Backyard Brains Logo

Neuroscience for Everyone!

+1 (855) GET-SPIKES (855-438-7745)

items ()

It’s the Fin-al Countdown: Last Steps in Fish Behavior Analysis

fish behavior analysis

— Written by Sofia Eisenbeiser —

Well, folks, we made it.  The last week, the final frontier, time to sink or swim.  Luckily, I’ve spent the past 10 weeks with the most expert swimmers of all, our BYB fish.  And, boy, have they taught me well.  So, let’s dive in!

Alright, where were we?  In the beginning we discussed play and how science might attempt to concretely define it in lower-level vertebrates whose minds we don’t fully understand (yet…).  That was followed up with the discovery that fish like to play laser tag, and that some of them even prefer to play with differently colored lasers! 

The first few weeks of my project revolved around more conceptual ideas and thinking about how exactly to quantify qualitative traits.  How am I supposed to take something as abstract as the day-to-day of a fish and turn that into cold, hard data? 

Well… the answer is actually quite simple: ethograms!


Pick a Card Project Update: Deploying Hardware for TinyML Model

Pick a Card Project Update

— Written by Nour Chahine —

Before I start discussing my updates, I have an announcement to make: I solemnly swear that I will solely be working with SSVEPs for the remainder of my project!

Over the past few weeks, I worked on feeding different forms of data into the neural network. I mainly applied two approaches: the power spectral density vector method and the notch filter knockout vector method. Both methods rely on quantifying the EEG response relative to the flashing frequencies of the cards.

Power Spectral Density Vector

The power spectral density vector method involves computing the power spectral density of the EEG signal, and then making a vector of the total power of the signal at each of the target frequencies.


Don’t Blink, You Might Miss It! Can Machine Learning Be Our Eyes and Ears?

Can Machine Learning Be Our Eyes - hardware for FOMO glasses

— Written by Ariyana Miri —

Welcome back FOMO gang!

If you’ve been following along with my FOMO glasses journey, then you know I’m trying to build a pair of glasses that capture a photo every time you blink. In my first post, we discussed the idea behind my project, and the implications the success of the project can have. In the second, we discussed the nature of an eye blink signal, and how the data is being processed. Today, we’re talking about hardware and how I’m actually putting the thing together, so check out the other posts if you haven’t had the chance yet!

Can Machine Learning Be Our Eyes - layered blinks

With my project, while the signal of an eye blink is stronger and easier to detect than others, the hardware aspect presents an entirely new challenge. How are we going to get everything to communicate together and fit on a pair of glasses?