Working with EEG (electroencephalography) data is hard, and this little library aims to make it easier. EEGrunt consists of a collection of functions for reading EEG data from CSV files, converting and filtering it in various ways, and finally generating pretty and informative visualizations.
Update: We’ve added functions to plot heart rate and heart rate variability from recorded OpenBCI ECG (electrocardiography) data. You can test these out with the analyze_ecg_channel.py and analyze_ecg_data.py demo scripts. We’ve posted a new tutorial on our blog to get you started: EEGrunt update: Analyze heart rate and HRV with Python
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EEGrunt is compatible with data from OpenBCI and Muse.
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EEGrunt has bandpass, notch, and highpass filters for cleaning up powerline interference, OpenBCI's DC offset, and zeroing in on the frequency band you want to analyze.
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EEGrunt makes it easy to generate signal plots, amplitude trend graphs, spectrograms, and FFT (fast-fouier transform) graphs, etc.
- Download or clone the repo:
git clone https://github.com/curiositry/EEGrunt - Run
sudo bash install_linux_dependencies.sh(tell me if this doesn’t work) - Take a look in
analyze_data.pyand edit at will, or create your own script usingEEGrunt.py. Make sure to set the required variables — device, path, and filename. - Run it:
python analyze_data.py - Read the announcement post for the official tutorial!
- [Optional] Interested in analyzing ECG data with EEGrunt? Take a look at the new tutorial.