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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). Some PDFs that describe the SSS mathematics are here: [[attachment:Taulu_IEEE_05.pdf]], [[attachment:Taulu_JAP_05.pdf]], [[attachment:Taulu_PMB_06.pdf]]. | 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 application by Samu Taulu et al. |
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Maxfilter is essential if you have acquired your data using Maxshield (to remove the shielding effects). It is also helpful for 1) removing noise, particularly using its "temporal extension", 2) detecting bad channels, 3) realigning (interpolated) data after movement (provided you have used continuous HPI) and 4) moving the data to a standard space (e.g, across subjects). | Maxfilter is essential if you have acquired your data using Maxshield (to remove the shielding effects). It is also helpful for 1) removing noise, particularly using its "temporal extension", 2) detecting bad channels, 3) realigning (interpolated) data after movement (provided you have used continuous HPI) and 4) moving the data to a standard space (e.g, across subjects). |
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MaxFilter
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 application by Samu Taulu et al.
Maxfilter is essential if you have acquired your data using Maxshield (to remove the shielding effects). It is also helpful for 1) removing noise, particularly using its "temporal extension", 2) detecting bad channels, 3) realigning (interpolated) data after movement (provided you have used continuous HPI) and 4) moving the data to a standard space (e.g, across subjects).
In the past, we have had Maxfilter versions 2.0 and 2.1 (see below); we now use version 2.2. You can find user manuals under the relevant versions of Maxfilter
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.