Awesome
EEGEdu
<p align="center"> <a href="https://github.com/kylemath/EEGEdu/blob/master/LICENSE" target="_blank"> <img src="https://img.shields.io/badge/License-MIT-brightgreen.svg" alt="MIT license" /> </a> <img src="https://img.shields.io/github/v/release/kylemath/EEGEdu?include_prereleases" /> <a href="https://github.com/kylemath/EEGEdu/blob/master/CONTRIBUTING.md" target="_blank"> <img src="https://img.shields.io/badge/PRs-welcome-brightgreen.svg" alt="PRs welcome!" /> </a> <img src="https://img.shields.io/github/issues-raw/kylemath/EEGEdu" alt="issues" /> <img src="https://img.shields.io/github/issues-closed-raw/kylemath/EEGEdu" /> <a href="https://neurotechx.herokuapp.com/" target="_blank"> <img src="https://neurotechx.herokuapp.com/badge.svg"> </a> <a href="https://neurotechx.slack.com/messages/eegedu/" target="_blank"> <img src="https://img.shields.io/badge/NeurotechXChannel-%23eegedu-lightgrey" /> </a> </p> <p align="center"> <img src="logo.png" alt="Interactive Brain Playground Logo" width=500 /> </p>EEGEdu
is an Interactive Brain Playground.
EEGEdu
is served live at https://eegedu.com/. This website is served from the software in this repository. So, all you need to do to try the system out is head to EEGEdu.
EEGEdu
is designed as an interactive educational website to learn/teach about working with electroencephalogram (EEG) data. It is a teaching tool that allows for students to interact with their own brain waves.
EEGEdu
has been inspired by multiple works that came before. It is inspired by EEG101, but EEGEdu
is web-based. Being web-based allows students to interact with EEG brain data without having to install any software. Others have used Neurotech EEG-notebooks in python for data collection and analysis with muse-lsl. These software support the Interaxon MUSE headset but require a bluetooth low-energey (BLE) dongle to work with common operating systems (e.g. Windows or Mac OSX). These tools also required the editing pyglatt
code to connect to Muse headsets. Thus, previous software are cumbersome and serve as a barrier to entry for many students learning about EEG. EEGEdu
aims to provide students with an accesible introduction to working with their own brain waves.
EEGEdu Curriculum
EEGEdu provides an step-by-step incremental tutorial for students to interact with EEG-based brain signals. So, we break down the curriculum into 10 lessons as follows:
- Connect + hardware, Biophysics + signal viewing
- Heart rate time domain data
- Heart rate frequency domain -beats per minute
- Raw Data + artifacts + blinks + record
- Frequency domain explanation + Raw spectra + record
- Frequency bands + record
- Spectrogram
- Neurofeedback p5js demos
- Eyes closed eyes open experiment
- SSVEP experiment
- BCI trainer
Installation for Development
If you are interested in developing EEGEdu, here are some instructions to get the software running on your system. Note: Currently EEGEdu development requires a Mac OSX operating system.
To start, you will need to install Homebrew and yarn. These are easy to install with the following Terminal / bash
commands:
## Install homebrew
/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
## Install yarn
# NOTE: this will also install Node.js if it is not already installed.
brew install yarn
# NOTE: Node.js must be version 10.x for Muse interaction
# Thus, if you are getting version issues, install n, with the following command:
# sudo npm install -g n
# Then, you can switch to version 10.x with the following command:
# sudo n 10.16.0
Then, in Terminal/bash
, clone this Git repository and change directory into the newly cloned folder:
git clone https://github.com/kylemath/EEGEdu
cd EEGEdu
Then, you can install the required yarn
packages for EEGEdu:
yarn install
Local Development Environment
Then, you can run the Local Development Environment of EEGEdu:
yarn start dev
If it is working correctly, the EEGEdu application will automatically open in a browser window at http://localhost:3000.
Local Production Environment
To start the Local Production Environment, you can use the following commands:
yarn cache clean
yarn run build
serve -s build
Local Testing of Changes
- Install any new packages
yarn install
- Start the Local Development Environment
yarn start dev
- Look for errors in terminal log
- Open's browser to http://localhost:3000
- Open Javascript console
- Look for errors in console
- Connect Mock data stream by clicking Connect button
- Run through the
checkFunction
below with Mock data. - Disconnect Mock data stream
- Turn on Interaxon Muse headband
- Connect Muse data stream
- Repeat the
checkFunction
below with Muse data.
# Pseudocode for checking EEGEdu functionality
checkFunction = {
view raw data
change sliders
make sure data changes and no errors
click spectra
move sliders
make sure changes
click bands
move sliders
make sure changes
}
Deployment
EEGEdu is running on Firebase and deployment happens automagically using GitHub post-commit hooks, or Actions, as they are commonly called. You can see how the application is build and deployed by inspecting the workflow.
Currently this automagic deployment is not working, so we can deploy to firebase manually:
First, install the Firebase deployment tools:
sudo brew install firebase
sudo yarn global add firebase-tools
sudo yarn global add firebase
The first deployment requires login and initialization once:
firebase login
Browser opens, and login to Google account authorized for Firebase deployment:
firebase init
- options: Hosting Sites only
- public directory: build
- single-page app: No
- Overwrite - No
- Overwrite - No
Then, deployment to Firebase happens with the following commands:
# clean the local cache to ensure most recent version is served
yarn cache clean
# build the latest version of the site
yarn run build
# deploy the latest version to firebase
firebase deploy
References and Related Tools
- Muse 2016 EEG Headset JavaScript Library (using Web Bluetooth)
- Muse 2016 + Web Bluetooth demo app in Angular
- Explore Muse headband data in frequency domain
- Pipeable RxJS operators for working with EEG data in Node and the Browser
- React, A JavaScript library for building user interfaces
- Simple yet flexible JavaScript charting for designers & developers
- Muse 2016 EEG Headset LSL (NodeJS)
- Electroencephalogram (EEG) Recording Protocol for Cognitive and Affective Human Neuroscience Research
Contributing
The guide for contributors can be found here. It covers everything you need to know to start contributing to EEGEdu.
Credits
EEGEdu
- An Interactive Electrophysiology Tutorial with the Interaxon Muse brought to you by Mathewson Sons. A KyKorKey Production.