[Eeglablist] Artifact removal validation

[Ilana Hairston]

Just my two cents on this topic.
I routinely attempt to remove eye movements with ICA. Like others  
have mentioned it largely depends on the quality of the signal.
Things that affect the result are - the location of the eye electrode  
(duh), and whether other electrode (e.g., ground) are also picking up  
muscle movement from the eyes, which is pure individual differences  
between subjects.
Unlike the ECG, eye movements are variable. I find that I scroll  
through the data for a while to find components that consistently  
reflect the eye movements.
You also need to remember that the eye electrodes are picking up EEG  
(i see this more strongly since i record sleeping people), so  
completely removing the activity associated with the eyes will remove  
other things as well. So when i find that the eye movements are not  
clearly isolated using the ICA i opt to living with the artefact, and  
just visually removing extremely noisy trials.

ilana





On Jun 20, 2007, at 1:52 AM, Stefan Debener wrote:

> Hi Borna,
>
> you mention that ICA gives a different set of components when applied
> repeatedly to the same data. In my experience, this indicates that,  
> a),
> you don't have enough data points relative to the number of channels,
> and/or b), the data are not well recorded or pre-processed. With a
> reasonable recording & pre-processing, an average data quality, and a
> reasonable length of your data, you will find virtually identical and
> thus very robust ICs (most weights of dipolar ICs correlate >.95). If
> you do not get this, something is wrong.
>
> So, I would recommend fixing this first: Try a high pass filter,  
> remove
> noisy, drifting or clipping channels (or better avoid recording  
> those!),
> make sure you don't have electrolyte bridges in your data, remove
> non-stereotyped artifact periods (gross movements, cable movement,
> swallowing, etc.), and, importantly, ensure that the cap does not  
> change
> its position on the head while recording the data (ICA assumes spatial
> stationarity!!). If this is all ok, then run extended infomax ICA. You
> will see a number of dipolar ICs (between about 5 and 25) which are  
> very
> robust.
>
> To identify those ICs reflecting artefacts, it is good to have some a
> priori knowledge about EEG artifacts. ICA easily pulls out eye blinks
> and eye movements, which can be fully automatically removed by
> correlating the IC weights with a template (e.g., the map of the first
> eye blink in your data will correlate best with the IC reflecting eye
> blinks. Try looking up maps of artifacts on a single trial level,  
> that's
> very informative...). An example about using ICA to fully  
> automatically
> remove artifacts is given in Debener et al. (2007). Neuroimage, 34,
> 587-597. However, Arnos Neuroimage paper suggests much more flexible,
> and fully implemented approaches in this regard. Another artifact that
> is very commmon is the ECG artifact. Depending on your subject and  
> your
> electrode layout, you will get one (sometimes 2) IC showing a nice  
> ECG R
> peak, thus reflecting the electrical heart activity volume  
> conducted to
> the EEG sensors (the second, if it shows up, usually reflects the
> rotational part of the R peak vector: the heart wrings out to pump the
> blood, it does not pump by compression). With some experience, this
> artifact is so easy to spot that I cannot see that there is "much
> subjective" in IC identification (compared to any other artifact
> processing method, such as thresholding at +- 50 uV, or any other
> arbitrarily chosen value like the number of principal components that
> account for something).
>
> Validation 1: If you want to compare the performance of ICA to any  
> other
> artifact correction algorithm, then you should look at the single- 
> trial
> performance, not the averaged data. One could for instance define eye
> blink peak latencies, and then compare the uncorrected eye blink maps
> with the corrected maps at the same latencies. I did this for a few
> datasets, and ICA in my tests easily outperformed regression  
> approaches.
> That is, you can expect to find a higher correlation following
> regression than following ICA eye blink removal. Of course, this  
> will be
> obtained only if your ICA solution is robust, thus depend to some  
> extend
> on your preprocessing. I'd love to see a paper published using this
> approach ...
>
> Validation 2: Try to obtain results based on ICA preprocessing with  
> any
> other signal processing routine, for instance with regard to some well
> known neurocognitive effect. I virtually always find that, if the ICA
> decomposition is meaningful and robust across subjects,  ICA performs
> better than channel based data processing. ICA obtains nice links
> between behavior and EEG single trial amplitudes (e.g. Debener et al.,
> 2005, J Neurosci), ICA components show systematic trial-by-trial
> correlations with the fMRI BOLD signal (some papers about to be
> published soon, e.g. Scheeringa et al., Int J Psychophysiol), and AEPs
> source localization is easy even on a single subject level when  
> based on
> ICA preprocessing (e.g. Hine & Debener, 2007, Clin Neurophysiol).
>
> Validation 3: However, I fully agree that many open issues with regard
> to ICA validation persist. More likely than not, the whole approach  
> can
> be further improved and be made "less subjective" than it is right  
> now....
>
> Hope this helps,
> Stefan
>
>
> Borna Noureddin wrote:
>> Hello,
>>
>> I have been working through the most recent tutorial on artifact
>> rejection, and have a question about validation.  How can I  
>> validate or
>> evaluate the performance of ICA for artifact removal?
>>
>> The tutorial gives some steps for identifying artifact components,  
>> but
>> they are quite subjective.  This is further compounded by the fact  
>> that,
>> given the same set of data, running ICA gives a different set of
>> components each time.  So, even with an acceptable validation method,
>> it's unclear how I can objectively and reliably apply that method.
>>
>> Is there, for example, a "post-artifact-removal" version of the  
>> tutorial
>> dataset that can be compared with the original tutorial dataset  
>> provided
>> with EEGLAB?  And if so, is there documentation about how  
>> precisely the
>> artifacts were removed?
>>
>> Thanks,
>> Borna Noureddin
>>
>>
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>
>
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