Calculate averages (e.g. event-related potentials) for single datasets

Source: R/data_averaging.R

This function is used to create an eeg_evoked object from eeg_epochs. By default, it will try to keep different conditions in the data separate using the epochs metadata from the object, thus yielding one average per condition. Alternatively, the user can specify which averages they want using the cols argument.

eeg_average(data, ...)

# Default S3 method
eeg_average(data, ...)

# S3 method for class 'eeg_epochs'
eeg_average(data, cols = NULL, verbose = TRUE, ...)

# S3 method for class 'eeg_evoked'
eeg_average(data, cols = NULL, weighted = TRUE, verbose = TRUE, ...)

# S3 method for class 'eeg_tfr'
eeg_average(data, cols = NULL, weighted = TRUE, verbose = TRUE, ...)

Arguments

data

An eeg_epochs of eeg_tfr object.

...

Other arguments passed to the averaging functions

cols

Columns from the epochs structure that the average should group on. NULL, the default, uses all columns other than the epoch column.

verbose

Print informative messages during averaging. Defaults to TRUE

weighted

Produce a weighted average over epochs, which accounts for upstream differences in the number of epochs that contribute to each average.

Value

An object of class eeg_evoked if applied to eeg_epochs; eeg_tfr if applied to eeg_tfr.

Methods (by class)

  • eeg_average(default): Default method for averaging EEG objects

  • eeg_average(eeg_epochs): Create evoked data from eeg_epochsobjects

  • eeg_average(eeg_evoked): average an eeg_evoked object over epochs.

  • eeg_average(eeg_tfr): average an eeg_tfr object over epochs.

Author

Matt Craddock matt@mattcraddock.com

Examples

eeg_average(demo_spatial)
#> Creating epochs based on combinations of variables: participant_id epoch_labels 
#> Evoked EEG data
#> 
#> Number of channels	:	 23 
#> Epoch limits		: -0.301 - 0.488 seconds
#> Electrode names		:	 Fp1 Fp2 Fz Cz Pz P3 P4 C3 C4 P7 P8 F3 F4 T7 T8 F7 F8 Oz Fpz EXG1 EXG2 EXG3 EXG4 
#> Sampling rate		:	 128  Hz
eeg_average(demo_spatial, cols = "everything")
#> Creating epochs based on combinations of variables: participant_id 
#> Evoked EEG data
#> 
#> Number of channels	:	 23 
#> Epoch limits		: -0.301 - 0.488 seconds
#> Electrode names		:	 Fp1 Fp2 Fz Cz Pz P3 P4 C3 C4 P7 P8 F3 F4 T7 T8 F7 F8 Oz Fpz EXG1 EXG2 EXG3 EXG4 
#> Sampling rate		:	 128  Hz