[Eeglablist] cleaned signal

Mon Jan 11 10:42:44 PST 2016

>
>
> Hello Alessandra, 
> To correctly understand how to remove components,
> it's best to
> look at and consider several things that I've listed below, especially  if
> you haven't had a chance to yet. There are only a few major classes of
> well-established artifacts with clear patterns, so they are no hard to
> find. All the best.
>
> 1
> . First make sure you are running your ICA correctly on correctly cleaned
> and prepared data, data that is also of adequate length for ICA's
> requirements, etc..
> In short, is it a good ICA decomposition?
>


> 2. If this your first time with eeglab, please make sure you have opened
> and worked with the eeglab tutorial data first so you understand how to run
> all major steps. It's better to cut your teeth with that data first.
>
> 3
> . See the Onton and Makeig chapter in Luck's Handbook of Event-related
> COmponents. A copy of this and other important papers are available at
> Dr.Makeig's site, and also by searching on Google Scholar.
>
> 4
> . Review several recent articles using ICA and removing artifactual ICs
>
> 5
> . Review past eeglab list messages (recent ones exist) from the last year
>
> 6
> . Check out the Artifact/ICA rejection tutorials in the eeglab online
> tutorial. Try using the tutorial data first.
>
> 7
> . Use MARA, ADJUST, SASICA, or IC-MARC (from Frolich) - which are all
> tools for telling you which ICs are artifactual.These are all plugins for
> matlab. You will also benefit from reviewing the articles associates with
> each.
>
> 8
> . Note that that "mixed" ICs that contain neural and artifact data, should
> probably not be removed.
>
> 9
> . Remember that some people j
> us
> t remove a few ICs related to eye or muscle artifacts, and some people
> remove a lot more ICs, and some just analyze the good ICs and ignore the
> rest.
>  Just emulate high-quality papers if top journals, if you're not sure
> about best steps.
>
> 10
> . See also the "Clean Continuous data with ASR" plugin for eeglab, which
> can clean data quite well using a different decomposition technique to
> rebuild bad parts of the data.
>
> 11
> . If you have an ERP design, you will likely see the benefits of removing
> ICs when you plot ERPs.
>
> 1
> 2.
> Looking at the data in terms of topomaps across a condition will also give
> you an idea of how well cleaned your data is.
>
> 1
> 3
> . Last examine your normal eegplot with all channels, and your spectopo
> plots, to determine what the data looks like before and after
> testing various kinds of 
> IC removal.
>


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> On Mon, Jan 11, 2016 at 8:53 AM, Alessandra Di Pietro <
> dipietroalessandra at hotmail.it> wrote:
>
>> Hi everyone,
>>
>> I'm working for the first time on EEGLAB, I performed ICA with 35
>> components and I obtained the first butterfly plot (
>> http://it.tinypic.com/r/2eehahc/9), after that I reject the artifacts
>> components by ICA (http://it.tinypic.com/r/15ebrqv/9) but we are not
>> sure if we deleted all the wrong components. I removed this components and
>> plotted the butterfly cleaned ( http://it.tinypic.com/r/34opy1e/9).
>> Could you help me to understand if I cleaned correctly the signal?
>>
>> Thank you so much
>>
>> Alessandra
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>> dirty Pictures, dirty Images, dirty Photos, dirty Videos - Image -
>> TinyPic - Servizio di hosting d'immagini, condivisione immagini & hosting
>> di video gratuito <http://it.tinypic.com/r/2eehahc/9>
>> it.tinypic.com
>> Tinypic™ is a photo and video sharing service that allows you to easily
>> upload, link and share your images and videos on MySpace®, eBay®, blogs and
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>>
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