[Eeglablist] ICA analysis

[Makoto Miyakoshi]

Dear Sonia,

> There should be no need to reject bad epoch, since we will be removing
bad ICA components right? This should give us artifacts free epochs

Rejecting/correcting bad chunk of time-series data is one thing, and
rejecting artifact subspace is another. In theory, performing the former
and run ICA will increase quality of decomposition (especially for normal
informax; AMICA has its own datapoint rejection function). This is because
such window rejection increases data stationarity (i.e. probability
distribution function of one part of data is similar to that of other part
of data). So you should still clean the time-series data before ICA.

Very good point!

Makoto


On Fri, Jun 30, 2017 at 11:42 PM, Dr. Sonia Baloni <sbaloni at cbcs.ac.in>
wrote:

> Dear Makoto,
>  Thanks a lot for the information, although that creates one more doubt
> for me:
>
> Yes. As long as ICA detects data rank correctly, you don't need to do
> anything. However, sometimes it fails for unknown reason, so it's safer to
> explicitly use the data rank if you know it (how many channels you
> interpolated etc.) But I don't do the second ICA these days though, it
> usually makes little to no difference if your first ICA is good enough. If
> you want to drastically change the ICA results, you should reject 30-50% of
> data!
>
>
> If first ICA is good enough and second ICA is not required then:
>  There should be no need to reject bad epoch, since we will be removing
> bad ICA components right? This should give us artifacts free epochs
>
> Best
> Sonia
>
>
>
> On 01-Jul-2017, at 06:50, Makoto Miyakoshi <mmiyakoshi at ucsd.edu> wrote:
>
> Dear Sonia,
>
> Sorry for belated response. To add some info to Isaiah's nice reply,
>
> > 1. So if we are performing two ICAs, we should interpolate these
> channels after second ICA?
>
> Yes. As long as ICA detects data rank correctly, you don't need to do
> anything. However, sometimes it fails for unknown reason, so it's safer to
> explicitly use the data rank if you know it (how many channels you
> interpolated etc.) But I don't do the second ICA these days though, it
> usually makes little to no difference if your first ICA is good enough. If
> you want to drastically change the ICA results, you should reject 30-50% of
> data!
>
> > 2. I assume rank of the data (used in ICA analysis) would be equivalent
> to the number of channels in data after running clean line function.
>
> If some of your channels were bridged, you have less rank than the number
> of channels.
>
> > If there are for example 128 channels with which data was collected and
> 10 channels were removed with clean line function then the rank of the data
> would be 118. If we now want to run ICA on this data do we need to add “
> ‘pca’,117 “ in command line option - next to ‘extended’, 1 ? as suggested
> in  the tutotrial : https://sccn.ucsd.edu/wiki/
> Chapter_09:_Decomposing_Data_Using_ICA.
>
> If 128-10, then 'pca', 118 is correct. If 117, one component is wasted
> (which is trivial in this case).
>
> Makoto
>
>
>
> On Tue, Apr 4, 2017 at 7:00 AM, Isaiah Innis <isainnis at indiana.edu> wrote:
>
>>  Dr. Baloni:
>>
>>
>> 1. Yes, you would interpolate after the second ICA. I personally
>> interpolate as the last pre-processing step. Also I assume you are
>> referring to "clean raw data" and not cleanline, as the latter only removes
>> line noise.
>>
>> 2. Yes, your rank would be 128 -10 = 118. If the channels were deleted
>> from the dataset entirely ICA should detect this and run fine without
>> additional options. If not then you should use the 'pca' command. Are you
>> average referencing your data before ICA? In that case your rank would be
>> 117 (at least as I understand it - someone on the list please correct me if
>> this is inaccurate), otherwise it would be the number of channels remaining
>> = 118.
>>
>> Remember that if you remove any components in the 1st ICA the 2nd should
>> be run on the original number of components minus the number removed (e.g.
>> 117-25 = 92).
>>
>>
>> Thank you,
>>
>> On Thu, Mar 30, 2017 at 6:42 AM, Dr. Sonia Baloni <sbaloni at cbcs.ac.in> wr
>> ote:
>>
>>> Hi All,
>>>
>>>     I am new to EEGlab and trying to work with ICA analysis. I have been
>>> reading the eeglab list mails on ICA topic. I have gather few footnotes
>>>  and few question which I would like to ask:
>>>
>>>
>>> 1. Cleanline algorithm removes bad channels from the data. Posts in
>>> EEGlablist suggests that interpolation should be done after ICA.The
>>> tutorial suggests that two ICAs should be performed on the same data-set.
>>> So if we are performing two ICAs, we should interpolate these channels
>>> after second ICA?
>>>
>>> 2. I assume rank of the data (used in ICA analysis) would be equivalent
>>> to the number of channels in data after running clean line function. If
>>> there are for example 128 channels with which data was collected and 10
>>> channels were removed with clean line function then the rank of the data
>>> would be 118. If we now want to run ICA on this data do we need to add “
>>> ‘pca’,117 “ in command line option - next to ‘extended’, 1 ? as suggested
>>> in  the tutotrial : https://sccn.ucsd.edu/wiki/C
>>> hapter_09:_Decomposing_Data_Using_ICA.
>>>
>>>
>>> Thanks a lot.
>>>
>>> Best
>>> Sonia
>>>
>>>
>>>
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>>
>>
>> --
>> Isaiah Innis
>> Indiana University '13
>> EEG Technician, IUB IRF
>>
>>
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>
>
>
> --
> Makoto Miyakoshi
> Swartz Center for Computational Neuroscience
> Institute for Neural Computation, University of California San Diego
>
>
>


-- 
Makoto Miyakoshi
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego
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