A common first step in analysing your data is to pre-processing them using the Maxfilter program. This Neuromag sofware implements "Signal-Space Separation" (SSS), which is a clever mathematical way to separate magnetic signals coming from within the brain (or more precisely, a sphere within the sensors) from those coming from outside the brain (or more precisely, from outside a sphere outside the sensors). There are several technical papers on Maxfilter theory and applications e.g. this and this.
Maxfilter is helpful for 1) removing noise originating from sources outside the sensor array, 2) detecting bad channels, 3) realigning (interpolating) data after movement (provided you have used continuous HPI) and 4) moving the data to a standard space (e.g, across subjects).
Maxfilter use at the CBU
Our latest Maxfilter version is Maxfilter 2.2.14 (but the previous version 2.2.12 is also in use).
The following example will apply Maxfilter including Signal Space Separation (SSS), its temporal extension (ST), and movement compensation.
maxfilter-2.2.14 -f input_file.fif -o output_file.fif -st -origin 0 0 45 -frame head -autobad on -movecomp -cal /neuro/databases_triux/sss/sss_cal.dat -ctc /neuro/databases_triux/ctc/ct_sparse.fif
If you are using data from our previous Vectorview machine, the last two options should be
-cal /neuro/databases_vectorview/sss/sss_cal.dat -ctc /neuro/databases_vectorview/ctc/ct_sparse.fif
For more information on these and other options, see the Maxfilter 2.2 manual
Signal Space Separation is also implemented in MNE-Python.
You can get information about movement parameters using these scripts to analyse maxfilter output.
See also the archives of this email list (www.jiscmail.ac.uk/lists/NEUROMEG.html) which you may also want to join to hear about latest maxfilter updates.
Other related Wiki pages: