Note that this reference documentation is identical to the help that is displayed in MATLAB when you type “help ft_rejectvisual”.

  FT_REJECTVISUAL shows the preprocessed data in all channels and/or trials to
  allow the user to make a visual selection of the data that should be
  rejected. The data can be displayed in a "summary" mode, in which case
  the variance (or another metric) in each channel and each trial is
  computed. Alternatively, all channels can be shown at once allowing
  paging through the trials, or all trials can be shown, allowing paging
  through the channels.
  Use as
    [data] = ft_rejectvisual(cfg, data)
  The configuration can contain
    cfg.channel     = Nx1 cell-array with selection of channels (default = 'all'),
                      see FT_CHANNELSELECTION for details
    cfg.trials      = 'all' or a selection given as a 1xN vector (default = 'all')
    cfg.latency     = [begin end] in seconds, or 'minperlength', 'maxperlength',
                      'prestim', 'poststim' (default = 'maxperlength')
    cfg.method      = string, describes how the data should be shown, this can be
                      'summary'  show a single number for each channel and trial (default)
                      'channel'  show the data per channel, all trials at once
                      'trial'    show the data per trial, all channels at once
    cfg.keepchannel = string, determines how to deal with channels that are
                      not selected, can be
                      'no'     completely remove unselected channels from the data (default)
                      'yes'    keep unselected channels in the output data
                      'nan'    fill the channels that are unselected with NaNs
    cfg.metric      = string, describes the metric that should be computed in summary mode
                      for each channel in each trial, can be
                      'var'       variance within each channel (default)
                      'min'       minimum value in each channel
                      'max'       maximum value each channel
                      'maxabs'    maximum absolute value in each channel
                      'range'     range from min to max in each channel
                      'kurtosis'  kurtosis, i.e. measure of peakedness of the amplitude distribution
                      'zvalue'    mean and std computed over all time and trials, per channel
    cfg.alim        = value that determines the amplitude scaling for the
                      channel and trial display, if empty then the amplitude
                      scaling is automatic (default = [])
    cfg.eegscale    = number, scaling to apply to the EEG channels prior to display
    cfg.eogscale    = number, scaling to apply to the EOG channels prior to display
    cfg.ecgscale    = number, scaling to apply to the ECG channels prior to display
    cfg.emgscale    = number, scaling to apply to the EMG channels prior to display
    cfg.megscale    = number, scaling to apply to the MEG channels prior to display
    cfg.gradscale   = number, scaling to apply to the MEG gradiometer channels prior to display (in addition to the cfg.megscale factor)
    cfg.magscale    = number, scaling to apply to the MEG magnetometer channels prior to display (in addition to the cfg.megscale factor)
  The scaling to the EEG, EOG, ECG, EMG and MEG channels is optional and can
  be used to bring the absolute numbers of the different channel types in
  the same range (e.g. fT and uV). The channel types are determined from
  the input data using FT_CHANNELSELECTION.
  Optionally, the raw data is preprocessed (filtering etc.) prior to
  displaying it or prior to computing the summary metric. The
  preprocessing and the selection of the latency window is NOT applied
  to the output data.
  The following settings are usefull for identifying EOG artifacts:
    cfg.preproc.bpfilter    = 'yes'
    cfg.preproc.bpfilttype  = 'but'
    cfg.preproc.bpfreq      = [1 15]
    cfg.preproc.bpfiltord   = 4
    cfg.preproc.rectify     = 'yes'
  The following settings are usefull for identifying muscle artifacts:
    cfg.preproc.bpfilter    = 'yes'
    cfg.preproc.bpfreq      = [110 140]
    cfg.preproc.bpfiltord   =  8
    cfg.preproc.bpfilttype  = 'but'
    cfg.preproc.rectify     = 'yes'
    cfg.preproc.boxcar      = 0.2
  To facilitate data-handling and distributed computing you can use
    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.

reference/ft_rejectvisual.txt · Last modified: 2014/09/18 13:00 (external edit)

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