Hello dear Vladimir, hello dear all,
Let me come back to this issue - first of all I absolutely agree that
The slow drifts will only affect the lowermost frequency bin (if it
includes the DC) so baseline correction in the time domain does not
rescale all the frequencies or anything of that sort.
but the funny thing (and the main reason why I send the previous e-mail)
is that after processing the same data once with baseline correction and
once without, the Time Frequency analysis of the mean trials differ even
in frequencies as high as 10 - 20Hz and this difference can be (at
least) in the range (-2,2)*10^-25. ( I calculated the contrast of the
means of the TF data with and without baseline correction). This is one
order of magnitude less that my strongest activations in average TF
(~4*10^-24) but comparable to the contrast values among conditions in
TF. I understand that baseline correction affects the artifact rejection
process as well but to me the effect seems far than being small and
insignificant. I also have to note that I have more than 150 trials per
conditions whether I apply baseline correction or not and this number is
really similar in each case (+-5 trials). The baseline duration that I
used for testing was 100 ms.
I would absolutely expect to see the very same thing that you wrote in
your previous email - but this is not the case. Any intuitions?
Thanks and best,
P.
On 11/11/2010 6:09 PM, Vladimir Litvak wrote:
> Dear Panagiotis,
>
> On Thu, Nov 11, 2010 at 4:21 PM, Panagiotis Tsiatsis
> <[log in to unmask]> wrote:
>> 'Baseline correction is no longer done automatically by spm_eeg_filter. Use
>> spm_eeg_bc if necessary.'
>>
>> Dear All,
>>
>> I 've got a naive question concerning filtering and baseline correction in
>> MEG data. When applying high-pass filtering in the data, the following
>> message appears:
>>
>> 'Baseline correction is no longer done automatically by spm_eeg_filter. Use
>> spm_eeg_bc if necessary.'
>>
>> 1st Question: I suppose this means that the filtering functions does not
>> subtract the mean of the trial / continuous file, that is the zero
>> coefficient of the fourier transform, right?
>>
> Yes, the filtering function used to subtract the baseline in SPM5 so
> that warning is there for historical reasons.
>
>> 2nd Question: Would it be neccessary to apply Baseline Correction in MEG
>> data? That is, are there any DC compponent biases that might differ across
>> subjects or "strong", very slow drifts in the recorded activity across time?
>> I guess it should be neccessary for EEG data where there are amplifier
>> offset and slow conductance drifts, but I am not totally sure if this is the
>> case for MEG recordings
>>
>> 3rd Question: I am mainly asking the above questions because I want to
>> compare the difference in activity in the Time-Frequncy domain among
>> conditions (difference in power across various frequncy bands in time), and
>> I think that in one sense applying baseline correction in the time domain
>> and then transforming it to the Time - Frequency domain kind of normalizes
>> the power of activity across the different frequency bands according to the
>> baseline, which might eventually smear out the effect (difference in
>> frequency amplitude in time) that I want to see. In that sense I think that
>> applying or not Baseline corrections is a matter of what I want to check for
>> (relative/absolute power differences). The bottom-line question then would
>> be whether or not it is absolutely neccessary to apply baseline correction
>> in MEG (time / time-frequency) data because for example there would be DC
>> biases that would be different for different recordings.
>>
> There are slow drifts in the MEG that in most cases necessitate
> baseline correction of high-pass filtering if you want to look at
> ERFs. However, this is not relevant for your time-frequency analysis.
> The slow drifts will only affect the lowermost frequency bin (if it
> includes the DC) so baseline correction in the time domain does not
> rescale all the frequencies or anything of that sort. The only problem
> might be that large DC offsets in the data confuse some TF estimation
> methods so I'd at least subtract the baseline or the mean before doing
> TF.
>
>> 4th Question (irrelevant to the others): I know it would be computationally
>> extremely heavy, but is there a way to transform continuous data in the Time
>> - Frequency domain? It would be useful as then i.e. I would not have to
>> apply TF every time that I reepoch the data and I would have no
>> "edge-effects" when converting single trials in TF. Plus, it would be
>> helpful in eyeballing spontaneous activity data
>>
>>
> This is possible in principle but SPM functions will have great
> difficulties handling this kind of data. If you want to do it for 275
> MEG channels you'll have huge data arrays and can run into memory
> problems. So if you want to do it you need to write your own code
> possibly using Fieldtrip functions and only convert to SPM format once
> you extract your epochs. What you can do to avoid edge effects is to
> pad your epochs with extra data. There is now a function called
> spm_eeg_crop (I think it was added after the latest public release but
> I can send it to you) that you can use to later remove that padding
> from your TF dataset.
>
> Best,
>
> Vladimir
>
>> I would really appreciate your opinion on these matters. I know that they
>> might be really basic questions, but I still don't feel absolutely sure
>> about the answers.
>>
>> Thanks and best, and apologies for the long e-mail - I tried to explain my
>> questions as clearly as I could.
>>
>> Panagiotis
>>
>>
>>
>>
>>
>>
>> --
>> Panagiotis S. Tsiatsis
>> Max Planck Institute for Biogical Cybernetics
>> Cognitive NeuroImaging Group
>> Tuebingen, Germany
>>
--
Panagiotis S. Tsiatsis
Max Planck Institute for Biogical Cybernetics
Cognitive NeuroImaging Group
Tuebingen, Germany
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