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craddmcraddm
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fix conflict in topoplot
Merge branch 'master' of https://github.com/craddm/eegUtils # Conflicts: # R/topoplot.R # man/topoplot.Rd
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‎.travis.yml

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@@ -7,5 +7,6 @@ addons:
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apt:
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packages:
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- libfreetype6
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r_github_packages: lionel-/vdiffr
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after_success:
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- Rscript -e 'covr::codecov()'

‎NAMESPACE

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@@ -46,6 +46,9 @@ S3method(interp_elecs,eeg_data)
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S3method(list_epochs,eeg_ICA)
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S3method(list_epochs,eeg_epochs)
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S3method(plot_butterfly,default)
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S3method(plot_butterfly,eeg_data)
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S3method(plot_butterfly,eeg_epochs)
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S3method(plot_butterfly,eeg_evoked)
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S3method(plot_electrodes,default)
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S3method(plot_electrodes,eeg_data)
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S3method(plot_psd,data.frame)
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S3method(rm_baseline,data.frame)
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S3method(rm_baseline,eeg_data)
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S3method(rm_baseline,eeg_epochs)
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S3method(rm_baseline,eeg_evoked)
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S3method(rm_baseline,eeg_tfr)
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S3method(run_ICA,eeg_epochs)
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S3method(select_elecs,default)

‎NEWS.md

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### Function changes
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- `topoplot()` now has a scaling parameter to scale the size of any lines or markers drawn on the plot
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- `plot_tfr` function now useable, with baseline correction added.
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- `rm_baseline` now handles `eeg_tfr` objects.
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- `as.data.frame` method added for `eeg_tfr` objects.
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- `compute_tfr` function now available for use.
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- Data selectors added for `eeg_tfr` objects (e.g. `select_elecs`)
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- `plot_tfr()` function now useable, with baseline correction added.
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- `rm_baseline()` now handles `eeg_tfr` objects.
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- `as.data.frame()` method added for `eeg_tfr` objects.
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- `compute_tfr()` function now available for use with Morlet wavelets.
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- `plot_psd()` now allows changing of FFT parameters (e.g. number off FFT points, segment length)
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- Data selectors added for `eeg_tfr` objects (e.g. `select_elecs()`)
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### Internal changes/ bug fixes
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- `plot_timecourse()` overhauled to be S3 method
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- `plot_butterfly()` reworked internally to be more efficient
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- `rm_baseline()` simplified internally, reworked to use matrices; split to separate file.
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- `select_elecs()` now works for `eeg_evoked` objects
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- `eeg_decomp` function in progress for performing SSD analyses
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- Various methods added for TFR analyses
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- `plot_tfr()` function added to handle `eeg_tfr` objects.
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- `erp_image()` now works with `eeg_ICA` objects
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- Generic print methods added for `eeg_epochs` and `eeg_data`
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- `compute_tfr` function added to performed TFA on `eeg_epochs`
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- `compute_tfr()` function added to performed TFA on `eeg_epochs`
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- `epoch_data()` now warns if some events are not found rather than stops. Only stops if *no* events are found.
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### Internal changes/ bug fixes
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- `reref_eeg()`
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- correctly excludes multiple named electrodes (i.e. passed as characters rather than numbers), where it previously silently failed.
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- no longer records the reference data in the `ref_data` field
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- `tf_morlet` recoded to be called internally
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- `compute_psd`
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- `compute_psd()`
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- recoded to call `welch_fft()` in order to support possibility of different FFT methods.
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- now drops the DC component (frequency 0)
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- `welch_fft()` internal function added

‎R/baseline_correction.R

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

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