BrainKit 1.0 released

Nathan Whitmore continues to push the envelope on DIY brain stimulation. Somewhat above my limited capabilities, we can assume that as the project evolves, the build will get simpler.

BrainKit 1.0 released
To download BrainKit, click here to go to its GitHub page. You can also see an earlier post which lays out some of the concepts behind BrainKit here.

Planning BrainKit started about a year ago when I was thinking about the question “now that putting together the hardware and software to make a relatively inexpensive device to stimulate the brain is basically a solved problem, what is the next major obstacle to the use of noninvasive brain stimulation?” The answer was (and still is) figuring out where in the brain to stimulate to achieve some desired effect.

BrainKit was inspired by this idea, a brain stimulator which also is capable of monitoring brain activity and using statistics to understand the neural correlates of mental states and design stimulation montages more intelligently. For instance, BrainKit can find brain regions that show different patterns of activity in fatigued and alert states—and then allow you to stimulate these regions to see if it affects alertness.

Full article:

Signal to Noise — BrainKit:A Combination Brain Mapping and…

You’re more likely to know Nathan Whitmore as /u/ohsnapitsnathan, one of the moderators at the tDCS subReddit. Around making plans to attend NYC Neuromodulation Conference 2015, he’s started a GoFundMe campaign and announced an early Beta of his tDCS device, BrainKit. It’s a very ambitious project! He plans to include sensors that would  monitor brain activity using capacitance (Electrical Capacitance Volume Tomography)! The BrainKit would then generate optimal montages for specific desired effects! It’s Arduino based, and Nathan intends for it to be Open Source. It’s very early in the BrainKit’s development, but it appears to me that all the pieces are in place.

A feature new to BrainKit is the ability to act on this information by designing a montage. BrainKit’s montage designing algorithm is actually quite simple, and based on the principles that:
1. If increased performance on a psychometric measure is associated with higher excitability in a cortical area, BrainKit will deliver anodal stimulation to that area, if decreased excitability is associated with increased performance then BrainKit will use cathodal stimulation.
2. If functional connectivity between two electrodes is positively associated with good performance, anodal stimulation is delivered to both electrodes, if it is negatively associated with good performance then cathodal stimulation is applied to both electrodes.
3. If any electrode conflicts exist the previous two rules cause one electrodes to be marked for both anodal and cathodal stimulation, that electrode is excluded from the montage.