(This is a placeholder page for the OpenEEG project.)
First meetup: 2008-03-27 20h00 at 83c?
It sounds like our steps are the following:
- Assemble a set of OpenEEG boards
- Get together a set of electrodes
- Get the data into a computer
Once we've done that, let's look at heartbeats. They're big and easy to see. This is also a good first step towards bootstrapping the data analysis part.
After we've gotten good signal, we can move on to strapping the thing to people's heads and looking for brainwaves. This is where things get interesting. We can export the data and throw it over to the wacky Machine Learning meetup, we can tie it into crazy Cyborg group stuff, make, and we can use it as an input to other systems (be they security, music, or teledildonics).
Here's Jonathan's sketch of a plan, which sounds reasonable to me:
- Get a pool of OpenEEG cards. I would suggest starting with 8. Maybe several people want to "adopt" one by buying a kit and we can have a solder party. I will adopt one myself.
- Put them all in a metal box with a solid ground. Ideally, it has RF
- Find someone with a 8-channel USB-audio interface for signal acquisition. These are pretty common with the musician crowd. (Sample rate is overkill, but that's OK). Anybody have one we could borrow?
- Find some good electrodes. Sanity check each channel by detecting heartbeats. (If we can't get those loud and clear, no point looking at the brain's much weaker signals.)
- Now are we ready to do EEGs. I would suggest -- nay, insist -- on a double-blind protocol, otherwise we will see results that aren't there. It's simply human nature. So get subject(s), over several trials, to say, attempt achieving an alpha state, and for a control, I don't know, read BoingBoing or something. Get a third party to randomize the order and keep track of data labels. (Might experiment with evoked potentials too as long as we're all set up.)
- When you have some data, call in the geeks. I am personally expert at Fourier analysis, and have some experience with LDA (linear discriminant analysis) using SVD (singular value decomposition). I'd be happy to share what I know (including open-source tools like Octave and SciPy) and chew on the data for an evening.
- Bootstrap from there, as this may not work at all.