[Eeglablist] bad channels removal before ICA

[Darren Weber]

It is acceptable for avg ERP to replace bad channels with the _weighted_
average from surrounding electrodes (weighted by distance, either linear or
arc length distance, so it's best if you have electrode coordinates for this
calculation).  A better method may be to interpolate the timeseries at the
bad channel from a surface spline of all good channels.  I recall reading
this in a statement paper on methods (can't recall the reference just now).
Obvioulsy it is better to have good measured data, but we don't always live
in that world ;-)  I'm not sure what the implications of this might be for
ICA, my comments are just based on reading of acceptable methods in avg
ERPs.

Best, Darren


----- Original Message ----- 
From: "Jia Wu" <jia.wu at louisville.edu>
To: <eeglablist at sccn.ucsd.edu>
Sent: Thursday, October 23, 2003 8:55 AM
Subject: [Eeglablist] bad channels removal before ICA


> Hi Everyone,
> I have a very basic question towards the processing
> ICA-artcifact-removal. We use a 128 channels systom to get ERP.
> Sometimes we have bad channels because of the high impedences in them.
> What should I do in the two cases below?
>
> 1, I do ICA on averaged data. The averaging program can detact and
> remove bad channels. I have two questions here. 1.1, after averaging,
> the bad channels are replaces by 0 if they are bad most of the time.
> What should I put in ICA? I don't think I should put 0 in it. So should
> I use the averaged data from adjacent channels? 1.2, The averaging
> program seems to be able to detact and remove eye blinks data also. Then
> what the meaning of doing an ICA on the data?
>
> 2. I do ICA on single trials. Should I just remove the bad channels by
> visual examination? If so, the same question as above, after i remove
> the bad channels, what data in the bad channels should I use in ICA,
> averages of adjacent channels?
>
> Finally, I actually don't know whether I need to do anything on the bad
> channels before ICA. I guessed ICA could separate the bad channels out
> by different components. But it is not proved by my work. When I put
> data including some bad channels in ICA, I tried to get 128 components.
> At least the first 10 components (having the largest variance) only
> represent the bad channels. I don't think it's right to remove the 10
> largest components in ICA, is it?
>
> By the way, should ICA be applied on averaged data or single trial? If
> on single trial, I still need to average it in the later ANOVA. So
> what's the difference?
>
> I'm eagerly waiting for anybody's hints.
>
> Thanks,
> Jia
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