<p>In short: we know that eye-blinks are reflected frontally in the EEG data and we use that knowledge to identify which components reflect for example eye-blinks. It would not make sense to identify a component related to eye-blinks on the back of the head - there would be something wrong with your data. </p>

<p>What ICA does is (data driven) estimate a number of statistically independent components (if you have 64 channels you get 64 components) based on your data. The input data is usually in the form of channels X timepoints X trials. </p>

<p>Eventually the output of ICA gives you, for each component, a weight for each channel which you can use to create the component scalp map. Together, the component scalp maps, the time course of the components, and frequency information of the component provide supplementary information that you can use to identify artifacts such as (but not limited to) eye-blinks, horizontal eye movements and heart beat.</p>

<p>In the above figure component 14 and 15 do not seem to be eye artifacts. (1) they are very focal, which is probably related to noise specific to that channel. (2) They look to be completely uncorrelated with the eye blinks in the original signal. </p>

CC BY-SA 3.0
2014-12-29T04:13:47.297Z
You are right about what you say on component 14 and 15. but Could you give me more information (i.e literature review) about "the weight for each channel" and it's relation with " location on scalp map", since i try to search on this on the web, but I have not found any result.
Learner
CC BY-SA 3.0
2014-12-29T14:33:23.540Z
When you record EEG data, you know where you have placed each electrode - for example according to the 10-10 system. So if you have as output a weight for each channel, then you also know the location on the scalp. I don't think you would find a literature review specifically for EEG data. ICA is a general technique and can be used on all kinds of multidimensional data. However, a good start on ICA would be to read [http://sccn.ucsd.edu/~arno/indexica.html](http://sccn.ucsd.edu/~arno/indexica.html)  which outlines what ICA actually does.
blackBerry
CC BY-SA 3.0
2014-12-27T17:22:09.670Z
2014-12-27T17:22:09.670Z
blackBerry
<p>I have read the following <a href="https://papers.cnl.salk.edu/PDFs/Extended%20ICA%20Removes%20Artifacts%20from%20Electroencephalographic%20Recordings%201998-3137.pdf" rel="noreferrer">paper</a> , which is try to remove EOG artifacts from EEG signal , the authors give the following figure .<br>
<img src="https://i.sstatic.net/VaDAc.png" alt="ICA of EOG "></p>

<p>The authors claim that components 1 and 2  represent electro-oculographic (EOG) artifacts. This is because its scalp map refers to locations very near to eye. My first question is <strong>how we map an ICA component to a certain location on scalp map</strong>. Secondly, how do we know <strong>a component lies on the front of the head</strong>, and not on another location, for example the center? </p>

<eeg>
Removing ocular artifacts from EEG signals
Learner
# Removing ocular artifacts from EEG signals

(Asked by: username_0 on 2014-12-27)

I have read the following [paper](https://papers.cnl.salk.edu/PDFs/Extended%20ICA%20Removes%20Artifacts%20from%20Electroencephalographic%20Recordings%201998-3137.pdf) , which is try to remove EOG artifacts from EEG signal , the authors give the following figure .  
![ICA of EOG ](https://i.sstatic.net/VaDAc.png)

The authors claim that components 1 and 2 represent electro-oculographic (EOG) artifacts. This is because its scalp map refers to locations very near to eye. My first question is **how we map an ICA component to a certain location on scalp map**. Secondly, how do we know **a component lies on the front of the head**, and not on another location, for example the center?

**Tags:** eeg


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## 1 answers

### Answer by username_1 on 2014-12-27. Score: 12 | Accepted answer

In short: we know that eye-blinks are reflected frontally in the EEG data and we use that knowledge to identify which components reflect for example eye-blinks. It would not make sense to identify a component related to eye-blinks on the back of the head - there would be something wrong with your data.

What ICA does is (data driven) estimate a number of statistically independent components (if you have 64 channels you get 64 components) based on your data. The input data is usually in the form of channels X timepoints X trials.

Eventually the output of ICA gives you, for each component, a weight for each channel which you can use to create the component scalp map. Together, the component scalp maps, the time course of the components, and frequency information of the component provide supplementary information that you can use to identify artifacts such as (but not limited to) eye-blinks, horizontal eye movements and heart beat.

In the above figure component 14 and 15 do not seem to be eye artifacts. (1) they are very focal, which is probably related to noise specific to that channel. (2) They look to be completely uncorrelated with the eye blinks in the original signal.

> **Comments:**
> **username_0**: You are right about what you say on component 14 and 15. but Could you give me more information (i.e literature review) about "the weight for each channel" and it's relation with " location on scalp map", since i try to search on this on the web, but I have not found any result.  
> **username_1**: When you record EEG data, you know where you have placed each electrode - for example according to the 10-10 system. So if you have as output a weight for each channel, then you also know the location on the scalp. I don't think you would find a literature review specifically for EEG data. ICA is a general technique and can be used on all kinds of multidimensional data. However, a good start on ICA would be to read [http://sccn.ucsd.edu/~arno/indexica.html](http://sccn.ucsd.edu/~arno/indexica.html)  which outlines what ICA actually does.  

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