Note that this reference documentation is identical to the help that is displayed in Matlab when you type “help ft_preprocessing”.
FT_PREPROCESSING reads MEG and/or EEG data according to user-specified trials and applies several user-specified preprocessing steps to the signals. Use as [data] = ft_preprocessing(cfg) or [data] = ft_preprocessing(cfg, data) The first input argument "cfg" is the configuration structure, which contains all details for the dataset filenames, trials and the preprocessing options. You can only do preprocessing after defining the segments of data to be read from the file (i.e. the trials), which is for example done based on the occurence of a trigger in the data. If you are calling FT_PREPROCESSING with only the configuration as first input argument and the data still has to be read from file, you should specify cfg.dataset = string with the filename cfg.trl = Nx3 matrix with the trial definition, see FT_DEFINETRIAL cfg.padding = length to which the trials are padded for filtering (default = 0) cfg.padtype = string, type of padding (default: 'data' padding or 'mirror', depending on feasibility) cfg.continuous = 'yes' or 'no' whether the file contains continuous data (default is determined automatic) Instead of specifying the dataset, you can also explicitely specify the name of the file containing the header information and the name of the file containing the data, using cfg.datafile = string with the filename cfg.headerfile = string with the filename If you are calling FT_PREPROCESSING with also the second input argument "data", then that should contain data that was already read from file in a previous call to FT_PREPROCESSING. In that case only the configuration options below apply. The channels that will be read and/or preprocessed are specified with cfg.channel = Nx1 cell-array with selection of channels (default = 'all'), see FT_CHANNELSELECTION for details The preprocessing options for the selected channels are specified with cfg.lpfilter = 'no' or 'yes' lowpass filter (default = 'no') cfg.hpfilter = 'no' or 'yes' highpass filter (default = 'no') cfg.bpfilter = 'no' or 'yes' bandpass filter (default = 'no') cfg.bsfilter = 'no' or 'yes' bandstop filter (default = 'no') cfg.dftfilter = 'no' or 'yes' line noise removal using discrete fourier transform (default = 'no') cfg.medianfilter = 'no' or 'yes' jump preserving median filter (default = 'no') cfg.lpfreq = lowpass frequency in Hz cfg.hpfreq = highpass frequency in Hz cfg.bpfreq = bandpass frequency range, specified as [low high] in Hz cfg.bsfreq = bandstop frequency range, specified as [low high] in Hz cfg.dftfreq = line noise frequencies in Hz for DFT filter (default = [50 100 150]) cfg.lpfiltord = lowpass filter order (default set in low-level function) cfg.hpfiltord = highpass filter order (default set in low-level function) cfg.bpfiltord = bandpass filter order (default set in low-level function) cfg.bsfiltord = bandstop filter order (default set in low-level function) cfg.lpfilttype = digital filter type, 'but' or 'fir' or 'firls' (default = 'but') cfg.hpfilttype = digital filter type, 'but' or 'fir' or 'firls' (default = 'but') cfg.bpfilttype = digital filter type, 'but' or 'fir' or 'firls' (default = 'but') cfg.bsfilttype = digital filter type, 'but' or 'fir' or 'firls' (default = 'but') cfg.lpfiltdir = filter direction, 'twopass', 'onepass' or 'onepass-reverse' (default = 'twopass') cfg.hpfiltdir = filter direction, 'twopass', 'onepass' or 'onepass-reverse' (default = 'twopass') cfg.bpfiltdir = filter direction, 'twopass', 'onepass' or 'onepass-reverse' (default = 'twopass') cfg.bsfiltdir = filter direction, 'twopass', 'onepass' or 'onepass-reverse' (default = 'twopass') cfg.medianfiltord = length of median filter (default = 9) cfg.demean = 'no' or 'yes', whether to apply baseline correction (default = 'no') cfg.baselinewindow = [begin end] in seconds, the default is the complete trial (default = 'all') cfg.detrend = 'no' or 'yes', remove linear trend from the data (done per trial) (default = 'no') cfg.polyremoval = 'no' or 'yes', remove higher order trend from the data (done per trial) (default = 'no') cfg.polyorder = polynome order for poly trend removal (default = 2; note that all lower-order trends will also be removed when using cfg.polyremoval) cfg.derivative = 'no' or 'yes', computes the first order derivative of the data (default = 'no') cfg.hilbert = 'no', 'abs', 'complex', 'real', 'imag', 'absreal', 'absimag' or 'angle' (default = 'no') cfg.rectify = 'no' or 'yes' (default = 'no') cfg.precision = 'single' or 'double' (default = 'double') Preprocessing options that you should only use for EEG data are cfg.reref = 'no' or 'yes' (default = 'no') cfg.refchannel = cell-array with new EEG reference channel(s), this can be 'all' for a common average reference cfg.implicitref = 'label' or empty, add the implicit EEG reference as zeros (default = []) cfg.montage = 'no' or a montage structure (default = 'no') Preprocessing options that you should only use when you are calling FT_PREPROCESSING with also the second input argument "data" are cfg.trials = 'all' or a selection given as a 1xN vector (default = 'all') Preprocessing options that you should only use when you are calling FT_PREPROCESSING with a single cfg input argument are cfg.method = 'trial' or 'channel', read data per trial or per channel (default = 'trial') To facilitate data-handling and distributed computing with the peer-to-peer module, this function has the following options: cfg.inputfile = ... cfg.outputfile = ... If you specify one of these (or both) the input data will be read from a *.mat file on disk and/or the output data will be written to a *.mat file. These mat files should contain only a single variable, corresponding with the input/output structure. See also FT_DEFINETRIAL, FT_REDEFINETRIAL, FT_APPENDDATA, FT_APPENDSPIKE
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