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An Interactive EEG tutorial that _taught_ EEG and BCI basics.

EEG 101 is dead, may it rest in peace.

Due to Interaxon rescinding support for its Muse SDK, it is no longer possible for open-source developers to build mobile apps that connect to Muse devices, including this one. Thus, we have stopped development here and removed the app from the Google Play Store.

If you are interested in learning or teaching EEG, we recommend you check out the EEGEdu project, which we see as the spiritual successor to EEG 101, expanding on the EEG 101 experience by adding features that we could only have dreamed of when we started this project back in 2016.

Good luck and happy neurohacking,


  • Teaches the basics of EEG, including where signals come from, how devices work, and how to process data
  • Contains a general purpose binary classifier for EEG data
  • Streams data from the Muse with the LibMuse Java API
  • Built with React Native for Android
  • Completely free, open-source, and available for use/adaption in any project

Video Walkthrough

Lesson Content

  • Neurophysiology of EEG
  • EEG hardware
  • Filtering
  • Epoching
  • Artefact Removal
  • The Fourier Transform
  • The Power Spectral Density Curve
  • Brain waves
  • Brain-Computer Interfaces
  • Machine Learning

How it works


Our goal with EEG 101 was to create a flexible base for EEG and BCI mobile development that novice programmers can build on top of for multiple platforms with different EEG devices. To satisfy those concerns, we've built the app in React Native, which allows for fast, straight-forward front-end development and the potential to port to iOS, Web, or Desktop (Electron) in the future.

Currently, EEG 101 is split right down the middle between Java and React. If you're interested in how we connect to the Muse, process EEG data, and plot the results in real time, check out the graph and signal classes in the android source folders. Our implementations are all (for the most part) typical Android components written in Java.

If you'd like to use EEG 101 as a base for your own app in React Native, take a look at how we've written the tutorial in the src folder. Connecting to a Muse and plotting real-time EEG data is as simple as using one of the Native components we have already prepared.

Setup (instructions may be out of date)

  1. Install and setup React Native. Note: EEG 101 uses lots of native code, so create-react-native-app and Expo are not an option. Follow the instructions for "Building Apps with Native Code." You may also need to install the JDK, Node, Watchman, and the Gradle Daemon as well
  2. Install yarn
  3. Clone this repo git clone
  4. Download the LibMuse SDK from Muse's developer website. We've already taken care of integrating the sdk library into the app, so just make sure you end place in <clonedRepoName>/EEG101/android/app/src/main/jniLibs/armeabi-v7a/ and libmuse_android.jar in <clonedRepoName/EEG101/android/app/libs/
  5. run yarn install in the EEG101 folder
  6. Connect an Android device with USB debug mode enabled. Because the LibMuse library depends on an ARM architecture, EEG 101 will not build in an emulator
  7. Run react-native start to start React packager
  8. In new terminal, run adb reverse tcp:8081 tcp:8081 to ensure debug server is connected to your device and then react-native run-android to install EEG 101

Common setup problems

  1. Gradle build error: Attribute "title" has already been defined
  1. INSTALL_FAILED_UPDATE_INCOMPATIBLE: Package com.eeg_project signatures do not match the previously installed version; ignoring!
  • Solution: Uninstall any pre-existing versions of the app on your device
  1. Could not connect to development server
  • Solution: Make sure that the device is connected, run adb reverse tcp:8081 tcp:8081, and restart the React packager (react-native-start)
  1. Could not get BatchedBridge
  • Solution: Run adb reverse tcp:8081 tcp:8081 again and reload
  1. Error retrieving parent for item: No resource found that matches the given name 'android:TextAppearance.Material.Widget.Button.Borderless.Colored'