BRAND Tutorial

This repository contains a brief tutorial on how to get started using BRAND. The tutorial consists of the following steps:

1. Set up your system for BRAND by following the instructions in 00_setup.md
2. Run a calibration task and train a decoder using 01_calibration.ipynb
3. Run a closed-loop cursor control task with your trained decoder using 02_control.ipynb
4. If you have multiple machines on whicn you can run BRAND, try out the multi-machine version of this task with 03_multi_machine.ipynb

Once you have finished the above steps, you will have a working example of how to use BRAND to run an experiment.

Next, you may want to:

• Learn how graphs are configured (see docs in brand/README.md and examples in notebooks/graphs)
• Write finite-state machine and graphics nodes to fit the task of your choice (see radialFSM.py and display_centerOut.py)
• Try out a new decoder architecture (see wiener_filter.py)
• Acquire data from a neural recording device like a Blackrock Neurotech Neural Signal Processor (NSP) (see cerebusAdapter.c)

If you run into any issues, please document and submit them here on GitHub.