[Eeglablist] Poor ICA decompostion

[Marius Klug]

Dear Kelly,

other than the mentioned -1 for avRef your pipeline is fine as far as I
know. You could try 1.5Hz highpass filtering just to see if it produces
better results, but be careful with too much filtering. I normally also
only filter with 100Hz lowpass.

The decomposition is also highly dependent on the data quality of course.
Could you provide representative screenshots of the channel data that you
will feed into AMICA? Also how long are your datasets? The rule of thumb is
to have at least EEG.nchans^2*30 samples, so in your case that means 491520
samples, which is 33 minutes.

Last but not least: In case you are using mobile subjects with significant
artifacts, this decomposition isn't actually so bad. We have subjects like
that as well (Gramann MoBI Lab), if the data quality was particularly bad
to begin with.

Best of luck with your research!
Marius

Am Di., 16. Okt. 2018 um 17:39 Uhr schrieb Arnaud Delorme <arno at ucsd.edu>:

> 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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-- 
Marius Klug
Research Associate / PhD Student at TU Berlin, Germany
Department of Biological Psychology and Neuroergonomics
+49 (0)30 314-79 514
bemobil.bpn.tu-berlin.de
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