R/data_epoching.R

In craddm/eegUtils: Utilities for Electroencephalographic (EEG) Analysis

#' Create epochs from EEG data
#'
#' Creates epochs around specified event triggers. Requires data of class
#' `eeg_data`. Where multiple events are specified, epochs will be created
#' around each event.
#'
#' @author Matt Craddock \email{matt@@mattcraddock.com}
#' @param data Continuous data to be epoched.
#' @param ... Parameters passed to functions
#' @export

epoch_data <- function(data,
                       ...) {
  UseMethod("epoch_data", data)
}

#' Create epochs from EEG data
#'
#' @param data Continuous data to be epoched.
#' @param ... Parameters passed to functions
#' @export

epoch_data.default <- function(data, ...) {
  stop("Requires object of class eeg_data.")
}

#' @param events Character vector of events to epoch around.
#' @param time_lim Time in seconds to form epoch around the events. Defaults to
#'   one second either side.
#' @param baseline Baseline times to subtract. Can be set to a numeric vector of length two to specify
#'   a time window to use as a baseline in each epoch (e.g. c(-.1, 0)), "none",
#'   which will perform no baseline correction, or `NULL` to use the mean of the
#'   whole epoch. As of v0.6 of `eegUtils`, the default is "none".
#' @param epoch_labels Character vector of same length as events which'll be
#'   used to label the epochs.
#' @importFrom dplyr left_join inner_join
#' @importFrom purrr map map_df
#' @importFrom tibble tibble
#' @return Returns an epoched object of class `eeg_epochs`
#'
#' @describeIn epoch_data Epoch `eeg_data` objects
#'
#' @export

epoch_data.eeg_data <- function(data,
                                events,
                                time_lim = c(-1, 1),
                                baseline = "none",
                                epoch_labels = NULL,
                                ...) {

  if (!any(events %in% unique(data$events$event_type))) {
    stop("No events found - check event codes.")
  }

  if (!all(events %in% unique(data$events$event_type))) {
    warning("Some events not found - check event codes.")
  }

  event_tags <- NULL
  if (!is.null(epoch_labels) && length(events) != length(epoch_labels)) {
    stop("event_labels must be the same length as events.")
    } else if (!is.null(epoch_labels)) {
      event_tags <- tibble::tibble(event_type = events,
                                   epoch_labels = epoch_labels)
      events(data) <-
        dplyr::left_join(events(data),
                         tibble::tibble(event_type = events,
                                        event_label = epoch_labels),
                         by = "event_type")
   }

  # generate time vector
  init_times <- seq(time_lim[1], time_lim[2], 1/data$srate)
  #check if times need adjusting.
  time_check <- min(abs(init_times))
  if (time_check != 0) {
    time_offset <- init_times[which.min(abs(init_times))]
    init_times <- init_times - time_offset
    message("Adjusting output limits to match sampling rate.")
    time_lim[1] <- min(init_times)
    time_lim[2] <- max(init_times)
  }

  message(paste("Output limits: ", time_lim[1], time_lim[2]))

  # If the data has been downsampled, sample spacing will be greater than 1.
  # Subsequent steps need to account for this when selecting based on sample number.
  # Multiplying srate by spacing accomplishes this.

  samp_diff <- min(diff(data$timings$sample))
  srate <- data$srate * samp_diff

  # create a vector that counts back and ahead of the timelock event in samples
  # i.e. if srate is 1000, a vector from -100 to 0 to 100 would be -.1 s before
  # to .1 s after event onset

  samps <- init_times / (1/srate)
  event_table <- data$events

  # go through all events and find which match those specified as the events to
  # epoch around

  epoch_zero <- event_table[which(event_table$event_type %in% events), ][["event_onset"]]

  # for each epoching event, create a vector of samples before and after that
  # are within the time limits
  epoched_data <- purrr::map(epoch_zero,
                             ~ . + samps)

  epoched_data <- purrr::map_df(epoched_data,
                                ~ tibble::tibble(sample = .,
                                                 time = samps / srate),
                                .id = "epoch")

  epoched_data$epoch <- as.numeric(epoched_data$epoch)

  # create new event_table
  event_table <- dplyr::inner_join(event_table,
                                   epoched_data,
                                   by = c("event_onset" = "sample"))

   epoched_data <- dplyr::left_join(epoched_data,
                                    cbind(data$signals,
                                          data$timings),
                                    by = c("sample" = "sample"))
  # epoched_data <- data.frame(
  #   epoch = rep(
  #     1:length(epoch_zero),
  #     each = length(samps)
  #     ),
  #   sample = rowSums(
  #     expand.grid(
  #       samps,
  #       epoch_zero)
  #     ),
  #   time = init_times)
  #
  # epoched_data <- merge(epoched_data,
  #                       cbind(data$signals,
  #                             data$timings),
  #                       by = "sample")
  # Check for any epochs that contain NAs
  epoched_data <- split(epoched_data,
                        epoched_data$epoch)

  na_epochs <- vapply(epoched_data,
                      function(x) any(is.na(x)),
                      FUN.VALUE = logical(1))

  epoched_data <- epoched_data[!na_epochs]
  epoched_data <- do.call("rbind",
                          epoched_data)

  event_table <- event_table[event_table$epoch %in% names(na_epochs[!na_epochs]), ]

  if (any(na_epochs)) {
    message(paste(sum(na_epochs),
                  "epoch(s) with NAs removed. Epoch boundaries would lie outside data."))
  }

  epoched_data$time.y <- NULL
  names(epoched_data)[[3]] <- "time"

  if (is.null(data$epochs)) {
    data$epochs <-
      tibble::new_tibble(list(epoch = 1,
                              participant_id = character(1),
                              recording = character(1)),
                         nrow = 1,
                         class = "epoch_info")
  }

  epoch_trigs <- event_table[event_table$event_type %in% events,
                             c("event_type", "epoch")]

  if (!is.null(event_tags)) {
    epoch_trigs <- dplyr::left_join(epoch_trigs,
                                    event_tags, by = "event_type")
  }

  n_epochs <- length(unique(epoched_data$epoch))

  epochs <-
    tibble::new_tibble(list(epoch = 1:n_epochs,
                            participant_id = rep(data$epochs$participant_id, n_epochs),
                            recording = rep(data$epochs$recording, n_epochs)),
                            nrow = n_epochs,
                            class = "epoch_info")

  epochs <- dplyr::left_join(epochs,
                             as_tibble(epoch_trigs),
                             by = "epoch")

  data <- eeg_epochs(data = epoched_data[, -1:-3],
                     srate = data$srate,
                     timings = epoched_data[, 1:3],
                     events = event_table,
                     chan_info = data$chan_info,
                     reference = data$reference,
                     epochs = epochs)

  if (!identical(baseline, "none")) {
    data <- rm_baseline(data,
                        time_lim = baseline)
  } else {
    message("No baseline removal performed.")
  }

  n_epochs <- length(unique(epochs$epoch))
  message(paste("Creating", n_epochs, "epochs."))

  data
}

#' @export

epoch_data.eeg_epochs <- function(data, ...) {
  stop("Data is already epoched, cannot currently re-epoch.")
}