Brain-Computer Interface Programs (BCI)
A non-exhaustive list
For BCI designs, there are multiple toolboxes and design software that we can use. Each has their own pros and cons.
Pros
A few NCIL members already have some familiarity with it
Compatible with Lab Streaming Layer (LSL) and OpenBCI
Many ready-to-use paradigms included
Commonly used in research
Cons
Uses Lua programming language, not Python
Poor documentation—not detailed or updated often
Only compatible with Windows or Linux
Pros
Uses Python
Compatible with PsychoPy, LSL, OpenBCI, and BrainProducts that we use in the lab
Compatible with Mac, Windows, and Linux
Has built-in nodes for LDA, SVM, and many other classifiers, and you’re able to code your own
Has a web API so you’re not limited to a desktop/laptop to present your stimulus presentation
Increasingly prevalent in the literature
Documentation seems to be very detailed
Free for academic research or for events like hackathons
Cons
Requires a computer with four cores
Most newer laptops have over four cores, plus in-lab computers do. Older laptops often have less than four, so it might limit who can use NeuroPype at hackathons
Subscription for commercial use
Pros
Uses nodes written in YAML to acquire, monitor, and record data
Uses nodes written in Python to process data and implement commands in real-time
Uses packages we already use—pandas, scipy, scikit-learn, and others
Compatible with Psychopy, LSL, OpenBCI devices
Completely free and open-source
Cons
Relatively new—documentation is still in development
Not currently widely used
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