#' Baseline correction
#'
#' Used to remove the mean of a specified time period from the data. Currently
#' only performs subtractive baseline. With a data frame, searches for
#' "electrode" and "epoch" columns, and groups on these when found. An electrode
#' column is always required; an epoch column is not.
#'
#' @author Matt Craddock \email{matt@@mattcraddock.com}
#' @param data Data to be baseline corrected.
#' @param ... other parameters to be passed to functions
#' @export
rm_baseline <- function(data, ...) {
UseMethod("rm_baseline", data)
}
#' @param time_lim Numeric character vector (e.g. time_lim <- c(-.1, 0)). If
#' none given, defaults to mean of the whole of each epoch if the data is epoched, or the
#' channel mean if the data is continuous.
#' @describeIn rm_baseline remove baseline from continuous \code{eeg_data}
#' @export
rm_baseline.eeg_data <- function(data, time_lim = NULL, ...) {
if (is.null(time_lim)) {
baseline_dat <- colMeans(data$signals)
} else {
base_times <- select_times(data,
time_lim = time_lim)
baseline_dat <- colMeans(base_times$signals)
}
data$signals <- sweep(data$signals,
2,
baseline_dat,
'-')
data
}
#' @describeIn rm_baseline Remove baseline from eeg_epochs
#' @export
rm_baseline.eeg_epochs <- function(data,
time_lim = NULL,
...) {
n_epochs <- length(unique(data$timings$epoch))
n_times <- length(unique(data$timings$time))
n_chans <- ncol(data$signals)
elecs <- names(data$signals)
if (is.null(time_lim)) {
# reshape to 3D matrix
data$signals <- as.matrix(data$signals)
dim(data$signals) <- c(n_times, n_epochs, n_chans)
# colMeans gives an n_epochs * n_channels matrix - i.e. baseline value for
# each epoch and channel
baseline_dat <- colMeans(data$signals)
# now we go through each timepoint subtracting the baseline values
data$signals <- sweep(data$signals,
c(2, 3),
baseline_dat)
} else {
base_times <- select_times(data,
time_lim = time_lim)
base_times$signals <- as.matrix(base_times$signals)
n_bl_times <- length(unique(base_times$timings$time))
dim(base_times$signals) <- c(n_bl_times, n_epochs, n_chans)
base_times <- colMeans(base_times$signals)
data$signals <- as.matrix(data$signals)
dim(data$signals) <- c(n_times, n_epochs, n_chans)
data$signals <- sweep(data$signals,
c(2, 3),
base_times,
"-")
}
# Reshape and turn back into data frame
data$signals <- array(data$signals,
dim = c(n_epochs * n_times, n_chans))
data$signals <- as.data.frame(data$signals)
names(data$signals) <- elecs
data
}
#' @describeIn rm_baseline Legacy method for data.frames
#' @export
rm_baseline.data.frame <- function(data,
time_lim = NULL,
...) {
if (!("time" %in% colnames(data))) {
stop("Time dimension is required.")
}
if (length(time_lim) == 1) {
stop("time_lim should specify the full time range.")
}
# if the data is epoched, group by electrode and epoch; otherwise, just by
# electrode.
if ("epoch" %in% colnames(data)) {
data <- dplyr::group_by(data,
electrode,
epoch,
add = TRUE)
} else{
data <- dplyr::group_by(data,
electrode,
add = TRUE)
}
if (is.null(time_lim)) {
# if no time_lim provided, just delete mean of all time points
data <- dplyr::mutate(data,
amplitude = amplitude - mean(amplitude))
} else {
data_sel <- dplyr::filter(data,
time >= time_lim[1],
time <= time_lim[2])
baseline <- dplyr::summarise(data_sel,
bl = mean(amplitude))
# This is relatively memory intensive - not so bad now but would prefer
# another way. Could get extremely painful with time-frequency data.
data <- dplyr::left_join(data,
baseline)
data <- dplyr::mutate(data,
amplitude = amplitude - bl)
data <- dplyr::select(data,
-bl)
}
data <- ungroup(data)
data
}
#' @param type Type of baseline correction to apply. Options are ("divide",
#' "ratio", "absolute", "db")
#' @describeIn rm_baseline Method for \code{eeg_tfr} objects
#' @export
rm_baseline.eeg_tfr <- function(data,
time_lim = NULL,
type = "divide",
...) {
valid_types <- c("absolute",
"divide",
"pc",
"ratio",
"db")
if (!(type %in% valid_types)) {
stop("Unknown baseline type ", type)
}
bline <- select_times(data, time_lim)
bline <- colMeans(bline$signals, na.rm = TRUE)
# This function implements the various baseline correction types
do_corrs <- function(data,
type,
bline) {
switch(type,
"divide" = ((data - bline) / bline) * 100,
"pc" = ((data - bline) / bline) * 100 - 100,
"absolute" = data - bline,
"db" = 10 * log10(data / bline),
"ratio" = data / bline
)
}
orig_dims <- dim(data$signals)
orig_dimnames <- dimnames(data$signals)
data$signals <- apply(data$signals,
1,
do_corrs,
type = type,
bline = bline)
dim(data$signals) <- c(orig_dims[2],
orig_dims[3],
orig_dims[1])
data$signals <- aperm(data$signals,
c(3, 1, 2))
dimnames(data$signals) <- orig_dimnames
data$freq_info$baseline <- type
data$freq_info$baseline_time <- time_lim
data
}
#' @describeIn rm_baseline Method for \code{eeg_evoked} objects
#' @export
rm_baseline.eeg_evoked <- function(data,
time_lim = NULL,
...) {
if (is.null(time_lim)) {
baseline_dat <- colMeans(data$signals)
} else {
base_times <- select_times(data,
time_lim = time_lim)
baseline_dat <- colMeans(base_times$signals)
}
data$signals <- sweep(data$signals,
2,
baseline_dat,
"-")
data
}
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.