[Eeglablist] Poor ICA decompostion

[Arnaud Delorme]

Dear Kelly,

You should remove 128-#chans interpolated-1 (because you used average reference after iinterpolating) when running AMICA.
How do the other ICA algorithms fare? Can you simply remove the channels — instead of interpolating them — and tell us about your components as well.

Best wishes,

Arno

> On Oct 16, 2018, at 7:07 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 (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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