Anuradha,<div><br></div><div>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.</div>
<div><br></div><div>Scott Makeig</div><div><br><div class="gmail_quote">On Fri, Nov 11, 2011 at 11:10 AM, Sharma, Anuradha <span dir="ltr"><<a href="mailto:Anuradha.Sharma@med.uni-heidelberg.de">Anuradha.Sharma@med.uni-heidelberg.de</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex;">Dear All,<br>
<br>
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?<br>
<br>
Would be thankful for any tips...<br>
<br>
Best,<br>
Anuradha<br>
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</blockquote></div><br><br clear="all"><div><br></div>-- <br>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, <a href="http://sccn.ucsd.edu/%7Escott" target="_blank">http://sccn.ucsd.edu/~scott</a><br>
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