[EEG] data and bowel movements

Kelly hurtstotouchfire at gmail.com
Thu Jun 25 13:18:13 PDT 2009

Well, that was exciting.  I think I may actually have a
linguistics-free weekend, and some time to hack on this.

Wiggly lines indeed.  Did you determine what the y axis was on ERP
data?  I still haven't had time to look that up.  I'm guessing
voltage?  What I was wondering is if there is V/ms for each electrode?
 And how is that localized information dealt with in terms of
interpreting the data?


Your timesharing proposal makes more sense now, and I'm definitely for
it.  Sounds like A Project though.  This may be one of the ideas worth
poking the broader NB community about.


I'm hoping Leah will take an interest in this since she was the only
one around with a definite project in mind that requires hi-res data.
When we first talked about this it sounded like the solution was to
throw money at it.  Does anyone have a more creative solution?


This stuff I have only minor experience with (reasonable neuro
background and one Berkeley AI course) but hot damn it sounds like
fun.  I can't really spearhead much of it, but I'd really like to
learn along the way.  Also, I am actually working in an EEG lab at
Berkeley, so if I can track down someone there who's sufficiently
nerdy enough, I will try to drag them over to noisebridge.  I have no
idea how academic EEG processing compares to what hackers and machine
learning people would come up with.


I am exceedingly down.  At some point, I also need to sit down and
fill in the gaps in my knowledge of the mathematics involved.  I don't
know all of the right buzzwords yet, but I think that I should be able
to tackle most of it if I can figure out how to google it...


I have a lot of resources in this area, I'm just not sure what people
feel is useful to learn about.  Possible topics:

- Action potentials and neuronic function at an ionic level, neural
networks, potentiation, etc.  This is all molecular and cell bio
stuff.  Super interesting, marginally relevant to your overall
conception of brain function.

I think that for hacking EEG, having a basic understanding of the
source of EEG signals is important if you want them to hold any
meaning whatsoever.  I recommend knowing what a dipole is, knowing
roughly why that's relevant to EEG and comprehending this summary:

- Gross anatomy of the brain, with an emphasis on the bits that will
cause electrical activity on the scalp.  This is useful if you have a
project in mind to implement with EEG and want to figure out its

- EEG based Brain Computer Interfaces.  I'm working on some
interesting BCI research at Berkeley.  I'm not expert in this stuff
but I could certainly give an overview of things people are doing and
various limitations.  I might also be able to drag someone in from
Berkeley, but this is an area where there are already plenty of
non-academic people doing amazing work, so we could look that
direction as well.

- Neuroscience review on any number of specific aspects of cognition.
Memory, learning, emotion, music, motor memory, epilepsy,
hallucination, sleep, meditation... Basically lit review stuff for
anyone with a specific cognitive focus.

Ok, that's enough brain dump for now.  I'll be around 83c this
weekend, but have Linguistics to do until then.  Leah's coming by
noisebridge on Sunday I think.  She's interested in machine learning
EEG stuff as well.


On Tue, Jun 23, 2009 at 3:52 AM, Christopher Abad<aempirei at gmail.com> wrote:
> also i been using my brain to think about the overall EEG happenings and
> came up with some words  .i got some crap up in here and heres some info on
> whats the haps with the eeg progress. ill go over what i been doin..
> for all the homeys who dont like matlib, dont wanna pay for matlab or dont
> wanna steal matlab, i been trollin da nets for some free data in not matlab
> format. first of all, heres some free datasets for john q public.
> http://www.vis.caltech.edu/~rodri/data.htm
> you will notice there are mostly matlab ones, but there are also ones called
> ASC. these are mostly plain text single or multi column time series float
> values. these are useful. download them. also the descriptions are on the
> page, read them.
> to see whats up in this business, i graphed these datasets. not very useful
> but gives you an idea what the hell is eeg data? the answer is this: wiggly
> lines.
> these are seizures:
> http://www.twentygoto10.com/eeg/120130/120.gif
> http://www.twentygoto10.com/eeg/120130/130.gif
> these are ERP trials:
> http://www.twentygoto10.com/eeg/cg-analysis/
> the graphs with two colors are frequency data, red is pre-task, green is
> post-task, i did not normalize for 1/f noise
> the other is the time series data for the entire approximately 2 seconds of
> data. beyond that i cant discern shit from shinola here in these.
> you will notice this stuff is in general disarray. yes, this my standard
> modus operandi. get the shit done, wipe after.
> coming into this i did not realize how slow EEG data is. IT IS VERY SLOW.
> we're talking sub 100HZ, and very often, sub 20HZ. hook your brain up to my
> car stereo and you will hear the subs rattlin the trunk.  this is not
> friendly and unusual for many reasons, esp for those with normal signal
> processing experience with sound and video (which is WAY FAST). up until
> today i had thought that the soundcard in was a viable input, but realize
> now that this is not because theres probably bandpass filters gettign rid of
> noise below like 10-20hz. additionally, it just flat out takes a lot more
> time to get a decent number of sample points since we're sampling at
> ridiculously low speeds (of course we can sample at normal 'audio' speeds,
> but since we're lookin at data below like 20hz, we effectively are only
> getting like 20 samples per second and we're just averaging wide windows of
> sample data (if were at like 44.1khz oor whatev), this means we need lots of
> data if we are gonna use statistical correction and noise cancellation
> methods which ill mention in a sec).
> from what ive gleaned, the electrodes and amps are effectively little
> antennas picking up low frequency small signal fluctionations in EMF, this
> means we need some expensive ass amps (relatively) for the hat, i noticed
> we're using burr-brown instrumentational amps that do like 10000x gain. very
> fancy.  ive also looked at specs online for the general brain amps, ill go
> over some hardware recomendations in a sec which can address various issues
> we have.
> it seems like the main badguy we have is noise. there are two primary ways
> to reduce noise in so far as what ive come across. hardware methods,
> software methods. the primary hardware methods that dont involve chopping
> peoples heads open are clean power and moving the amps closer to the signal
> (the brain). from what ive looked at, it seems that they use unit-gain op
> amps to do this, just to make the wire happy essentially, so these amps are
> that expensive compared to the high gains at the output-stage. also, the
> designs of these are all pretty straightforward so i can offer up various
> napkin schematics for this. also, if we're not using battery power now on
> the eeg kits, i recommend just switching to battery power to get rid of the
> complexities and issues of power supplies and transformers.
> i was talkin to my homeboy about the noise problems from the software side,
> he pointed me into the direction of control theory and it seems like theres
> much progress in using kalman filters to reduce noise in image and sound
> signals, and with much success. let me point you to this project :
> http://rsbweb.nih.gov/ij/plugins/kalman.html
> i think directly applying this method to reduction of noise is one of the
> quickest and easiest ways to clean up the miserably noisy data from the
> openeeg kits. theres only so much thaty ou can do with bandpass filters and
> smoothing functionds, and recursive prediction and statistical modeling of
> the noise is the best way to really handle this. furthermore this particular
> homeboy is relatively adept at machine learning and control systems, and
> therefore i will convince him to help with machine learning methods when we
> make it to the ERP phase of our eeg project. right now i think theres been a
> lot of work done in ai research and control theory fields that basically
> adress all our needs for ERP, specifically, using cepstral analysis ideas,
> kalman filters, neural networks and markov-based grammar models have been
> very successful in the commercial and .mil space.
> since the signals are way lower than i expected in frequency, we can
> actually try to timeshare the primary high gain amps across a number of
> electrodes to cover more surface area. we're probably sampling at 100-1000
> times faster than the wave frequencies we're trying to catch, so we can
> easily timeshare the amps by cycling through a series of electrodes. this
> takes minimal design. ive gone over the design of the openeeg and that atmel
> thats on it is basically doing nothing compared to what it could be doing. i
> think they just chose it cause the ease of use. designing our own amp isnt
> really a stretch of imagination or a waste of time when/if we decide to use
> the funny cap or want to go to active electrodes.
> heres some crappy code i used. none of the tools are that useful but if
> youre not that experienced with signals or graphing of crap in the unix
> commandline, heres barebones stuff that youcan get a sense of how to do it.
> http://www.twentygoto10.com/eeg/shitlab.c - loads up MATLAB datafiles from
> linux in C and maybe you can convert datasets, it uses libmatio (which sucks
> and no example code, so heres an example code)
> http://www.twentygoto10.com/eeg/cg-analysis/quickfft.c - convert time series
> data to frequency series data using the crappy DCT-II method of real to real
> data
> http://www.twentygoto10.com/eeg/120130/gnuplot.plot - heres the gnuplot
> script i used to graph the crap (also theres a dct.plot with slightly
> different parameters)
> if anyone is interested in learning the related useful informations in
> signal processing and amplifier design relevant to the EEG project goings on
> and more, then i'l be willing to put together soemthing for one of the up
> coming eeg meetings.
> anyone got one to share?
> EEG devices are two steps away from wearing tinfoil hats.
> if you wanna chat about this or about shoes and clothes, hit me on the IRC
> - aempirei
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