This undergraduate-level workshop series will introduce you to all the topics you'll need to get started on your own neurotech projects! There are no prerequisites—we'll be building neurotech skills from the ground up.
A combination of:
- lectures
- coding notebooks
- product design
Each week, students will work in pairs to go through a notebook, filling in the code and running it. Mentors will be available to help the pairs debug or understand the overall concepts better.
NOTE: This course is extensive! We're teaching a lot of material, and some can be quite advanced... But we are here to guide you throughout the entire process, so if you feel lost at any point, don't worry! Just come to Hacknight and we'll learn it together :)
NOTE 2: We're trying to introduce you to a lot of crazy things in a short timeframe! This means we have to rely on readings to prep you for each week's workshop. Please make sure to do the mandatory readings and any mandatory prep noted before you come to the workshop, as it will make understanding the material infinitely easier :)
There is no official textbook for this course; however, we will be providing readings and resources throughout that will function as our own quasi-textboook!
(To sync this version of workshops with your version, follow the instructions in this link!)
Week 1: Intro to Neuroscience
Basic neuroanatomy, synapses and neuronal signalling, and the electroencephalogram (EEG)
Week 2: Intro to Python
Absolute basics of programming, practice problem solving
Week 3: Graphing & Filtering EEG Data
- How to load EEG data from CSVs (or FIFs) and graph it with MatPlotLib
- Filtering noise and an introduction to the Fast Fourier Transform
Convolution, Fourier transform, impulse responses, signal types, continuous vs. discrete, aliasing, Nyquist's Theorem, FIR vs IIR, different types of filters, filter order
What exactly is EEG, physics of EEG, oscillatory processes vs ERPs, power spectral analysis for EEG power bands
Front-end programming, the "interface" part of the brain-computer interface
Signal acquisition from the Muse using MuseJs