[Eeglablist] A question on semi-automatic detection on continuous data

[Tarik S Bel-Bahar]

Hello Eve,

Until you find the tools in question in BCIlab,

One way to do this is as follows, let the list know if you have success
with these simple steps.

1. Prepare your data (filtering, re-referencing, bad channel dropping,
etc...)
up to the point that you are ready to remove "artifactual time periods".

2. use eeg_regepochs to make a series of "fake" contiguous epochs,
choosing an epoch size of your choice (.5 or 1 second).

3. run the epoch rejection tool of choice within eeglab, which will lead
to the dropping of some subset of the fake epochs from step 2.
 [you may have to try several different methods,
and determine what works best for your data]

4. if you are happy with retained epochs
run ICA on this newly cleaned set of fake contiguous epochs.
Note that you need to feed ICA enough data, and preferably
only periods of time when the cognitive activity in question was occurring.
for example, don't include the minutes of data when nothing was going on,
or "in between" your experimental blocks.

5. Now, if you are happy with the ICA decomposition you get
apply your ICA decomposition to the continuous file
just before step 2 above.


6. At this point, you can make your "real" epochs based on
your true events, and again drop the artifactual epochs.
(for example, the epochs that have extreme values).
...and you should be able to move forward.

7. Note if you use AMICA from Jason Palmer,
it is possible for AMICA to discount artifactual periods via the
"do_reject" flag.


Note: almost any method to remove artifactual periods from continuous data
will necessitate making "windows" within which the artifact detection
method.
If you can learn to do it on your own, you could make fake epochs
via your own scripts, and then rejoin all the remaining epochs.






On Fri, Sep 21, 2012 at 3:43 PM, alotof eve <alotof_tiger at yahoo.com> wrote:

>
> Dear experts,
>
> I cannot find the function of semi-automatic detection of artifact for
> continuous data in BCIlab as Makoto suggested. Any other way to do
> semi-automatic detection of artifact for continuous data (before segment)
> off-line?
>
> We do ICA but we want to do some artifact detection before it.
>
> Many thanks,
> Duo
>
>   ----- Forwarded Message -----
> *From:* alotof eve <alotof_tiger at yahoo.com>
> *To:* "mmiyakoshi at ucsd.edu" <mmiyakoshi at ucsd.edu>
> *Cc:* "eeglablist at sccn.ucsd.edu" <eeglablist at sccn.ucsd.edu>
> *Sent:* Friday, September 21, 2012 3:39 PM
> *Subject:* Re: [Eeglablist] A question on semi-automatic detection on
> continuous data
>
> Dear experts,
>
> I cannot find the function of semi-automatic detection of artifact for
> continuous data in BCIlab as Makoto suggested. Any other way to do
> semi-automatic detection of artifact for continuous data (before segment)
> off-line?
>
> Many thanks,
> Duo
>
>   ------------------------------
> *From:* Makoto Miyakoshi <mmiyakoshi at ucsd.edu>
> *To:* alotof eve <alotof_tiger at yahoo.com>
> *Cc:* "eeglablist at sccn.ucsd.edu" <eeglablist at sccn.ucsd.edu>; Christian
> Kothe <christiankothe at gmail.com>
> *Sent:* Thursday, September 20, 2012 7:55 AM
> *Subject:* Re: [Eeglablist] A question on semi-automatic detection on
> continuous data
>
> Dear Duo,
>
> BCIlab supports it. If you are interested in, please refer to BCIlab wiki.
> http://sccn.ucsd.edu/wiki/BCILAB
> I also copy this to the developer of BCIlab, Christian Kothe, for comments.
>
> Makoto
>
> 2012/9/19 alotof eve <alotof_tiger at yahoo.com>:
> > Hello, experts,
> >
> > I have a question on semi-automatic detection of artifact. I noticed that
> > when only the data is segmented, we can use semi-automatic method on tool
> > panel. When the data is continuous, the automatic method is invalid. I am
> > doing data analysis and I am asked to find a way to do automatic
> rejection
> > on continuous data. I wonder if there is any reason that EEGLab dosen't
> > offer this function?  Any suggestion on doing semi-automatic detection on
> > continous data?
> >
> > Thanks,
> > Duo
> >
> > _______________________________________________
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>
>
> --
> Makoto Miyakoshi
> JSPS Postdoctral Fellow for Research Abroad
> Swartz Center for Computational Neuroscience
> Institute for Neural Computation, University of California San Diego
>
>
>
>
>
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