets and putting them in the same format so that basically that same standardized code can be run instantly on, all of these different data sets without wasting time.
Then of course we have the workshops where we try to to share new research and new tutorials, question and answers and so on.
And finally, we see this as an opportunity for. You know, creating a community for networking, for connecting people that are more on the methodological side and instead other scientists that are more in you know they have maybe less confidence on the methods and they want to connect with someone that has that expertise.
On doing more applied research. And for students, of course. And that's this is the kind of resources I use also in my own lab.
So every student PhD student or master student that joins, I just tell them, okay, open the CSP website and download and such and such data sets, download these scripts and try to, to, learn from these.
We have these new sections that we didn't have last year on lectures and tutorials from the from the various editions of the workshop so you you find the 2,023 here as well.
And it is not just a repetition. Every year, right? So for example, last year we covered CCA, we won't cover it this year, so we don't need to repeat things that we have seen already.
So that we leave space for new things. So check out those things. So what's new?
So the first thing that is new this year is the CND format. So the continuous event in your data format.
It's a data structure that that we proposed for storing neural data. And the is specific to continuous sensory tasks like listening to speech.
So it was as I said it was a necessity because if you want to share standardized code we had to find a way to agree on some standardized data format.
And the idea is that if you save your data in this way, then you can run these scripts that we're sharing directly.
And then it also enables the design of graphical user interfaces and that's sort of stuff.
So C and D, this data format. Is somewhat after the raw data, so it's not your raw data.
It's minimally preprocessed. In a way that your stimulus features and your EEG are aligned and you're with the same sampling rate.
And so you can run TRFs and CCA or you know, Into subject correlation.
More rapidly and so you can you have access in this case you've seen a space scripts But also we connected.
This year we connected this format with the the work from and Christian brought back and Jonathan Simon and Nima's lab these toolboxes that they design so you can load CNSP, sorry, CND.
Data files from their toolboxes now and you can easily transition between bits and C and D. They are they're connected and we included all the functions for doing so
But in reality, this is not something complicated. It's just basically a guidance in terms of what is.
The folder structure you should use. Where if you do put the code, where should you put the tutorials?
Sorry, the code, for example. Where should you put the data sets? How should you call the file name?
So if you follow these instructions then basically it's it's just much easier to run the code we share.
And we share this on our GitHub. See if you just go on our GitHub and the links are all on the on the website.
Show them in a second. You have this folder structure with the example scripts. We have more tutorials throughout here.
The libraries are not ours. Some of these are ours, but others are just dependencies that we use.
And then the data sets are empty. You just need to go to the website, download the data set and put it into folder structure, and then you can run the script.
We are setting up a new documentation page. It's a working progress, but you can find you can already find some instructions on again how to save your data in this format and so on.
So on the website you have this. On the resource page, you have all of these links, the GitHub, the documentation.
And these other things I show you in a second.
The data browser. So we one thing that we built in the last year is a graphical user.
So just an interface like this. Which is built in Mathub in this case. And wait for the next slide because that's much cooler.
So the idea apart from the ugly looking interface which is matter of style This gives you an idea of the parameters that you need to decide when you when you fit these models.
And it gives you an easy access to, to do so. So basically what you can do is And I'll see if I can.
Take it up here. Yeah, so it on top. So this does the the GUI in my my computer and you can open here and you have access to whatever data sets you have on your computer and they're all in the same format so you can switch and
A loads and then it's going to give you the stimulus feature for that particular data set.
And you can say, oh, I want to fit the model on this number of participants on this amount of data per participant, for our backer models.
And just for sake of time, I don't have time to run it now, but basically. You can run the user TRFs, get prediction correlations, get the RF weights.
But you can also compare different data sets. You can take your speech data set, your music data set, you can plug them on top of each other.
It's still work to do, but it's a it's a starting point to make things a bit a bit faster a bit easier when we want to look at data that we have.
You know available without having to talk with the person that that actually share that data every time and this is a proof of concept.
We, it's a work in progress. It is a web-based data browser. So while the other one is a standalone application built in Matlab, you can just, you don't need to have MATLAB, you just install it.
But you still need to install it. This thing is that is in your browser. So you just go on your browser, you type this link, which I'll share on the chat in a second.
And you can fit. Forward to your apps and so on. So encoding decoding models.
Why do I think this is useful? And again, lots of this stuff is done selfishly because because we use it.
So it happened to me, for example, to talk with scientists that wanted to run a new experiment and they were asking me Can I run this speech listening experiment or this music listening experiment?
In with 5 min of data. Because my particular population can't deal with experiments that are longer than 5 min.
And usually my answer is, I don't know. I need to open my Matlab and run some reanalize some of my code.
So with interface, basically you can go to some loads some existing data and select how much data you want to.
You want to use and see basically, for yourself, if that is a suitable amount of data for your experiment.
And much more.
So again, for sake of time, I'll. I'll cut you short. We describe lots of these new components in the paper that we just submitted to archive.
So the C and D data structure, the analysis pipeline resources. We also include the simulation toolkit.
Where you can simulate EEG data in different ways. I encouraged using that because simulating data can be really, really useful.
Especially when your hypothesis are not that Clear, it's not clear what you would get from a TRF analysis.
So maybe before actually running an experiment, it can be quick and useful to to use these these functions.
Okay. So just to conclude the call for project proposal, so, and bring train, and which is one of the most interesting producers of portable EG systems.
They have been sporting us with this scholarship last year and also this year. It's 1,500 Europe.
And so what we encourage you to do is to propose a project. And to this proposal in 500 words.
You can decide to say, okay, I want to visit Giovanni's lab in Dublin or I want to visit Aaron's love in Rochester or Sekosia or Nottingham Trent or a Commonwealth computer can visit any of these institutions in the list.
And that are related the organizers or you can propose some other visit to someone else as long as it makes sense.
With CNSP. So as long as it has something to do with, you know, the resources that we share or something related to that.
Or anyway something that is has an open science mindset. And so whatever data you deal with, we would encourage you to share it.
The call for proposal is on the website. You can find it here in the workshops, page.
Okay, that's all from me. So Right on time. I'm a bit late.