[Eeglablist] Sampling rate

[Makoto Miyakoshi]

> In your experience, does the remaining effect of gradient artefact appear
in a single component (after singular value decomposition approach and running
ICA ) similar to other types of artefacts like muscle and blink components?

Theoretically no, due to non-stationality. Practically, it depends, but
generally it does not work.

> If yes, what are the characteristics of the gradient component (in time,
topography and frequential domains)?

ERP looks like the subtracted GA template.

Makoto

On Sun, Nov 16, 2014 at 9:09 AM, mori larin <morilarin88 at gmail.com> wrote:

> Dear Makoto,
>
> In your experience, does the remaining effect of gradient artefact appear
> in a single component (after singular value decomposition approach and running
> ICA ) similar to other types of artefacts like muscle and blink components?
> If yes, what are the characteristics of the gradient component (in time,
> topography and frequential domains)?
>
> Best regards,
> Mori
>
>
> On 30 June 2014 18:48, Makoto Miyakoshi <mmiyakoshi at ucsd.edu> wrote:
>
>> Dear Morin,
>>
>> Generally speaking...
>>
>> > Could it be any number and which criteria needs to be considered?
>>
>> Isn't it better to choose the one so that the original sampling rate is
>> an integral multiple of the one chosen?
>>
>> > should I have to remove gradient artefacts before running ICA and then
>> trying to find the remaining effect of gradient artefact in ICA components?
>>
>> Yes definitely. It is because grandient artifact has very high amplitude.
>> Actually I recommend you try it yourself to see what happens. For artifact
>> subtraction I liked Liu's singular value decomposition approach (NeuroImage
>> 2012) because it does not smear out artifacts.
>>
>> Makoto
>>
>> On Mon, Jun 30, 2014 at 8:04 AM, mori larin <morilarin88 at gmail.com>
>> wrote:
>>
>>> Dear list,
>>>
>>> I am using EEG data and I have two questions:
>>> 1) I am not sure about sampling rate. The EEG data was recorded at 5000
>>> Hz and I have to down sample it for further work. I used 256 Hz and I do
>>> not know is it correct or not. How should we select the re-sampling rate?
>>> Could it be any number and which criteria needs to be considered?
>>>
>>> 2) For the EEG data which is recorded simultaneously with fMRI data, in
>>> order to remove gradient and BCG artefacts automatically from the data
>>> using ICA , should I have to remove gradient artefacts before running ICA
>>> and then trying to find the remaining effect of gradient artefact in ICA
>>> components? (and what are the methods to remove it) or I have to run ICA on
>>> the contaminated data directly? The latter I think I have to expect more
>>> components associated to gradient artefacts because the amplitude of the
>>> gradient artefacts are larger than brain signals.
>>>
>>> I really appreciate it if you could help me,
>>>
>>> Regards,
>>> Morin
>>>
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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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