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Merge branch 'master' of https://github.com/craddm/eegUtils # Conflicts: # R/topoplot.R # man/topoplot.Rd
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
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| #' 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.") | ||
| } | ||
|
|
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| # 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$signals, na.rm = TRUE) | ||
|
|
||
| # This function implements the various baseline correction types | ||
| do_corrs <- function(data, | ||
| type, | ||
| bline) { | ||
| switch(type, | ||
| "divide" = ((data - bline) / bline) * 100, | ||
| "pc" = ((data - bline) / bline) * 100 - 100, | ||
| "absolute" = data - bline, | ||
| "db" = 10 * log10(data / bline), | ||
| "ratio" = data / bline | ||
| ) | ||
| } | ||
|
|
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| orig_dims <- dim(data$signals) | ||
|
|
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| orig_dimnames <- dimnames(data$signals) | ||
|
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| data$signals <- apply(data$signals, | ||
| 1, | ||
| do_corrs, | ||
| type = type, | ||
| bline = bline) | ||
|
|
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| dim(data$signals) <- c(orig_dims[2], | ||
| orig_dims[3], | ||
| orig_dims[1]) | ||
|
|
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| 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 | ||
| } |
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