[Eeglablist] Poor ICA decompostion

[Makoto Miyakoshi]

Dear Kelly,

I like Marius's advice. Try 1.5Hz high-pass. Also, your data look fine with
me.

See the pictures shown here. If you screw up in copying weight matrices,
you'll NEVER know it by looking at scalp maps. You MUST check EEG.icaact
which is ICA activation on the scroll data.
https://sccn.ucsd.edu/wiki/Makoto's_preprocessing_pipeline#Tips_for_copying_ICA_results_to_other_datasets_.2806.2F26.2F2018_updated.29

> 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.

I have been very worried that Fiorenzo's message was widely misunderstood.
He did not say using PCA is unconditionally bad. Instead, his message was
that do not use PCA to save time. PCA is a lossy compression, so you do
lose information. Also, usually up to 20 principal components explains >95%
of data, but most of the variance here is artifacts (eye blinks etc).
Although 'keeping 95% variance' is a widely-used engineering standard
practice, it does not hold for EEG.

Using PCA is TOTALLY VALID if you know you need to reduce data dimension.
There is no evidence, for example, that channel rejection is better than
PAC dimension reduction.

Makoto



On Tue, Oct 16, 2018 at 8:18 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
>>
>>
>> _______________________________________________
> Eeglablist page: http://sccn.ucsd.edu/eeglab/eeglabmail.html
> To unsubscribe, send an empty email to
> eeglablist-unsubscribe at sccn.ucsd.edu
> For digest mode, send an email with the subject "set digest mime" to
> eeglablist-request at sccn.ucsd.edu



-- 
Makoto Miyakoshi
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://sccn.ucsd.edu/pipermail/eeglablist/attachments/20181025/3aa05eca/attachment.html>