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.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.channel = Nx1 cell-array with selection of channels (default = 'all'), see FT_CHANNELSELECTION for details cfg.keepchannel = string, determines how to deal with channels that are not selected, can be 'no' completely remove deselected channels from the data (default) 'yes' keep deselected channels in the output data 'nan' fill the channels that are deselected with NaNs 'repair' repair the deselected channels using FT_CHANNELREPAIR cfg.neighbours = neighbourhood structure, see also FT_PREPARE_NEIGHBOURS (required for repairing channels) cfg.trials = 'all' or a selection given as a 1xN vector (default = 'all') cfg.keeptrial = string, determines how to deal with trials that are not selected, can be 'no' completely remove deselected trials from the data (default) 'yes' keep deselected trials in the output data 'nan' fill the trials that are deselected 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.latency = [begin end] in seconds, or 'minperlength', 'maxperlength', 'prestim', 'poststim' (default = 'maxperlength') 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. See also FT_REJECTARTIFACT, FT_REJECTCOMPONENT