[Eeglablist] Poor ICA decompostion

[Kelly Michaelis]

Hi everyone,

I'm wondering if anyone can help shed some light on why I'm getting such
poor ICA decomposition and what to do about it. I've tried a number of
pipelines and methods, and each one is about this bad (The link below has
pictures of the scalp maps from two files below). I'm using a 128 channel
EGI system. Here is my pipeline:

1. Import, low pass filter at 40Hz, resample to 250Hz, high pass filter at
1Hz
2. Remove bad channels and interpolate, then re-reference to average ref
3. Epoch to 1s epochs, remove bad epochs using joint probability
4. run AMICA using PCA keep to reduce components to 128-#chans interpolated
5. Load raw data, filter same as above, resample, remove bad chans,
interpolate, re-reference
6. Apply ICA weights to continuous, pre-processed data
7. Do component rejection

What am I missing? Does anyone see any glaring errors here? My data are a
bit on the noisy side, and while I do capture things like blinks and
cardiac artifacts pretty clearly, I get the artifacts loading on a lot of
components, and I'm not getting many clear brain components. I got one
suggestion to reduce the number of components down to something like 64,
but this article by Fiorenzo, Delorme, Makeig recommends against that.

Any ideas?

Thanks,
Kelly

Scalp maps:
https://georgetown.box.com/s/1dv1n5fhv1uqgn1qc59lmssnh1387sud

On Thu, Oct 11, 2018 at 11:10 AM Kelly Michaelis <kcmichaelis at gmail.com>
wrote:

> Hi everyone,
>
> I'm wondering if anyone can help shed some light on why I'm getting such
> poor ICA decomposition and what to do about it. I've tried a number of
> pipelines and methods, and each one is about this bad (I've attached
> pictures of the scalp maps from two files below). I'm using a 128 channel
> EGI system. Here is my pipeline:
>
> 1. Import, low pass filter at 40Hz, resample to 250Hz, high pass filter at
> 1Hz
> 2. Remove bad channels and interpolate, then re-reference to average ref
> 3. Epoch to 1s epochs, remove bad epochs using joint probability
> 4. run AMICA using PCA keep to reduce components to 128-#chans interpolated
> 5. Load raw data, filter same as above, resample, remove bad chans,
> interpolate, re-reference
> 6. Apply ICA weights to continuous, pre-processed data
> 7. Do component rejection
>
> What am I missing? Does anyone see any glaring errors here? My data are a
> bit on the noisy side, and while I do capture things like blinks and
> cardiac artifacts pretty clearly, I get the artifacts loading on a lot of
> components, and I'm not getting many clear brain components. I got one
> suggestion to reduce the number of components down to something like 64,
> but this article by Fiorenzo, Delorme, Makeig recommends against that.
>
> Any ideas?
>
> Thanks,
> Kelly
>
>
>
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