[Eeglablist] ICA Problem

[Zhilin Zhang]

Hi, Habib,

In general,  for a typical EEG dataset, source number is very large.
You reduced the dimension of your dataset from 14 to 5, which is the
reason that you encountered this problem. Actually, you do not need to
reduce the dimension, since the dimension of your dataset is only 14.

To automatically test the best components and see the stable of ICA
decomposition, you can try A.Hyvarinen's ICASSO software, which is
based on his FastICA algorithm. See the link:
http://research.ics.aalto.fi/ica/icasso/

Also, you can try EEGLAB.


Best,
Zhilin



On Thu, Dec 6, 2012 at 11:04 PM, Habib Paracha <ra_lums at hotmail.co.uk> wrote:
> Hi,
>
> I am performing fast ICA on a 14 Channel EEG data. Reducing the dimension of
> data from 14 to 5 components. When I perform the ICA I get a different
> result every time I run it. I am using the FASTICA toolbox available for
> matlab.
>
> The Question is how will I be able to figure out which is the most suitable
> component of all the extracted ICs. I am trying to extract the P300 response
> from the data. I get the P300 data component randomly in the 1st, 2nd, 3rd
> or 4th Component. I have to visually figure out which is the best extracted
> IC and then rest of the code.
> Sometimes I even get the P300 signal but it is inverted(instead of the
> positive peak ICA outputs a negative peak data).
>
> Someone kindy tell me a technique to automatically detect the best component
> and run the rest of the code using this component.
> Also a way to cater the wrong results of ICA.
>
>
> Regards,
>
> Habib Paracha
>
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