> > I actually have a big MEG datafile on my hard drive, which I haven't gotten > around to playing with. > > It consists of about 120 time series, each with about 100,000 points in it. > > It represents the magnetic field of someone's brain, measured through 120 > sensors on their skull, while they sit in a chair and perform an experiment > of clicking a button when they see a line appear on a screen. (Pretty > exciting, huh?) I got the data from my friend Barak Pearlmutter at UNM, who > has spent a few years working on signal processing tools (using "blind > source signal separation" methods) designed to clean up the raw data > (basically subtracting off for noise caused by repeated reflection of > magnetic fields off the inside of the skull).
It's actually a very complicated data analysis. You basically have a spherical surface of data (the sensors), and you are trying to reconstruct the sources and sinks in 3d that created the 2d data you are observing. The problem is underconstrained, because many 3d data sets could produce the same 2d data set, but you try to build in some anatomical assumptions (ie: we know the hippocampus is probably a powerful source/sink, so pin that thumbtack there) to constrain the possible results. As you can imagine it's very weak spatially, but far more precise temporally than PET or FMRI, which can only measure blood flow changes occuring 1 second or more after the source activity. I think combined MEG/FMRI(or was it PET/FMRI) is going to be able to get the best of both worlds. Either way, there are plenty of technological obstacles. > > I guess that MEG can be used, over time, for stuff subtler than clicking > buttons when lines appear. But using it to track the dynamics of thoughts > seems a long way off.... Basically, one needs a lot more than 120 sensors!! > ... and then one needs to hope the signal processing code scales well (it > probably can be made to do so) > Well you can use far more complex behavioral tasks than that even with existing MEG technology(have people navigate a maze, do math, word problems, etc). But in order to get a footing with the new MEG technology, they need to start at the basics so that they can map MEG responses with known EEG signatures available from work that's already been done. The first decade of any new neurophys technique is characterized by a whirlwind of very basic, boring results (usually that create pretty pictures generating funding). Only after the tech has matured do you even begin to hit the cool stuff. I'll bet AI's will be required to analyze the data sets will be getting in the next 20 years. -brad ------- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
