[Eeglablist] P300 signal detection (Research Assistant)

[Romain Grandchamp]

Hi Habib,
do you mean there is no statistically significant differences in P300
mplitudes between your conditions or are you stuck somewhere before
reaching this stage?
Directions given at
http://neurofeedback.visaduma.info/emotivresearch.htmshould work. You
have to be sure you gather enough EEG data trials though,
as the recording device you use is quite sensitive to noise and
interferences.  100 trials should be a minimum in each conditions (attended
VS non-attended) in a first step to check that your P300 paradigm works.

Regards
Romain

---------- Message transféré ----------
> From: Research Assistant <ra_lums at hotmail.co.uk>
> To: <eeglablist at sccn.ucsd.edu>
> Cc:
> Date: Wed, 10 Oct 2012 11:44:17 +0500
> Subject: [Eeglablist] P300 signal detection
>  Hi all,
>
> I want to extract P300 signals. I currently have with me EMOTIV EPOC
> headset with its research SDK. I am new in this domain. I have been
> following tutorials of EEGLAB and trying to see the signals which I saved
> in the Testbench of my SDK.  I used the OpenViBE'a keyboard simulator
> program provided at this link:
> http://neurofeedback.visaduma.info/emotivresearch.htm
>
> But I am unable to find anything significant.
> Can someone help me please and guide me how to proceed.
>
> Regards,
>
> Habib Paracha
>
>
> ---------- Message transféré ----------
> From: Massimiliano Papera <massimiliano.papera at googlemail.com>
> To: eeglablist at sccn.ucsd.edu
> Cc:
> Date: Wed, 10 Oct 2012 14:12:07 +0100
> Subject: [Eeglablist] ICA problema Massimiliano Papera
> Dear all,
>
> I am just about to run the ICA and I just incurred in this statement in
> the tutorial:
>
> 'Note: If you try to run ICA on this new dataset, the number of dimensions
> of the data will have been reduced by the number of components subtracted.
> We suggest again 'baseline-zeroing' the data (if it is epoched when some
> components have been removed, data epoch-baseline means may change). To run
> ICA on the reduced dataset, use the *pca* option under the Tools > Run ICApop-up window, type
> * 'pca', '10' * in the Commandline options box to reduce the data
> dimensions to the number of remaining components (here 10), before running
> ICA (see runica.m<http://sccn.ucsd.edu/eeglab/locatefile.php?file=runica.m>.
> If the amount of data has not changed, ICA will typically return the same
> (remaining) independent components -- which were, after all, already found
> to be maximally independent for these data.'
>
> this is from
> http://sccn.ucsd.edu/wiki/Chapter_09:_Decomposing_Data_Using_ICA
>
> Let's us suppose that I have just removed, say, 4 ICS comps. Then how do I
> check if the amount of data has changed?
>
> Thank you very much for your help.
>
>
> Cheers
> Massimiliano Papera
>
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