[Eeglablist] Sampling rate

[Makoto Miyakoshi]

Dear Mori,

> Are my interpretation correct?

No. See below.

> Liue's singular value decomposition approach removes all the gradient
artefacts completely before running ICA on data. So nothing will remain for
ICA to detect and separate.

His SVD-based method works *better* than the simple template subtraction in
my opinion, but no method can do this job perfectly due to data
non-stationarity. After all, data quality depends on how cooperative
subjects are.

> Also, gradient artefacts are not similar to blink and muscle artefacts
which can be separated by ICA.

Depending on your TR and the number of slices, it can make a peak at the
theta, alpha, and beta ranges. ECG is harder to remove, and it tends to
overlap theta.

So it's pretty challenging.
If you haven't seen Paul Sajda's nice invention of EEG cap for fMRI-EEG
recording, you should check it. In his talk he said this hardware-level
solution is very important for good data quality.

Makoto

On Thu, Nov 20, 2014 at 10:05 AM, mori larin <morilarin88 at gmail.com> wrote:

> Dear Makoto,
>
> Thank you for your response and comments, I appreciate it.
> So from what I understand, Liue's singular value decomposition approach
> removes all the gradient artefacts completely before running ICA on data.
> So nothing will remain for ICA to detect and separate. Also, gradient
> artefacts are not similar to blink and muscle artefacts which can be
> separated by ICA.
> Are my interpretation correct?
>
> Best regards,
> Mori
>
> On Sunday, 16 November 2014, Makoto Miyakoshi <mmiyakoshi at ucsd.edu> wrote:
>
>> > 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
>>
>


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