Used to correct data using the mean of a specified time period. For
time-domain data, this will subtract the mean from all data. For eeg_tfr
objects, a variety of methods are available, including subtraction, and
conversion to "dB" change. With a data frame, it will search for "electrode"
and "epoch" columns, and groups on these when found. An electrode column is
always required; an epoch column is not. Note that baseline correction is
always applied on single-trial basis. For baseline correction based on
subtraction, this makes no difference compared to averaging first and then
baseline correcting, but for divisive measures used with time-frequency data,
this distinction can be very important, and can lead to counterintuitive
results.
rm_baseline(data, time_lim = NULL, ...)
# S3 method for eeg_data
rm_baseline(data, time_lim = NULL, verbose = TRUE, ...)
# S3 method for eeg_epochs
rm_baseline(data, time_lim = NULL, verbose = TRUE, ...)
# S3 method for data.frame
rm_baseline(data, time_lim = NULL, verbose = TRUE, ...)
# S3 method for eeg_tfr
rm_baseline(data, time_lim = NULL, type = "divide", verbose = TRUE, ...)
# S3 method for eeg_evoked
rm_baseline(data, time_lim = NULL, verbose = TRUE, ...)Data to be baseline corrected.
Numeric character vector (e.g. time_lim <- c(-.1, 0)) defining the time period to use as a baseline. If the value is NULL, it uses the mean of the whole of each epoch if the data is epoched, or the channel mean if the data is continuous.
other parameters to be passed to functions
Defaults to TRUE. Output descriptive messages to console.
Type of baseline correction to apply. Options are ("divide", "ratio", "absolute", "db", and "pc")
An eegUtils object or a data.frame, depending on the input.
eeg_data: remove baseline from continuous eeg_data
eeg_epochs: Remove baseline from eeg_epochs
data.frame: Legacy method for data.frames
eeg_tfr: Method for eeg_tfr objects
eeg_evoked: Method for eeg_evoked objects
rm_baseline(demo_epochs)
#> Removing channel means per epoch...
#> Epoched EEG data
#>
#> Number of channels : 11
#> Number of epochs : 80
#> Epoch limits : -0.197 - 0.451 seconds
#> Electrode names : A5 A13 A21 A29 A31 B5 B6 B8 B16 B18 B26
#> Sampling rate : 128 Hz
#> Reference : average
rm_baseline(demo_epochs, c(-.1, 0))
#> Baseline: -0.1 sBaseline: 0 s
#> Epoched EEG data
#>
#> Number of channels : 11
#> Number of epochs : 80
#> Epoch limits : -0.197 - 0.451 seconds
#> Electrode names : A5 A13 A21 A29 A31 B5 B6 B8 B16 B18 B26
#> Sampling rate : 128 Hz
#> Reference : average