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Simple and powerful
Timeflux gets out of your way so you can focus on your research. No complicated tool to learn. Describe your execution graph using a simple YAML syntax to acquire, monitor, and record data. Add a few processing nodes and build your BCI or biofeedback application.
Under the hood, Timeflux relies on industry standards such as SciPy, Pandas, Xarray and Scikit-learn.
Interface with most EEGs, biosignal equipment and stimulus presentation software thanks to the built-in Lab Streaming Layer node. Future-proof your recordings with the HDF5 storage format. Use our Pub/Sub implementation for easy inter-process communication and shared event streams. Go wild, and try the OSC protocol for your next artistic performance.
Timeflux is developer-friendly. Plugins are really just Python packages. We do the heavy-lifting, and you simply extend a base class to implement your favorite signal processing technique. You can also assemble multiple existing nodes into a new meta-node.
Timeflux was built with biosignals in mind, but it can handle many kinds of time series. IoT, geoscience, control engineering, you name it. Or go ahead and become the next algo-trading mogul.
We're here to help!
Right now, the documentation is a bit coarse, and some parts of the code need polishing. We're working on it. Meanwhile, if you need help, let us know! We also have a Slack workspace, join us!
The introduction paper has been published in the Proceedings of the 2019 Graz BCI Conference.