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
>
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