diegr (version 0.2.0)

baseline_correction: Baseline correction

Description

Compute amplitude values corrected to the selected baseline.

The function computes a baseline value within each epoch and subtracts it from the signal.

Usage

baseline_correction(data, baseline_range, type = "absolute")

Value

A data frame/tibble with added columns:

signal_base

Signal corrected by subtracting the baseline for each epoch.

baseline

A baseline value used for correction.

Arguments

data

A data frame, tibble or a database table with input data, required columns: time and signal. Optional columns: group, subject, sensor, condition and epoch, if present, are included in the grouping structure.

baseline_range

A numeric vector of time points used as the baseline (e.g., baseline_range = 125:250).

type

A character specifying the type of baseline correction. Currently, only "absolute" is supported, any other value results in an error.

Details

If the values from baseline_range vector extend beyond the range of the time column, the baseline computation proceeds as follows:

1. If a part of the baseline_range vector is in the time column and part is outside its range, the baseline correction is computed only from the part inside a time range.

2. If the whole baseline_range vector is out of the time range, the baseline and also the signal_base values of the output are NA's. In both cases, the function returns the output data along with a warning.

Notes:

• Rows with NA values in the signal column are ignored when computing the baseline, and a warning is issued.

• If any grouping variable present in the data contains only NA values, a warning is issued, as this may lead to invalid or uninformative grouping.

Examples

# Computing baseline correction for subject 1 on first 10 points, sensor "E1"
# a) Prepare data and compute
data01 <- epochdata |> dplyr::filter(.data$subject == 1 & .data$sensor == "E1")
basedata <- baseline_correction(data01, baseline_range = 1:10, type = "absolute")

## Note: You can also use baseline_correction() on the whole epochdata
## and then filter selected subject and sensor, the results are the same,
## the procedure above was chosen only for the speed of the example.

# b) Plot raw (black line) and corrected (red line) signal for epoch 1
epoch1 <- basedata |> dplyr::filter(.data$epoch == 1)
plot(epoch1$signal, type = "l", ylim = c(-20, 30), main = "Raw (black) vs Corrected (red) Signal",
xlab = "time point", ylab = "amplitude")
lines(epoch1$signal_base, col = "red")

# \donttest{
# Set baseline_range outside of time range
# results in NA's in baseline and signal_base columns,
# also returns a warning message
basedata <- baseline_correction(data01, baseline_range = 70:80, type = "absolute")
head(basedata)
# }