[Eeglablist] ICA in clinical populations

[Scott Makeig]

Anuradha,

You mention 'distorted topographies' for the schizophrenic group ICs --
this is likely a sign that their data are more noisy and need further
noisy-data removal followed by a second decomposition to get good results.
The EEGLAB sub-menu 'Tools > Reject Data Using ICA' can be of use for this.
These tools were written to require epoched data -- In cases where the data
are continuous, we use >> eeg_regepoch()   to create regular (1-s or 0.5-s)
epochs. After obtaining an (improved) ICA unmixing matrix (weights*sphere),
this can be applied to other datasets (e.g., task-related, etc.) drawn from
the same data.

Scott Makeig

On Fri, Nov 11, 2011 at 11:10 AM, Sharma, Anuradha <
Anuradha.Sharma at med.uni-heidelberg.de> wrote:

> Dear All,
>
> I have applied ICA to auditory oddball data in a group of healthy controls
> and schizophrenia patients to try and isolate the P3b (posterior) and P3f
> (frontal) components. The problem is that although I get two very clean
> components in most subjects in the control group, in the patients due to
> distorted topographies (which leads to different dipole localizations),
> time courses etc., it has been very hard uptil now to find the
> corresponding/comparable components in the patient group. Has anybody had a
> similar problem when using clinical populations, are there any
> reccommendable clustering strategies to find corresponding components in
> such populations?
>
> Would be thankful for any tips...
>
> Best,
> Anuradha
>
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-- 
Scott Makeig, Research Scientist and Director, Swartz Center for
Computational Neuroscience, Institute for Neural Computation; Prof. of
Neurosciences (Adj.), University of California San Diego, La Jolla CA
92093-0559, http://sccn.ucsd.edu/~scott
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