'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?
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.
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
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
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Panagiotis S. Tsiatsis
Max Planck Institute for Biogical Cybernetics
Cognitive NeuroImaging Group
Tuebingen, Germany
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