Hi rujing,
That would compute a TSNR result, but it might not be so desirable. That would not account for slow drifts, motion parameters in the data, etc. The drifts would make the biggest difference.
The way it is computed in afni_proc.py is to take the mean of the signal before the final linear re
gression divided by the stdev of the noise (the residual from the regression, after task, drifts, motion, etc. are removed).
Consider how it is done in
AFNI_data6/FT_analysis/s12.proc.FT.align, for example. Search for TSNR.
- rick