[Eeglablist] High pass filtering and ICA
Makoto Miyakoshi
mmiyakoshi at ucsd.edu
Thu Dec 5 08:09:38 PST 2024
Hi Gilbert,
I think EEGLAB has a GUI support for copying ICA-related matrices.
>From 'Edit' -> 'Dataset info' (the top menu item) in which the bottom 3
edit boxes are for copying ICA weight matrices from other dataset loaded to
the EEGLAB.
Makoto
On Thu, Nov 21, 2024 at 3:54 PM Gilbert, David G via eeglablist <
eeglablist at sccn.ucsd.edu> wrote:
> For the application of 0.5Hz high-pass to the 0.1Hz file in ERP analyses,
> can the files be epoched when the file 2 ICAs are applied to the file 1
> (0.1 Hz HP)? Is there a video showing how this is done? I keep getting an
> error using epoched data.
> Thanks in advance,
> David
>
> ________________________________
> From: Gilbert, David G
> Sent: Friday, September 27, 2024 3:43 PM
> To: Gilbert, David G <dgilbert at siu.edu>; Gunn, Matthew <mgunn at uic.edu>
> Subject: High pass filtering and ICA
>
> eeglablist<eeglablist-bounces at sccn.ucsd.edu> on behalf of Makoto
> Miyakoshi via eeglablist<eeglablist at sccn.ucsd.edu>
> To:
>
> eeglablist at sccn.ucsd.edu
> Fri 9/27/2024 3:28 PM
> [EXTERNAL EMAIL ALERT]: Verify sender before opening links or attachments.
>
> Hi Cedric,
>
> > But I haven't found any perfect solution personally.
>
> The perfect solution, if you want, is to process the EEG data in two
> different ways.
>
> 1. High-pass filter at 1-2 Hz + ICA; This preprocessing is optimum for
> ICA + time-frequency decomposition above the cutoff frequency of the HPF,
> but not for time-domain averaged ERP because some people say
> this 'distorts' the waveforms.
> 2. High-pass filter at < 0.5 Hz + ICA; This one is optimum for time-domain
> averaged ERP but not so for ICA + time-frequency analysis.
>
> Who said we can't do it, so why not?
> I think using the above approach is more straightforward than worrying
> about whether to use the HPF trick for ICA.
>
> An important confirmation is to compare the ICA results between 1 and 2
> (before that, repeat ICA several times on the same data to see how
> reproducible the results are!) If they are practically the same, there is
> no need to wonder about the choice.
>
> Makoto
>
>
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