'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






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