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| In the first step, we call maxfilter to establish which channels in the data set are bad by using the -autobad option. Maxfilter scans the first 20 seconds of your recording (or however long you waited before turing on cHPI) and returns the output to a log file. To save time we tell maxfilter to skip the remainder of the file. | In the first step, we call maxfilter to establish which channels in the data set are bad by using the -autobad option. Maxfilter scans the first 20 seconds of your recording (or however long you waited before turing on cHPI) and returns the output to a log file. To save time we tell maxfilter to skip the remainder of the file. The -ctc and -cal options refer to the fine calibration and cross talk correction data specific to our MEG system. Inputting them to maxfilter will produce a better result. |
A practical guide to using maxfilter for meg data pre-processing. Typically pre-processing requires three steps:
- finding bad channels,
- applying sss to remove noise,
- transforming the data to a different co-ordinate frame.
Each step is detailed below along with code that you can use. NB you need to input your file names in the appropriate places.
Step 1. Identifying Bad Channels with Maxfilter
In the first step, we call maxfilter to establish which channels in the data set are bad by using the -autobad option. Maxfilter scans the first 20 seconds of your recording (or however long you waited before turing on cHPI) and returns the output to a log file. To save time we tell maxfilter to skip the remainder of the file. The -ctc and -cal options refer to the fine calibration and cross talk correction data specific to our MEG system. Inputting them to maxfilter will produce a better result.
maxfilter -f <rawdata_file> -o <output_file> -ctc /neuro/databases/ctc/ct_sparse.fif -cal /neuro/databases/sss/sss_cal.dat -autobad 20 -skip 21 999999 -v | tee <log_file>
