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Quantify your EEG, Use your EMG – A Neuromath Journey (Part III)

Quantify your EEG, Use your EMG - Natalia Díaz working on her Python code
My Code is so sublime that I need two computers!

— Written by Natalia Díaz —

Hello. I’m here again! And this will be my last update on my neuromathematical project. If you don’t remember me, I’m Natalia Díaz, and I’m doing my university internship at Backyard Brains.

(If you’re wondering what I’m talking about when I say neuromathematics, check out my first and second blog post.)

In the last part of my internship, I have been working with the Python platform. It is great to do the experiments but we must also learn to interpret them. And as every numbers geek knows, there is no better way of doing that than math and statistics!

Until now, Backyard Brains have been using the Matlab platform for EEG analysis, but they’ve always wanted to achieve the same (or at least similar) result using Python. So, they asked me to try to “translate” the work they had already done, showing increased alpha wave power in the visual cortex when the eyes are closed.

At first I was a little scared, since I know the Python language, but I am not an expert! But as I worked on it, I realized that it was not difficult and that I knew more than I thought I knew. So I managed to make a code on this new platform that did the same as what they had. I must say that Matlab is easier for (a bit more complicated) math operations, but with a little effort and searching you can get a good result in Python.

Below you can see the spectrogram of the EEG of the visual cortex as we opened and closed our eyes. Look at that alpha wave power. It worked!

quantify your eeg - spectrogram of EEG of the visual cortex during opening and closing of our eyes

Realizing that I had done a good job (I think haha), BYB’s co-founder Tim asked me for a little more statistical analysis by creating new graphs and calculations. And I did it! By analyzing all the data of alpha power during eyes closed versus eyes open, and using boxplots, I can now show statistically that alpha power is higher in the visual cortex EEG when the eyes are closed. I think a p-value of 0.003 is convincing, don’t you?

alpha wave power in visual cortex EEG

Finally in summary, I am very happy to finish my internship having been able to help Backyard Brains with my knowledge and above all by having combined what I like the most: mathematics and neuroscience. My protocol is already on the website under our experiments page – “Quantifying Your EEG.” I hope they are happy with my work and consider me in future projects where they need neuromathematical help.

See you soon!

Say Hello to Another BYB Intern & Budding Neural Engineer

— Written by Miguel Cornejo —

BYB co-founder Tim (left) and Miguel (right) – Bookstores are also good places to get your electronics working!

Hi everyone! I am Miguel Cornejo, a high school senior at Colegio Alberto Blest Gana in San Ramón, Santiago, Chile. Backyard Brains has had a relationship with my school for 5 years, and I took their Neural Engineering Course two years ago, leading a team on studying Leg Muscle Recordings (EMGs) during soccer kicks.

I recently worked with Backyard Brains during a short 1 month long internship to modernize two of their Muscle SpikerShield Experiments – Controlling a Stepper Motor and Controlling an LCD screen with your muscles. Why did they need to be modernized? Because the new controller chips have now become so inexpensive my new protocol is a breeze. Tim and I worked together at various cafes in Nuñoa and downtown Santiago, and after only one burned out chip, my project finished quickly! As a result of this internship, I now have a small neural interfaces workshop in my house and stay in touch with BYB. In my spare time, when not learning next-gen engineering, I enjoy building gaming PCs and (of course) soccer.


How to Get Reeled into NeuroDuino – a Mathematician’s Guide (Part II)

Neuroduino
A NeuroDuino, Backyard Brains’ latest prototype

— Written by Natalia Díaz —

Hi there, it’s Natalia Díaz again with an update to my neuromathematical (yes, such a thing exists!) project. If you don’t remember me, I’m a student of Mathematical Engineering at the University of Santiago de Chile and I’m doing my internship here in BackyardBrains.

Since the last time we met (you and I, that is), BYB co-founder Tim Marzullo sent me some cool stuff. Not that it’s an exclusive privilege of interns, mind you! Anyone can find them in the “Muscle SpikerShield Bundle” kit.

With this bundle, you can do several very entertaining experiments such as seeing on your smartphone the action potentials that are produced when you move your muscles. You can also use the Muscle SpikerShield to control video games, robotics, and musical instruments.

It took a while for my board to pass customs, but it managed to arrive and we got to work right away. What I was most excited about was the arrival of new prototype from Backyard Brains – their very own customized Arduino board – codenamed NeuroDuino. (See above how handsome it is!)

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How to Get Reeled in By Neuroscience – a Mathematician’s Guide (Part I)

how to get reeled in by neuroscience

— Written by Natalia Díaz —

What lies at the intersection of math and medicine? Why many things, of course. Certainly more than could possibly fit into a blog post! But today, I am going to talk about the connection between brain function and numbers.

My name is Natalia Díaz and I am a student of Mathematical Engineering at the University of Santiago de Chile. Ever since I can remember, I have been tantalized by mathematics and medicine (especially brain function). The opportunity to mix both subjects finally arose when I entered college. That is how Neuroscience popped into my life!

To get my degree, I must complete my internship and my thesis. That’s how I started working with my mentors Dr. Patricio Rojas (University of Santiago) and Dr. Patricio Orio (University of Valparaíso). We are investigating, through numerical simulations, the effect of the electrical synapse topology between inhibitory neurons.

For this, we use a neural mathematical model of a mixed network of inhibitory and excitatory neurons of the cerebral cortex, and we study different types of topology (“all with all” or lattice style) of connection between inhibitory neurons characterizing the patterns obtained.

For example, the figure below shows a significant difference in network synchronization using different topologies. In the first yellowy-whitish graph, there is no gap junction (electrical synapse). The second shows a gap junction with a lattice topology, and in the last one we apply a gap junction with an all-to-all topology. To plot this, we use different values for the mean synaptic strength between excitatory neurons (mGsynE) and for the mean synaptic strength between inhibitory neurons (mGsynI). Lots of abbreviations, I know. But I promise they are fun!

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