R/file_io.R
In craddm/eegUtils: Utilities for Electroencephalographic (EEG) Analysis
Defines functions
import_set
read_vmrk
read_dat
read_vhdr
import_vhdr
import_cnt
import_raw
Documented in
import_cnt
import_raw
import_set
import_vhdr
read_dat
read_vmrk
#' Function for reading raw data.
#'
#' Currently BDF/EDF, 32-bit .CNT, and Brain Vision Analyzer files are
#' supported. Filetype is determined by the file extension.The `edfReader`
#' package is used to load BDF/EDF files, whereas custom code is used for .CNT
#' and BVA files. The function creates an `eeg_data` structure for
#' subsequent use.
#'
#' @author Matt Craddock, \email{matt@@mattcraddock.com}
#' @param file_name File to import. Should include file extension.
#' @param file_path Path to file name, if not included in filename.
#' @param recording Name of the recording. By default, the filename will be
#' used.
#' @param participant_id Identifier for the participant. Defaults to NA.
#' @param fast_bdf New, faster method for loading BDF files. Experimental.
#' @import tools
#' @importFrom purrr map_df is_empty
#' @importFrom tibble tibble as_tibble
#' @examples
#' \dontrun{
#' import_raw("test_bdf.bdf")
#' }
#' @return An object of class `eeg_data`
#' @export
import_raw <- function(file_name,
file_path = NULL,
recording = NULL,
participant_id = NA,
fast_bdf = TRUE) {
file_type <- tools::file_ext(file_name)
if (!is.null(file_path)) {
file_name <- file.path(file_path, file_name)
}
if (is.null(recording)) {
recording <- basename(tools::file_path_sans_ext(file_name))
}
if (file_type %in% c("bdf","edf")) {
message(paste("Importing",
file_name,
"as",
toupper(file_type)))
if (fast_bdf && file_type == "bdf") {
bdf_header <- read_bdf_header(file_name)
sigs <- read_bdf_data(file_name, bdf_header)
colnames(sigs) <- bdf_header$chan_labels
sigs <- tibble::as_tibble(sigs)
srate <- bdf_header$srate[[1]]
} else {
if (!requireNamespace("edfReader", quietly = TRUE)) {
stop("Package \"edfReader\" needed. Please install it.",
call. = FALSE)
}
data <- edfReader::readEdfSignals(edfReader::readEdfHeader(file_name))
#check for an annotations channel
anno_chan <- which(vapply(data,
function(x) isTRUE(x$isAnnotation),
FUN.VALUE = logical(1)))
#remove annotations if present - could put in separate list...
if (length(anno_chan) > 0) {
data <- data[-anno_chan]
message("Annotations are currently discarded. File an issue on Github if you'd like
this to change.")
}
sigs <- purrr::map_df(data, "signal")
srate <- data[[1]]$sRate
}
# Biosemi triggers should be in the range 0-255, but sometimes are read from
# the wrong "end"
if ("Status" %in% names(sigs)) {
events <- sigs$Status %% (256)
} else {
events <- integer(0)
}
if (purrr::is_empty(events)) {
warning("No status channel. Retaining all channels.")
chan_nos <- 1:ncol(sigs)
} else {
#all chans except Status
chan_nos <- (1:ncol(sigs))[-which(names(sigs) == "Status")]
}
timings <- tibble::tibble(sample = 1:nrow(sigs))
timings$time <- (timings$sample - 1) / srate
if (is.null(chan_nos)) {
chan_nos <- 1:(ncol(sigs) - 1)
}
sigs <- tibble::as_tibble(sigs[, chan_nos])
# The events are often recorded over a number of samples, but should
# typically be point events; this finds the time at which the events start
events_diff <- diff(events)
event_table <-
tibble::tibble(event_onset = which(events_diff > 0) + 1,
event_time = which(events_diff > 0) / srate,
event_type = events[which(events_diff > 0) + 1])
epochs <- tibble::new_tibble(list(epoch = 1,
participant_id = participant_id,
recording = recording),
nrow = 1,
class = "epoch_info")
data <- eeg_data(data = sigs,
srate = srate,
events = event_table,
timings = timings,
epochs = epochs)
data
} else if (identical(file_type,"cnt")) {
message(paste("Importing Neuroscan", toupper(file_type), file_name))
message(paste("Note: if this is 16-bit or an ANT Neuro .CNT file, reading will fail."))
data <- import_cnt(file_name)
sigs <- tibble::as_tibble(t(data$chan_data))
names(sigs) <- data$chan_info$electrode
srate <- data$head_info$samp_rate
timings <- tibble::tibble(sample = 1:dim(sigs)[[1]])
timings$time <- (timings$sample - 1) / srate
event_table <-
tibble::tibble(event_onset = data$event_list$offset + 1,
event_time = (data$event_list$offset + 1) / srate,
event_type = data$event_list$event_type)
epochs <- tibble::new_tibble(list(epoch = 1,
participant_id = participant_id,
recording = recording),
nrow = 1,
class = "epoch_info")
data <- eeg_data(data = sigs,
srate = srate,
chan_info = validate_channels(data$chan_info),
events = event_table,
timings = timings,
epochs = epochs)
} else if (identical(file_type, "vhdr")) {
if (!requireNamespace("ini", quietly = TRUE)) {
stop("Package \"ini\" needed to read BVA files. Please install it.",
call. = FALSE)
}
message(paste("Importing Brain Vision Analyzer file", file_name))
data <- import_vhdr(file_name,
recording = recording,
participant_id = participant_id)
} else {
stop("Unsupported filetype")
}
data
}
#' Import Neuroscan .CNT file
#'
#' Beta version of function to import Neuroscan .CNT files. Only intended for
#' import of 32-bit files.
#'
#' @param file_name Name of .CNT file to be loaded.
#' @importFrom tibble tibble
#' @keywords internal
import_cnt <- function(file_name) {
cnt_file <- file(file_name, "rb")
# Read in meta-info - number of channels, location of event table, sampling
# rate...
pos <- seek(cnt_file, 12)
next_file <- readBin(cnt_file,
integer(),
size = 4,
n = 1,
endian = "little")
pos <- seek(cnt_file, 353)
n_events <- readBin(cnt_file,
integer(),
n = 1,
endian = "little")
pos <- seek(cnt_file, 370)
n_channels <- readBin(cnt_file,
integer(),
n = 1,
size = 2,
signed = FALSE,
endian = "little")
pos <- seek(cnt_file, 376)
samp_rate <- readBin(cnt_file,
integer(),
n = 1,
size = 2,
signed = FALSE,
endian = "little")
pos <- seek(cnt_file, 864)
n_samples <- readBin(cnt_file,
integer(),
size = 4,
n = 1,
endian = "little")
pos <- seek(cnt_file, 886)
event_table_pos <- readBin(cnt_file,
integer(),
size = 4,
n = 1,
endian = "little") # event table
pos <- seek(cnt_file, 900)
data_info <- data.frame(n_events,
n_channels,
samp_rate,
event_table_pos)
chan_df <- data.frame(electrode = character(n_channels),
chan_no = numeric(n_channels),
x = numeric(n_channels),
y = numeric(n_channels),
baseline = numeric(n_channels),
sens = numeric(n_channels),
cal = numeric(n_channels),
stringsAsFactors = FALSE)
# Read channel names and locations
for (i in 1:n_channels) {
chan_start <- seek(cnt_file)
chan_df$electrode[i] <- readBin(cnt_file, character(),
n = 1, endian = "little")
chan_df$chan_no[i] <- i
pos <- seek(cnt_file, chan_start + 19)
chan_df$x[i] <- readBin(cnt_file,
double(),
size = 4,
n = 1, endian = "little") # x coord
chan_df$y[i] <- readBin(cnt_file,
double(),
size = 4,
n = 1, endian = "little") # y coord
pos <- seek(cnt_file, chan_start + 47)
chan_df$baseline[i] <- readBin(cnt_file,
integer(),
size = 1,
n = 1,
endian = "little")
pos <- seek(cnt_file, chan_start + 59)
chan_df$sens[i] <- readBin(cnt_file,
double(),
size = 4, n = 1,
endian = "little")
pos <- seek(cnt_file, chan_start + 71)
chan_df$cal[i] <- readBin(cnt_file,
double(),
size = 4,
n = 1,
endian = "little")
pos <- seek(cnt_file, (900 + i * 75))
}
beg_data <- seek(cnt_file) # beginning of actual data
real_n_samples <- event_table_pos - (900 + 75 * n_channels) / (2 * n_channels)
frames <- floor((event_table_pos - beg_data) / n_channels / 4)
chan_data <- matrix(readBin(cnt_file,
integer(),
size = 4,
n = n_channels * frames,
endian = "little"),
nrow = n_channels, ncol = frames)
# rescale chan_data to microvolts
mf <- chan_df$sens * (chan_df$cal / 204.8)
chan_data <- (chan_data - chan_df$baseline) * mf
# Read event table
pos <- seek(cnt_file,
event_table_pos)
teeg <- readBin(cnt_file,
integer(),
size = 1,
n = 1,
endian = "little")
tsize <- readBin(cnt_file,
integer(),
n = 1,
endian = "little")
toffset <- readBin(cnt_file,
integer(),
n = 1,
endian = "little")
ev_table_start <- seek(cnt_file)
ev_list <- data.frame(event_type = integer(n_events),
keyboard = character(n_events),
keypad_accept = integer(n_events),
accept_evl = integer(n_events),
offset = integer(n_events),
type = integer(n_events),
code = integer(n_events),
latency = numeric(n_events),
epochevent = integer(n_events),
accept = integer(n_events),
accuracy = integer(n_events),
stringsAsFactors = FALSE
)
for (i in 1:n_events) {
ev_list$event_type[i] <- readBin(cnt_file,
integer(),
size = 2,
n = 1,
endian = "little")
ev_list$keyboard[i] <- readBin(cnt_file,
integer(),
size = 1,
n = 1,
endian = "little")
temp <- readBin(cnt_file,
integer(),
size = 1,
n = 1,
signed = FALSE,
endian = "little")
ev_list$keypad_accept[i] <- bitwAnd(15, temp)
ev_list$accept_evl[i] <- bitwShiftR(temp, 4)
ev_list$offset[i] <- readBin(cnt_file,
integer(),
size = 4,
n = 1,
endian = "little")
ev_list$type[i] <- readBin(cnt_file,
integer(),
size = 2,
n = 1,
endian = "little")
ev_list$code[i] <- readBin(cnt_file,
integer(),
size = 2,
n = 1,
endian = "little")
ev_list$latency[i] <- readBin(cnt_file,
double(),
size = 4,
n = 1,
endian = "little")
ev_list$epochevent[i] <- readBin(cnt_file,
integer(),
size = 1,
n = 1,
endian = "little")
ev_list$accept[i] <- readBin(cnt_file,
integer(),
size = 1,
n = 1,
endian = "little")
ev_list$accuracy[i] <- readBin(cnt_file,
integer(),
size = 1,
n = 1,
endian = "little")
}
ev_list$offset <- (ev_list$offset - beg_data) / (4 * n_channels) + 1
close(cnt_file)
chan_df <- chan_df[, names(chan_df) %in% c("electrode", "chan_no")]
out <- list(chan_info = chan_df,
head_info = data_info,
chan_data = chan_data,
event_list = ev_list)
}
#' Function for importing Brain Vision Analyzer files
#' @param file_name file name of the header file.
#' @keywords internal
import_vhdr <- function(file_name,
participant_id,
recording) {
.data <- read_vhdr(file_name)
epochs <- tibble::new_tibble(list(epoch = 1,
participant_id = participant_id,
recording = recording),
nrow = 1,
class = "epoch_info")
.data$epochs <- epochs
.data
}
#' @keywords internal
read_vhdr <- function(file_name) {
header_info <- ini::read.ini(file_name)
if (identical(header_info$`Common Infos`$DataFormat, "ASCII")) {
stop("BVA ASCII not currently supported.")
}
if (identical(header_info$`Common Infos`$DataType,
"FREQUENCYDOMAIN")) {
stop("Only time domain data is currently supported,
this data is in the frequency domain.")
}
if (identical(header_info$`Common Infos`$DataOrientation,
"VECTORIZED")) {
multiplexed <- FALSE
} else {
multiplexed <- TRUE
}
data_file <- file.path(dirname(file_name),
header_info$`Common Infos`$DataFile)
vmrk_file <- file.path(dirname(file_name),
header_info$`Common Infos`$MarkerFile)
n_chan <- as.numeric(header_info$`Common Infos`$NumberOfChannels)
# header gives sampling times in microseconds
srate <- 1e6 / as.numeric(header_info$`Common Infos`$SamplingInterval)
bin_format <- header_info$`Binary Infos`$BinaryFormat
chan_labels <- lapply(header_info$`Channel Infos`,
function(x) unlist(strsplit(x = x, split = ",")))
chan_names <- sapply(chan_labels, "[[", 1)
#EEGLAB exported BVA files are missing the channel resolution, which occupies
#the 3rd position - check length of chan_labels elements first to cope with
#that. Probably always 1 microvolt anyway...
if (length(chan_labels[[1]]) == 3) {
chan_scale <- as.numeric(sapply(chan_labels, "[[", 3))
} else {
chan_scale <- rep(1, length(chan_labels))
}
chan_info <- parse_vhdr_chans(chan_names,
header_info$`Coordinates`)
file_size <- file.size(data_file)
.data <- read_dat(data_file,
file_size = file_size,
bin_format = bin_format,
multiplexed)
.data <- matrix(.data,
ncol = n_chan,
byrow = multiplexed)
if (any(!is.na(chan_scale))) {
.data <- sweep(.data,
2,
chan_scale,
"*")
}
colnames(.data) <- chan_names
.data <- tibble::as_tibble(.data)
n_points <- nrow(.data)
timings <- tibble::tibble(sample = 1:n_points,
time = (sample - 1) / srate)
.markers <- read_vmrk(vmrk_file)
.markers <-
dplyr::mutate(.markers,
event_onset = as.numeric(event_onset),
length = as.numeric(length),
.electrode = ifelse(.electrode == 0,
NA,
as.character(chan_info$electrode[.electrode])),
event_time = (event_onset - 1) / srate)
.data <- eeg_data(data = .data,
srate = srate,
events = .markers,
chan_info = chan_info,
timings = timings,
reference = NULL)
.data
}
#' Read the raw data for a BVA file.
#'
#' @param file_name Filename of the .dat file
#' @param file_size Size of the .dat file
#' @param bin_format Storage format.
#' @param multiplexed Logical; whether the data is VECTORIZED (FALSE) or MULTIPLEXED (TRUE)
#' @keywords internal
read_dat <- function(file_name,
file_size,
bin_format,
multiplexed) {
if (identical(bin_format, "IEEE_FLOAT_32")) {
nbytes <- 4
signed <- TRUE
file_type <- "double"
} else if (identical(bin_format, "UINT_16")) {
nbytes <- 2
signed <- FALSE
file_type <- "int"
} else {
nbytes <- 2
signed <- TRUE
file_type <- "int"
}
raw_data <- readBin(file_name,
what = file_type,
n = file_size,
size = nbytes,
signed = signed)
raw_data
}
#' Read BVA markers
#'
#' Import and parse BVA marker files.
#'
#' @param file_name File name of the .vmrk markers file.
#' @keywords internal
read_vmrk <- function(file_name) {
vmrks <- ini::read.ini(file_name)
marker_id <- names(vmrks$`Marker Infos`)
markers <- lapply(vmrks$`Marker Infos`,
function(x) unlist(strsplit(x, ",")))
# to do - fix to check for "New segment" instead - it's the extra date field that causes issues
if (length(markers[[1]]) == 6) {
date <- markers[[1]][[6]]
markers <- lapply(markers, `[`, 1:5)
} else {
date <- NA
}
markers <- do.call(rbind, markers)
colnames(markers) <- c("BVA_type",
"event_type",
"event_onset",
"length",
".electrode")
markers <- tibble::as_tibble(markers)
markers <-
dplyr::mutate(markers,
event_onset = as.numeric(event_onset),
length = as.numeric(length),
.electrode = as.numeric(.electrode))
markers
}
#' Load `EEGLAB` .set files
#'
#' Load `EEGLAB` .set files and convert them to `eeg_epochs` objects. Supports
#' import of files saved in either Matlab v6.5 or Matlab v7.3 formats. Currently,
#' any ICA weights or decompositions are discarded.
#'
#' @param file_name Filename (and path if not in present working directory)
#' @param df_out Defaults to FALSE - outputs an object of class `eeg_epochs`. Set
#' to TRUE for a normal data frame.
#' @param participant_id Character vector. By default, the filename will be used as the id of the
#' participant.
#' @param recording Character vector. By default, the filename will be used as the name of the
#' recording.
#' @param drop_custom Drop custom event fields. TRUE by default.
#' @param verbose Print informative messages. TRUE by default.
#' @author Matt Craddock \email{matt@@mattcraddock.com}
#' @importFrom dplyr group_by mutate rename
#' @importFrom tibble tibble as_tibble
#' @importFrom purrr is_empty map_df
#' @examples
#' \dontrun{import_set("your_data.set")}
#' @return An object of class `eeg_epochs`
#' @export
import_set <- function(file_name,
df_out = FALSE,
participant_id = NULL,
recording = NULL,
drop_custom = FALSE,
verbose = TRUE) {
checkpkgs <-
unlist(
lapply(c("R.matlab", "hdf5r"),
requireNamespace,
quietly = TRUE)
)
if (!all(checkpkgs)) {
missing_pkg <- c("R.matlab", "hdf5r")[!checkpkgs]
if (length(missing_pkg) == 1) {
stop(paste("Package",
missing_pkg,
"needed. Please install it."),
call. = FALSE)
} else {
stop(
paste("Packages",
missing_pkg[[1]],
"&",
missing_pkg[[2]],
"needed. Please install them."
),
call. = FALSE
)
}
}
if (verbose) {
message("Importing from EEGLAB .set file.")
}
if (is.null(recording)) {
recording <- basename(tools::file_path_sans_ext(file_name))
}
if (is.null(participant_id)) {
participant_id <- basename(tools::file_path_sans_ext(file_name))
}
check_hdf5 <- hdf5r::is.h5file(file_name)
if (check_hdf5) {
if (verbose) {
message("Matlab 7.3 file format detected.")
}
return(
read_hdf5_set(file_name,
recording = recording,
participant_id = participant_id)
)
}
temp_dat <- R.matlab::readMat(file_name)
if (identical(names(temp_dat)[[1]], "EEG")) {
temp_dat <- temp_dat$EEG[, 1, 1]
}
n_chans <- temp_dat[["nbchan"]]
n_trials <- temp_dat[["trials"]]
times <- temp_dat[["times"]]
chan_info <- drop(Reduce(rbind,
temp_dat["chanlocs"]))
# var_names <- dimnames(temp_dat$EEG)[[1]]
#
# n_chans <- temp_dat$EEG[[which(var_names == "nbchan")]]
# n_trials <- temp_dat$EEG[[which(var_names == "trials")]]
# times <- temp_dat$EEG[[which(var_names == "times")]]
# chan_info <- drop(Reduce(rbind, temp_dat$EEG["chanlocs",,]))
pick_empties <-
vapply(
chan_info,
function(x) is.null(x) | length(x) == 0,
FUN.VALUE = logical(1)
)
chan_info[pick_empties] <- NA
chan_info <- lapply(data.frame(t(chan_info)),
unlist) # unlist each data.frame column
chan_info <- data.frame(chan_info) # turn back into data frame
chan_info <- parse_chaninfo(chan_info)
# check if the data is stored in the set or in a separate .fdt
#if (is.character(temp_dat$EEG[[which(var_names == "data")]])) {
if (is.character(temp_dat[["data"]])) {
if (verbose) {
message("Importing data from .fdt file.")
}
fdt_file <- paste0(tools::file_path_sans_ext(file_name),
".fdt")
fdt_file <- file(fdt_file, "rb")
# read in data from .fdt
# do this in chunks to avoid memory errors for large files...?
signals <- readBin(fdt_file,
"double",
n = n_chans * n_trials * length(times),
size = 4,
endian = "little")
close(fdt_file)
dim(signals) <- c(n_chans, length(times) * max(n_trials, 1))
times <- rep(times, max(n_trials, 1))
if (n_trials == 1) {
continuous <- TRUE
} else {
continuous <- FALSE
}
} else {
# if the data is in the .set file, load it here instead of above
#signals <- temp_dat$EEG[[which(dimnames(temp_dat$EEG)[[1]] == "data")]]
signals <- temp_dat[["data"]]
dim_signals <- dim(signals)
if (length(dim_signals) == 3) {
dim(signals) <- c(dim_signals[1], dim_signals[2] * dim_signals[3])
times <- rep(times, n_trials)
continuous <- FALSE
} else {
continuous <- TRUE
}
}
signals <- t(signals)
colnames(signals) <- unique(chan_info$electrode)
signals <- as.data.frame(signals)
signals$time <- as.numeric(times)
#srate <- temp_dat$EEG[[which(var_names == "srate")]][[1]]
srate <- temp_dat[["srate"]][[1]]
if (!continuous) {
signals <- dplyr::group_by(signals,
time)
signals <- dplyr::mutate(signals,
epoch = 1:dplyr::n())
signals <- dplyr::ungroup(signals)
}
#event_info <- temp_dat$EEG[[which(var_names == "event")]]
event_info <- temp_dat[["event"]]
event_table <- event_info
dim(event_table) <- c(dim(event_info)[[1]],
dim(event_info)[[3]])
event_table <- t(event_table)
colnames(event_table) <- dimnames(event_info)[[1]]
event_table <- tibble::as_tibble(event_table)
event_table <-
purrr::map_df(event_table,
~unlist(
purrr::map(.,
~ifelse(is.null(.),
NA,
.)))
)
event_table <- lapply(event_table,
unlist)
empty_entries <- unlist(lapply(event_table,
rlang::is_empty))
if (any(empty_entries)) {
empty_cols <- names(event_table)[empty_entries]
message(paste0("Removing empty event table column (s):",
empty_cols))
event_table <- event_table[!empty_entries]
}
event_table <- tibble::as_tibble(event_table)
# EEGLAB stores latencies in samples and allows non-integer samples (e.g.
# through downsampling, or more rapidly sampled events than EEG signal)
#
if (any(event_table$latency %% 1 > 0)) {
message("Rounding non-integer event sample latencies...")
event_table$latency <- round(event_table$latency)
# This can result in an event with a latency of zero in samples, which
# causes problems with subsequent import steps - fix that and turn it into
# sample 1
event_table$latency <- ifelse(event_table$latency == 0,
1,
event_table$latency)
}
# EEGLAB stores latencies in samples starting from 1, my event_time is in
# seconds, starting from 0
event_table$event_time <- (event_table$latency - 1) / srate
std_cols <- c("latency",
"event_time",
"type",
"epoch")
if (drop_custom & any(!colnames(event_table) %in% std_cols)) {
message("Dropping custom columns...")
event_table <- event_table[, std_cols]
}
col_check <- colnames(event_table) %in% c("event_type", "event_onset")
if (any(col_check)) {
dupe_checks <- colnames(event_table)[col_check]
dupe_labs <- paste0(dupe_checks, ".x")
}
#need to build in check for duplicate columns
event_table <- dplyr::rename(event_table,
event_type = "type",
event_onset = "latency")
if (df_out) {
return(signals)
} else {
signals$time <- signals$time / 1000
# convert to seconds - eeglab uses milliseconds
if (continuous) {
timings <- tibble::tibble(time = signals$time, #epoch = signal,
sample = 1:length(signals$time))
n_epochs <- 1
} else {
timings <- tibble::tibble(time = signals$time,
epoch = signals$epoch,
sample = 1:length(signals$time))
event_table$time <- NA
event_table$time <- timings[which(timings$sample %in% event_table$event_onset,
arr.ind = TRUE), ]$time
n_epochs <- length(unique(timings$epoch))
}
epochs <-
tibble::new_tibble(list(epoch = 1:n_epochs,
participant_id = rep(participant_id, n_epochs),
recording = rep(recording, n_epochs)),
nrow = n_epochs,
class = "epoch_info")
if (continuous) {
out_data <- eeg_data(signals[, 1:n_chans],
srate = srate,
timings = timings,
chan_info = chan_info,
events = event_table,
epochs = epochs)
} else {
out_data <- eeg_epochs(signals[, 1:n_chans],
srate = srate,
timings = timings,
chan_info = chan_info,
events = event_table,
reference = NULL,
epochs = epochs)
}
out_data
}
}
#' Parse channel info from an EEGLAB set file
#'
#' Internal function to convert EEGLAB chan_info to eegUtils style
#'
#' @param chan_info Channel info list from an EEGLAB set file
#' @param drop If there are additional columns, remove all columns except
#' electrode if TRUE, or just unexpected columns if FALSE.
#' @keywords internal
parse_chaninfo <- function(chan_info,
drop = FALSE) {
# chan_info <- chan_info[sort(names(chan_info),
# method = "shell")]
expected <- c("labels", "radius",
"ref", "sph.phi",
"sph.radius", "sph.theta",
"theta", "type",
"urchan", "X",
"Y", "Z")
# Check for two things:
# 1) Missing expected columns.
# 2) Columns which are non-standard.
if (!all(names(chan_info) %in% expected)) {
if (drop) {
warning("EEGLAB chan info has unexpected format, taking electrode names only.")
out <- data.frame(chan_info["labels"])
names(out) <- "electrode"
return(validate_channels(out))
} else {
warning("EEGLAB chan info has unexpected format, taking only expected columns.")
miss_cols <- expected[!(expected %in% names(chan_info))]
#Why did I set this to NA? must have been a reason but it has unintended
#consequences. Remember to build a test when I end up back here with an
#example...
chan_info[miss_cols] <- NA
}
}
if (!all(expected %in% names(chan_info))) {
miss_cols <- expected[!(expected %in% names(chan_info))]
chan_info[miss_cols] <- NA
}
chan_info <- chan_info[expected]
names(chan_info) <- c("electrode",
"radius",
"ref",
"sph_phi",
"sph_radius",
"sph_theta",
"angle",
"type",
"urchan",
"cart_x",
"cart_y",
"cart_z"
)
chan_info <- chan_info[c("electrode",
"cart_x",
"cart_y",
"cart_z")]
# EEGLAB co-ordinates are rotated 90 degrees compared to our coordinates,
# and left-right flipped
names(chan_info) <- names(chan_info)[c(1, 3, 2, 4)]
chan_info <- chan_info[, c(1, 3, 2, 4)]
chan_info$cart_x <- -chan_info$cart_x
sph_coords <- cart_to_spherical(chan_info[, c("cart_x", "cart_y", "cart_z")])
xy <- project_elecs(sph_coords)
chan_info <- dplyr::bind_cols(electrode = as.character(chan_info$electrode),
sph_coords,
chan_info[, 2:4],
xy)
chan_info
}
#' Parse BVA channel info
#'
#' @param chan_labels The `Channel Infos` section from a BVA vhdr file
#' @param chan_info The `Coordinates` section from a BVA vhdr file
#' @keywords internal
parse_vhdr_chans <- function(chan_labels,
chan_info,
verbose = TRUE) {
init_chans <- data.frame(electrode = chan_labels)
if (is.null(chan_info)) {
init_chans$radius <- NA
init_chans$theta <- NA
init_chans$phi <- NA
if (verbose) message("No channel locations found.")
return(validate_channels(init_chans))
} else {
coords <- lapply(chan_info,
function(x) as.numeric(unlist(strsplit(x, split = ","))))
new_coords <- data.frame(do.call(rbind,
coords))
names(new_coords) <- c("radius", "theta", "phi")
new_coords <- cbind(init_chans, new_coords)
new_coords[new_coords$radius %in% c(0, NaN), 2:4] <- NA
chan_info <- bva_elecs(new_coords)
tibble::as_tibble(chan_info)
}
}
#' Convert BVA spherical locations
#'
#' Reads the BrainVoyager spherical electrode locations and converts them to
#' Cartesian 3D and 2D projections.
#'
#' @param chan_info A data.frame containing electrodes
#' @param radius Head radius in millimetres
#' @keywords internal
bva_elecs <- function(chan_info, radius = 85) {
chan_info <-
dplyr::mutate(chan_info,
cart_x = sin(deg2rad(theta)) * cos(deg2rad(phi)),
cart_y = sin(deg2rad(theta)) * sin(deg2rad(phi)),
cart_z = cos(deg2rad(theta)),
x = theta * cos(deg2rad(phi)),
y = theta * sin(deg2rad(phi)))
chan_info[, c("cart_x", "cart_y", "cart_z")] <-
chan_info[, c("cart_x", "cart_y", "cart_z")] * radius
chan_info
}
#' Import Fieldtrip files
#'
#' Fieldtrip is a Matlab package for EEG/MEG processing and analysis.
#'
#' @author Matt Craddock \email{matt@@mattcraddock.com}
#' @param file_name Name of file to be imported.
#' @param recording Name of the recording. By default, the filename will be
#' used.
#' @param participant_id Identifier for the participant.
#' @param verbose Informative messages printed to console. Defaults to TRUE.
#' @examples
#' \dontrun{import_ft("fieldtrip_test.mat")}
#' @return An object of class `eeg_data`, `eeg_epochs`, or
#' `eeg_tfr`, depending on the type of input data.
#' @export
import_ft <- function(file_name,
participant_id = NULL,
recording = NULL,
verbose = TRUE) {
if (!requireNamespace("R.matlab", quietly = TRUE)) {
stop("Package \"R.matlab\" needed. Please install it.",
call. = FALSE)
}
tmp_ft <- R.matlab::readMat(file_name)
tmp_ft <- tmp_ft[[1]]
struct_names <- rownames(tmp_ft)
if ("pos" %in% struct_names) {
stop("Import of source data is not currently supported.")
}
if (is.null(participant_id)) {
participant_id <- basename(tools::file_path_sans_ext(file_name))
}
if (is.null(recording)) {
recording <- basename(tools::file_path_sans_ext(file_name))
}
if ("dimord" %in% struct_names) {
dimensions <- proc_dimord(unlist(tmp_ft["dimord", ,],
use.names = FALSE))
if ("frequency" %in% dimensions) {
return(ft_freq(tmp_ft,
dimensions,
struct_names = struct_names,
participant_id = participant_id,
recording = recording,
verbose = verbose))
}
} else {
# work out if the file stores components (from ICA or PCA) or "raw" data
if ("unmixing" %in% struct_names) {
return(ft_comp(tmp_ft))
} else {
return(ft_raw(tmp_ft,
struct_names = struct_names,
participant_id = participant_id,
recording = recording,
verbose = verbose))
}
}
}
read_bdf_header <- function(file_name) {
file_read <- file(file_name, "rb")
pos <- seek(file_read, 1)
sys_type <- readChar(file_read, 7)
if (sys_type == "BIOSEMI") {
samp_bits <- 24
} else {
samp_bits <- 16
}
patient_id <- readChar(file_read, 80)
recording_id <- readChar(file_read, 80)
recording_date <- readChar(file_read, 8)
recording_time <- readChar(file_read, 8)
n_bytes <- readChar(file_read, 8)
format_version <- readChar(file_read, 44)
n_records <- readChar(file_read, 8)
dur_records <- readChar(file_read, 8)
n_chan <- as.integer(readChar(file_read, 4))
chan_labels <- readChar(file_read, n_chan * 16)
chan_labels <- unlist(strsplit(chan_labels, " "))
chan_labels <- chan_labels[chan_labels != ""]
if (length(chan_labels) != n_chan) {
stop("Chan_label import went wrong!")
}
chan_types <- readChar(file_read, n_chan * 80)
chan_types <- unlist(strsplit(chan_types, " "))
chan_types <- chan_types[chan_types != ""]
chan_units <- readChar(file_read, n_chan * 8)
chan_units <- unlist(strsplit(chan_units, " "))
chan_units <- chan_units[chan_units != ""]
phys_mins <- readChar(file_read, n_chan * 8)
phys_mins <- as.integer(unlist(strsplit(phys_mins, " ")))
phys_max <- readChar(file_read, n_chan * 8)
phys_max <- as.integer(unlist(strsplit(phys_max, " ")))
dig_mins <- readChar(file_read, n_chan * 8)
dig_mins <- unlist(strsplit(gsub("-", " -", dig_mins), " "))
dig_mins <- as.integer(dig_mins)
dig_mins <- dig_mins[!is.na(dig_mins)]
dig_max <- readChar(file_read, n_chan * 8)
dig_max <- as.integer(unlist(strsplit(gsub("-", " -", dig_max), " ")))
prefilt <- readChar(file_read, n_chan * 80)
prefilt <- remove_empties(prefilt, " ")
n_samp_rec <- readChar(file_read, n_chan * 8) # sampling rate
n_samp_rec <- remove_empties(n_samp_rec)
reserved <- readChar(file_read, n_chan * 32)
close(file_read)
list(sys_type = sys_type,
samp_bits = samp_bits,
patient_id = patient_id,
recording_id = recording_id,
recording_time = recording_time,
n_chans = n_chan,
n_records = as.integer(n_records),
record_dur = as.integer(dur_records),
chan_labels = chan_labels,
chan_types = chan_types,
chan_units = chan_units,
phys_mins = phys_mins,
phys_max = phys_max,
dig_mins = dig_mins,
dig_max = dig_max,
prefiltering = prefilt,
srate = as.integer(n_samp_rec))
}
remove_empties <- function(strings, char_match = " ") {
strings <- unlist(strsplit(strings, char_match))
strings[strings != ""]
}
read_bdf_data <- function(file_name, headers) {
sig_file <- file(file_name,
"rb")
#skip headers
start_pos <- (headers$n_chans + 1) * 256
pos <- seek(sig_file,
start_pos)
bytes_per_rec <- headers$samp_bits / 8
n_chans <- headers$n_chans
sig_length <- headers$srate[[1]] * bytes_per_rec * n_chans # length of one record in bytes
rec_length <- headers$srate[[1]]
rec_size <- sig_length / 256 # calc size in kilobytes in memory
records_per <- min(floor(20000 / rec_size), headers$n_records) # read up to 20000 kb at once
gains <- (headers$phys_max - headers$phys_min) / (headers$dig_max - headers$dig_mins)
offsets <- headers$phys_mins - gains * headers$dig_mins
sig_out <- matrix(0,
nrow = headers$srate[[1]] * headers$n_records,
ncol = n_chans)
chunk_size <- rec_length * records_per
n_chunks <- floor(headers$n_records / records_per)
remaining <- headers$n_records %% records_per
first_record <- integer(sig_length * records_per)
shifted <- integer(sig_length * records_per)
for (records in 1:n_chunks) {
first_record <- readBin(sig_file,
"raw",
n = sig_length * records_per,
endian = "little")
shifted <- as.integer(first_record) * as.integer(2^c(0, 8, 16))
shifted <- array(shifted,
dim = c(3,
headers$srate[[1]],
n_chans,
records_per))
shifted <- colSums(shifted)
shifted <- matrix(aperm(shifted,
c(1, 3, 2)),
nrow = prod(dim(shifted)[c(1, 3)]))
modifier <- chunk_size * (records - 1)
sig_out[(1:chunk_size) + modifier, ] <- shifted
}
if (remaining > 0) {
final_records <- readBin(sig_file,
"raw",
n = sig_length * remaining,
endian = "little")
shifted <- as.integer(final_records) * as.integer(2^c(0, 8, 16))
shifted <- array(shifted, dim = c(3,
headers$srate[[1]],
n_chans,
remaining))
shifted <- colSums(shifted)
modifier <- nrow(sig_out) - remaining * headers$srate[[1]] + 1
sig_out[modifier:nrow(sig_out), ] <- matrix(aperm(shifted,
c(1, 3, 2)),
nrow = prod(dim(shifted)[c(1, 3)]))
}
# Correct for sign
sig_out <- sig_out - bitwShiftL(bitwShiftR(sig_out,
23),
24)
sig_out[, 1:(n_chans - 1)] <- sig_out[, 1:(n_chans - 1)] * gains[1] + offsets[1]
close(sig_file)
sig_out
}
craddm/eegUtils documentation built on March 24, 2022, 9:17 a.m.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions import_set read_vmrk read_dat read_vhdr import_vhdr import_cnt import_raw
Documented in import_cnt import_raw import_set import_vhdr read_dat read_vmrk
#' Function for reading raw data.
#'
#' Currently BDF/EDF, 32-bit .CNT, and Brain Vision Analyzer files are
#' supported. Filetype is determined by the file extension.The `edfReader`
#' package is used to load BDF/EDF files, whereas custom code is used for .CNT
#' and BVA files. The function creates an `eeg_data` structure for
#' subsequent use.
#'
#' @author Matt Craddock, \email{matt@@mattcraddock.com}
#' @param file_name File to import. Should include file extension.
#' @param file_path Path to file name, if not included in filename.
#' @param recording Name of the recording. By default, the filename will be
#' used.
#' @param participant_id Identifier for the participant. Defaults to NA.
#' @param fast_bdf New, faster method for loading BDF files. Experimental.
#' @import tools
#' @importFrom purrr map_df is_empty
#' @importFrom tibble tibble as_tibble
#' @examples
#' \dontrun{
#' import_raw("test_bdf.bdf")
#' }
#' @return An object of class `eeg_data`
#' @export
import_raw <- function(file_name,
file_path = NULL,
recording = NULL,
participant_id = NA,
fast_bdf = TRUE) {
file_type <- tools::file_ext(file_name)
if (!is.null(file_path)) {
file_name <- file.path(file_path, file_name)
}
if (is.null(recording)) {
recording <- basename(tools::file_path_sans_ext(file_name))
}
if (file_type %in% c("bdf","edf")) {
message(paste("Importing",
file_name,
"as",
toupper(file_type)))
if (fast_bdf && file_type == "bdf") {
bdf_header <- read_bdf_header(file_name)
sigs <- read_bdf_data(file_name, bdf_header)
colnames(sigs) <- bdf_header$chan_labels
sigs <- tibble::as_tibble(sigs)
srate <- bdf_header$srate[[1]]
} else {
if (!requireNamespace("edfReader", quietly = TRUE)) {
stop("Package \"edfReader\" needed. Please install it.",
call. = FALSE)
}
data <- edfReader::readEdfSignals(edfReader::readEdfHeader(file_name))
#check for an annotations channel
anno_chan <- which(vapply(data,
function(x) isTRUE(x$isAnnotation),
FUN.VALUE = logical(1)))
#remove annotations if present - could put in separate list...
if (length(anno_chan) > 0) {
data <- data[-anno_chan]
message("Annotations are currently discarded. File an issue on Github if you'd like
this to change.")
}
sigs <- purrr::map_df(data, "signal")
srate <- data[[1]]$sRate
}
# Biosemi triggers should be in the range 0-255, but sometimes are read from
# the wrong "end"
if ("Status" %in% names(sigs)) {
events <- sigs$Status %% (256)
} else {
events <- integer(0)
}
if (purrr::is_empty(events)) {
warning("No status channel. Retaining all channels.")
chan_nos <- 1:ncol(sigs)
} else {
#all chans except Status
chan_nos <- (1:ncol(sigs))[-which(names(sigs) == "Status")]
}
timings <- tibble::tibble(sample = 1:nrow(sigs))
timings$time <- (timings$sample - 1) / srate
if (is.null(chan_nos)) {
chan_nos <- 1:(ncol(sigs) - 1)
}
sigs <- tibble::as_tibble(sigs[, chan_nos])
# The events are often recorded over a number of samples, but should
# typically be point events; this finds the time at which the events start
events_diff <- diff(events)
event_table <-
tibble::tibble(event_onset = which(events_diff > 0) + 1,
event_time = which(events_diff > 0) / srate,
event_type = events[which(events_diff > 0) + 1])
epochs <- tibble::new_tibble(list(epoch = 1,
participant_id = participant_id,
recording = recording),
nrow = 1,
class = "epoch_info")
data <- eeg_data(data = sigs,
srate = srate,
events = event_table,
timings = timings,
epochs = epochs)
data
} else if (identical(file_type,"cnt")) {
message(paste("Importing Neuroscan", toupper(file_type), file_name))
message(paste("Note: if this is 16-bit or an ANT Neuro .CNT file, reading will fail."))
data <- import_cnt(file_name)
sigs <- tibble::as_tibble(t(data$chan_data))
names(sigs) <- data$chan_info$electrode
srate <- data$head_info$samp_rate
timings <- tibble::tibble(sample = 1:dim(sigs)[[1]])
timings$time <- (timings$sample - 1) / srate
event_table <-
tibble::tibble(event_onset = data$event_list$offset + 1,
event_time = (data$event_list$offset + 1) / srate,
event_type = data$event_list$event_type)
epochs <- tibble::new_tibble(list(epoch = 1,
participant_id = participant_id,
recording = recording),
nrow = 1,
class = "epoch_info")
data <- eeg_data(data = sigs,
srate = srate,
chan_info = validate_channels(data$chan_info),
events = event_table,
timings = timings,
epochs = epochs)
} else if (identical(file_type, "vhdr")) {
if (!requireNamespace("ini", quietly = TRUE)) {
stop("Package \"ini\" needed to read BVA files. Please install it.",
call. = FALSE)
}
message(paste("Importing Brain Vision Analyzer file", file_name))
data <- import_vhdr(file_name,
recording = recording,
participant_id = participant_id)
} else {
stop("Unsupported filetype")
}
data
}
#' Import Neuroscan .CNT file
#'
#' Beta version of function to import Neuroscan .CNT files. Only intended for
#' import of 32-bit files.
#'
#' @param file_name Name of .CNT file to be loaded.
#' @importFrom tibble tibble
#' @keywords internal
import_cnt <- function(file_name) {
cnt_file <- file(file_name, "rb")
# Read in meta-info - number of channels, location of event table, sampling
# rate...
pos <- seek(cnt_file, 12)
next_file <- readBin(cnt_file,
integer(),
size = 4,
n = 1,
endian = "little")
pos <- seek(cnt_file, 353)
n_events <- readBin(cnt_file,
integer(),
n = 1,
endian = "little")
pos <- seek(cnt_file, 370)
n_channels <- readBin(cnt_file,
integer(),
n = 1,
size = 2,
signed = FALSE,
endian = "little")
pos <- seek(cnt_file, 376)
samp_rate <- readBin(cnt_file,
integer(),
n = 1,
size = 2,
signed = FALSE,
endian = "little")
pos <- seek(cnt_file, 864)
n_samples <- readBin(cnt_file,
integer(),
size = 4,
n = 1,
endian = "little")
pos <- seek(cnt_file, 886)
event_table_pos <- readBin(cnt_file,
integer(),
size = 4,
n = 1,
endian = "little") # event table
pos <- seek(cnt_file, 900)
data_info <- data.frame(n_events,
n_channels,
samp_rate,
event_table_pos)
chan_df <- data.frame(electrode = character(n_channels),
chan_no = numeric(n_channels),
x = numeric(n_channels),
y = numeric(n_channels),
baseline = numeric(n_channels),
sens = numeric(n_channels),
cal = numeric(n_channels),
stringsAsFactors = FALSE)
# Read channel names and locations
for (i in 1:n_channels) {
chan_start <- seek(cnt_file)
chan_df$electrode[i] <- readBin(cnt_file, character(),
n = 1, endian = "little")
chan_df$chan_no[i] <- i
pos <- seek(cnt_file, chan_start + 19)
chan_df$x[i] <- readBin(cnt_file,
double(),
size = 4,
n = 1, endian = "little") # x coord
chan_df$y[i] <- readBin(cnt_file,
double(),
size = 4,
n = 1, endian = "little") # y coord
pos <- seek(cnt_file, chan_start + 47)
chan_df$baseline[i] <- readBin(cnt_file,
integer(),
size = 1,
n = 1,
endian = "little")
pos <- seek(cnt_file, chan_start + 59)
chan_df$sens[i] <- readBin(cnt_file,
double(),
size = 4, n = 1,
endian = "little")
pos <- seek(cnt_file, chan_start + 71)
chan_df$cal[i] <- readBin(cnt_file,
double(),
size = 4,
n = 1,
endian = "little")
pos <- seek(cnt_file, (900 + i * 75))
}
beg_data <- seek(cnt_file) # beginning of actual data
real_n_samples <- event_table_pos - (900 + 75 * n_channels) / (2 * n_channels)
frames <- floor((event_table_pos - beg_data) / n_channels / 4)
chan_data <- matrix(readBin(cnt_file,
integer(),
size = 4,
n = n_channels * frames,
endian = "little"),
nrow = n_channels, ncol = frames)
# rescale chan_data to microvolts
mf <- chan_df$sens * (chan_df$cal / 204.8)
chan_data <- (chan_data - chan_df$baseline) * mf
# Read event table
pos <- seek(cnt_file,
event_table_pos)
teeg <- readBin(cnt_file,
integer(),
size = 1,
n = 1,
endian = "little")
tsize <- readBin(cnt_file,
integer(),
n = 1,
endian = "little")
toffset <- readBin(cnt_file,
integer(),
n = 1,
endian = "little")
ev_table_start <- seek(cnt_file)
ev_list <- data.frame(event_type = integer(n_events),
keyboard = character(n_events),
keypad_accept = integer(n_events),
accept_evl = integer(n_events),
offset = integer(n_events),
type = integer(n_events),
code = integer(n_events),
latency = numeric(n_events),
epochevent = integer(n_events),
accept = integer(n_events),
accuracy = integer(n_events),
stringsAsFactors = FALSE
)
for (i in 1:n_events) {
ev_list$event_type[i] <- readBin(cnt_file,
integer(),
size = 2,
n = 1,
endian = "little")
ev_list$keyboard[i] <- readBin(cnt_file,
integer(),
size = 1,
n = 1,
endian = "little")
temp <- readBin(cnt_file,
integer(),
size = 1,
n = 1,
signed = FALSE,
endian = "little")
ev_list$keypad_accept[i] <- bitwAnd(15, temp)
ev_list$accept_evl[i] <- bitwShiftR(temp, 4)
ev_list$offset[i] <- readBin(cnt_file,
integer(),
size = 4,
n = 1,
endian = "little")
ev_list$type[i] <- readBin(cnt_file,
integer(),
size = 2,
n = 1,
endian = "little")
ev_list$code[i] <- readBin(cnt_file,
integer(),
size = 2,
n = 1,
endian = "little")
ev_list$latency[i] <- readBin(cnt_file,
double(),
size = 4,
n = 1,
endian = "little")
ev_list$epochevent[i] <- readBin(cnt_file,
integer(),
size = 1,
n = 1,
endian = "little")
ev_list$accept[i] <- readBin(cnt_file,
integer(),
size = 1,
n = 1,
endian = "little")
ev_list$accuracy[i] <- readBin(cnt_file,
integer(),
size = 1,
n = 1,
endian = "little")
}
ev_list$offset <- (ev_list$offset - beg_data) / (4 * n_channels) + 1
close(cnt_file)
chan_df <- chan_df[, names(chan_df) %in% c("electrode", "chan_no")]
out <- list(chan_info = chan_df,
head_info = data_info,
chan_data = chan_data,
event_list = ev_list)
}
#' Function for importing Brain Vision Analyzer files
#' @param file_name file name of the header file.
#' @keywords internal
import_vhdr <- function(file_name,
participant_id,
recording) {
.data <- read_vhdr(file_name)
epochs <- tibble::new_tibble(list(epoch = 1,
participant_id = participant_id,
recording = recording),
nrow = 1,
class = "epoch_info")
.data$epochs <- epochs
.data
}
#' @keywords internal
read_vhdr <- function(file_name) {
header_info <- ini::read.ini(file_name)
if (identical(header_info$`Common Infos`$DataFormat, "ASCII")) {
stop("BVA ASCII not currently supported.")
}
if (identical(header_info$`Common Infos`$DataType,
"FREQUENCYDOMAIN")) {
stop("Only time domain data is currently supported,
this data is in the frequency domain.")
}
if (identical(header_info$`Common Infos`$DataOrientation,
"VECTORIZED")) {
multiplexed <- FALSE
} else {
multiplexed <- TRUE
}
data_file <- file.path(dirname(file_name),
header_info$`Common Infos`$DataFile)
vmrk_file <- file.path(dirname(file_name),
header_info$`Common Infos`$MarkerFile)
n_chan <- as.numeric(header_info$`Common Infos`$NumberOfChannels)
# header gives sampling times in microseconds
srate <- 1e6 / as.numeric(header_info$`Common Infos`$SamplingInterval)
bin_format <- header_info$`Binary Infos`$BinaryFormat
chan_labels <- lapply(header_info$`Channel Infos`,
function(x) unlist(strsplit(x = x, split = ",")))
chan_names <- sapply(chan_labels, "[[", 1)
#EEGLAB exported BVA files are missing the channel resolution, which occupies
#the 3rd position - check length of chan_labels elements first to cope with
#that. Probably always 1 microvolt anyway...
if (length(chan_labels[[1]]) == 3) {
chan_scale <- as.numeric(sapply(chan_labels, "[[", 3))
} else {
chan_scale <- rep(1, length(chan_labels))
}
chan_info <- parse_vhdr_chans(chan_names,
header_info$`Coordinates`)
file_size <- file.size(data_file)
.data <- read_dat(data_file,
file_size = file_size,
bin_format = bin_format,
multiplexed)
.data <- matrix(.data,
ncol = n_chan,
byrow = multiplexed)
if (any(!is.na(chan_scale))) {
.data <- sweep(.data,
2,
chan_scale,
"*")
}
colnames(.data) <- chan_names
.data <- tibble::as_tibble(.data)
n_points <- nrow(.data)
timings <- tibble::tibble(sample = 1:n_points,
time = (sample - 1) / srate)
.markers <- read_vmrk(vmrk_file)
.markers <-
dplyr::mutate(.markers,
event_onset = as.numeric(event_onset),
length = as.numeric(length),
.electrode = ifelse(.electrode == 0,
NA,
as.character(chan_info$electrode[.electrode])),
event_time = (event_onset - 1) / srate)
.data <- eeg_data(data = .data,
srate = srate,
events = .markers,
chan_info = chan_info,
timings = timings,
reference = NULL)
.data
}
#' Read the raw data for a BVA file.
#'
#' @param file_name Filename of the .dat file
#' @param file_size Size of the .dat file
#' @param bin_format Storage format.
#' @param multiplexed Logical; whether the data is VECTORIZED (FALSE) or MULTIPLEXED (TRUE)
#' @keywords internal
read_dat <- function(file_name,
file_size,
bin_format,
multiplexed) {
if (identical(bin_format, "IEEE_FLOAT_32")) {
nbytes <- 4
signed <- TRUE
file_type <- "double"
} else if (identical(bin_format, "UINT_16")) {
nbytes <- 2
signed <- FALSE
file_type <- "int"
} else {
nbytes <- 2
signed <- TRUE
file_type <- "int"
}
raw_data <- readBin(file_name,
what = file_type,
n = file_size,
size = nbytes,
signed = signed)
raw_data
}
#' Read BVA markers
#'
#' Import and parse BVA marker files.
#'
#' @param file_name File name of the .vmrk markers file.
#' @keywords internal
read_vmrk <- function(file_name) {
vmrks <- ini::read.ini(file_name)
marker_id <- names(vmrks$`Marker Infos`)
markers <- lapply(vmrks$`Marker Infos`,
function(x) unlist(strsplit(x, ",")))
# to do - fix to check for "New segment" instead - it's the extra date field that causes issues
if (length(markers[[1]]) == 6) {
date <- markers[[1]][[6]]
markers <- lapply(markers, `[`, 1:5)
} else {
date <- NA
}
markers <- do.call(rbind, markers)
colnames(markers) <- c("BVA_type",
"event_type",
"event_onset",
"length",
".electrode")
markers <- tibble::as_tibble(markers)
markers <-
dplyr::mutate(markers,
event_onset = as.numeric(event_onset),
length = as.numeric(length),
.electrode = as.numeric(.electrode))
markers
}
#' Load `EEGLAB` .set files
#'
#' Load `EEGLAB` .set files and convert them to `eeg_epochs` objects. Supports
#' import of files saved in either Matlab v6.5 or Matlab v7.3 formats. Currently,
#' any ICA weights or decompositions are discarded.
#'
#' @param file_name Filename (and path if not in present working directory)
#' @param df_out Defaults to FALSE - outputs an object of class `eeg_epochs`. Set
#' to TRUE for a normal data frame.
#' @param participant_id Character vector. By default, the filename will be used as the id of the
#' participant.
#' @param recording Character vector. By default, the filename will be used as the name of the
#' recording.
#' @param drop_custom Drop custom event fields. TRUE by default.
#' @param verbose Print informative messages. TRUE by default.
#' @author Matt Craddock \email{matt@@mattcraddock.com}
#' @importFrom dplyr group_by mutate rename
#' @importFrom tibble tibble as_tibble
#' @importFrom purrr is_empty map_df
#' @examples
#' \dontrun{import_set("your_data.set")}
#' @return An object of class `eeg_epochs`
#' @export
import_set <- function(file_name,
df_out = FALSE,
participant_id = NULL,
recording = NULL,
drop_custom = FALSE,
verbose = TRUE) {
checkpkgs <-
unlist(
lapply(c("R.matlab", "hdf5r"),
requireNamespace,
quietly = TRUE)
)
if (!all(checkpkgs)) {
missing_pkg <- c("R.matlab", "hdf5r")[!checkpkgs]
if (length(missing_pkg) == 1) {
stop(paste("Package",
missing_pkg,
"needed. Please install it."),
call. = FALSE)
} else {
stop(
paste("Packages",
missing_pkg[[1]],
"&",
missing_pkg[[2]],
"needed. Please install them."
),
call. = FALSE
)
}
}
if (verbose) {
message("Importing from EEGLAB .set file.")
}
if (is.null(recording)) {
recording <- basename(tools::file_path_sans_ext(file_name))
}
if (is.null(participant_id)) {
participant_id <- basename(tools::file_path_sans_ext(file_name))
}
check_hdf5 <- hdf5r::is.h5file(file_name)
if (check_hdf5) {
if (verbose) {
message("Matlab 7.3 file format detected.")
}
return(
read_hdf5_set(file_name,
recording = recording,
participant_id = participant_id)
)
}
temp_dat <- R.matlab::readMat(file_name)
if (identical(names(temp_dat)[[1]], "EEG")) {
temp_dat <- temp_dat$EEG[, 1, 1]
}
n_chans <- temp_dat[["nbchan"]]
n_trials <- temp_dat[["trials"]]
times <- temp_dat[["times"]]
chan_info <- drop(Reduce(rbind,
temp_dat["chanlocs"]))
# var_names <- dimnames(temp_dat$EEG)[[1]]
#
# n_chans <- temp_dat$EEG[[which(var_names == "nbchan")]]
# n_trials <- temp_dat$EEG[[which(var_names == "trials")]]
# times <- temp_dat$EEG[[which(var_names == "times")]]
# chan_info <- drop(Reduce(rbind, temp_dat$EEG["chanlocs",,]))
pick_empties <-
vapply(
chan_info,
function(x) is.null(x) | length(x) == 0,
FUN.VALUE = logical(1)
)
chan_info[pick_empties] <- NA
chan_info <- lapply(data.frame(t(chan_info)),
unlist) # unlist each data.frame column
chan_info <- data.frame(chan_info) # turn back into data frame
chan_info <- parse_chaninfo(chan_info)
# check if the data is stored in the set or in a separate .fdt
#if (is.character(temp_dat$EEG[[which(var_names == "data")]])) {
if (is.character(temp_dat[["data"]])) {
if (verbose) {
message("Importing data from .fdt file.")
}
fdt_file <- paste0(tools::file_path_sans_ext(file_name),
".fdt")
fdt_file <- file(fdt_file, "rb")
# read in data from .fdt
# do this in chunks to avoid memory errors for large files...?
signals <- readBin(fdt_file,
"double",
n = n_chans * n_trials * length(times),
size = 4,
endian = "little")
close(fdt_file)
dim(signals) <- c(n_chans, length(times) * max(n_trials, 1))
times <- rep(times, max(n_trials, 1))
if (n_trials == 1) {
continuous <- TRUE
} else {
continuous <- FALSE
}
} else {
# if the data is in the .set file, load it here instead of above
#signals <- temp_dat$EEG[[which(dimnames(temp_dat$EEG)[[1]] == "data")]]
signals <- temp_dat[["data"]]
dim_signals <- dim(signals)
if (length(dim_signals) == 3) {
dim(signals) <- c(dim_signals[1], dim_signals[2] * dim_signals[3])
times <- rep(times, n_trials)
continuous <- FALSE
} else {
continuous <- TRUE
}
}
signals <- t(signals)
colnames(signals) <- unique(chan_info$electrode)
signals <- as.data.frame(signals)
signals$time <- as.numeric(times)
#srate <- temp_dat$EEG[[which(var_names == "srate")]][[1]]
srate <- temp_dat[["srate"]][[1]]
if (!continuous) {
signals <- dplyr::group_by(signals,
time)
signals <- dplyr::mutate(signals,
epoch = 1:dplyr::n())
signals <- dplyr::ungroup(signals)
}
#event_info <- temp_dat$EEG[[which(var_names == "event")]]
event_info <- temp_dat[["event"]]
event_table <- event_info
dim(event_table) <- c(dim(event_info)[[1]],
dim(event_info)[[3]])
event_table <- t(event_table)
colnames(event_table) <- dimnames(event_info)[[1]]
event_table <- tibble::as_tibble(event_table)
event_table <-
purrr::map_df(event_table,
~unlist(
purrr::map(.,
~ifelse(is.null(.),
NA,
.)))
)
event_table <- lapply(event_table,
unlist)
empty_entries <- unlist(lapply(event_table,
rlang::is_empty))
if (any(empty_entries)) {
empty_cols <- names(event_table)[empty_entries]
message(paste0("Removing empty event table column (s):",
empty_cols))
event_table <- event_table[!empty_entries]
}
event_table <- tibble::as_tibble(event_table)
# EEGLAB stores latencies in samples and allows non-integer samples (e.g.
# through downsampling, or more rapidly sampled events than EEG signal)
#
if (any(event_table$latency %% 1 > 0)) {
message("Rounding non-integer event sample latencies...")
event_table$latency <- round(event_table$latency)
# This can result in an event with a latency of zero in samples, which
# causes problems with subsequent import steps - fix that and turn it into
# sample 1
event_table$latency <- ifelse(event_table$latency == 0,
1,
event_table$latency)
}
# EEGLAB stores latencies in samples starting from 1, my event_time is in
# seconds, starting from 0
event_table$event_time <- (event_table$latency - 1) / srate
std_cols <- c("latency",
"event_time",
"type",
"epoch")
if (drop_custom & any(!colnames(event_table) %in% std_cols)) {
message("Dropping custom columns...")
event_table <- event_table[, std_cols]
}
col_check <- colnames(event_table) %in% c("event_type", "event_onset")
if (any(col_check)) {
dupe_checks <- colnames(event_table)[col_check]
dupe_labs <- paste0(dupe_checks, ".x")
}
#need to build in check for duplicate columns
event_table <- dplyr::rename(event_table,
event_type = "type",
event_onset = "latency")
if (df_out) {
return(signals)
} else {
signals$time <- signals$time / 1000
# convert to seconds - eeglab uses milliseconds
if (continuous) {
timings <- tibble::tibble(time = signals$time, #epoch = signal,
sample = 1:length(signals$time))
n_epochs <- 1
} else {
timings <- tibble::tibble(time = signals$time,
epoch = signals$epoch,
sample = 1:length(signals$time))
event_table$time <- NA
event_table$time <- timings[which(timings$sample %in% event_table$event_onset,
arr.ind = TRUE), ]$time
n_epochs <- length(unique(timings$epoch))
}
epochs <-
tibble::new_tibble(list(epoch = 1:n_epochs,
participant_id = rep(participant_id, n_epochs),
recording = rep(recording, n_epochs)),
nrow = n_epochs,
class = "epoch_info")
if (continuous) {
out_data <- eeg_data(signals[, 1:n_chans],
srate = srate,
timings = timings,
chan_info = chan_info,
events = event_table,
epochs = epochs)
} else {
out_data <- eeg_epochs(signals[, 1:n_chans],
srate = srate,
timings = timings,
chan_info = chan_info,
events = event_table,
reference = NULL,
epochs = epochs)
}
out_data
}
}
#' Parse channel info from an EEGLAB set file
#'
#' Internal function to convert EEGLAB chan_info to eegUtils style
#'
#' @param chan_info Channel info list from an EEGLAB set file
#' @param drop If there are additional columns, remove all columns except
#' electrode if TRUE, or just unexpected columns if FALSE.
#' @keywords internal
parse_chaninfo <- function(chan_info,
drop = FALSE) {
# chan_info <- chan_info[sort(names(chan_info),
# method = "shell")]
expected <- c("labels", "radius",
"ref", "sph.phi",
"sph.radius", "sph.theta",
"theta", "type",
"urchan", "X",
"Y", "Z")
# Check for two things:
# 1) Missing expected columns.
# 2) Columns which are non-standard.
if (!all(names(chan_info) %in% expected)) {
if (drop) {
warning("EEGLAB chan info has unexpected format, taking electrode names only.")
out <- data.frame(chan_info["labels"])
names(out) <- "electrode"
return(validate_channels(out))
} else {
warning("EEGLAB chan info has unexpected format, taking only expected columns.")
miss_cols <- expected[!(expected %in% names(chan_info))]
#Why did I set this to NA? must have been a reason but it has unintended
#consequences. Remember to build a test when I end up back here with an
#example...
chan_info[miss_cols] <- NA
}
}
if (!all(expected %in% names(chan_info))) {
miss_cols <- expected[!(expected %in% names(chan_info))]
chan_info[miss_cols] <- NA
}
chan_info <- chan_info[expected]
names(chan_info) <- c("electrode",
"radius",
"ref",
"sph_phi",
"sph_radius",
"sph_theta",
"angle",
"type",
"urchan",
"cart_x",
"cart_y",
"cart_z"
)
chan_info <- chan_info[c("electrode",
"cart_x",
"cart_y",
"cart_z")]
# EEGLAB co-ordinates are rotated 90 degrees compared to our coordinates,
# and left-right flipped
names(chan_info) <- names(chan_info)[c(1, 3, 2, 4)]
chan_info <- chan_info[, c(1, 3, 2, 4)]
chan_info$cart_x <- -chan_info$cart_x
sph_coords <- cart_to_spherical(chan_info[, c("cart_x", "cart_y", "cart_z")])
xy <- project_elecs(sph_coords)
chan_info <- dplyr::bind_cols(electrode = as.character(chan_info$electrode),
sph_coords,
chan_info[, 2:4],
xy)
chan_info
}
#' Parse BVA channel info
#'
#' @param chan_labels The `Channel Infos` section from a BVA vhdr file
#' @param chan_info The `Coordinates` section from a BVA vhdr file
#' @keywords internal
parse_vhdr_chans <- function(chan_labels,
chan_info,
verbose = TRUE) {
init_chans <- data.frame(electrode = chan_labels)
if (is.null(chan_info)) {
init_chans$radius <- NA
init_chans$theta <- NA
init_chans$phi <- NA
if (verbose) message("No channel locations found.")
return(validate_channels(init_chans))
} else {
coords <- lapply(chan_info,
function(x) as.numeric(unlist(strsplit(x, split = ","))))
new_coords <- data.frame(do.call(rbind,
coords))
names(new_coords) <- c("radius", "theta", "phi")
new_coords <- cbind(init_chans, new_coords)
new_coords[new_coords$radius %in% c(0, NaN), 2:4] <- NA
chan_info <- bva_elecs(new_coords)
tibble::as_tibble(chan_info)
}
}
#' Convert BVA spherical locations
#'
#' Reads the BrainVoyager spherical electrode locations and converts them to
#' Cartesian 3D and 2D projections.
#'
#' @param chan_info A data.frame containing electrodes
#' @param radius Head radius in millimetres
#' @keywords internal
bva_elecs <- function(chan_info, radius = 85) {
chan_info <-
dplyr::mutate(chan_info,
cart_x = sin(deg2rad(theta)) * cos(deg2rad(phi)),
cart_y = sin(deg2rad(theta)) * sin(deg2rad(phi)),
cart_z = cos(deg2rad(theta)),
x = theta * cos(deg2rad(phi)),
y = theta * sin(deg2rad(phi)))
chan_info[, c("cart_x", "cart_y", "cart_z")] <-
chan_info[, c("cart_x", "cart_y", "cart_z")] * radius
chan_info
}
#' Import Fieldtrip files
#'
#' Fieldtrip is a Matlab package for EEG/MEG processing and analysis.
#'
#' @author Matt Craddock \email{matt@@mattcraddock.com}
#' @param file_name Name of file to be imported.
#' @param recording Name of the recording. By default, the filename will be
#' used.
#' @param participant_id Identifier for the participant.
#' @param verbose Informative messages printed to console. Defaults to TRUE.
#' @examples
#' \dontrun{import_ft("fieldtrip_test.mat")}
#' @return An object of class `eeg_data`, `eeg_epochs`, or
#' `eeg_tfr`, depending on the type of input data.
#' @export
import_ft <- function(file_name,
participant_id = NULL,
recording = NULL,
verbose = TRUE) {
if (!requireNamespace("R.matlab", quietly = TRUE)) {
stop("Package \"R.matlab\" needed. Please install it.",
call. = FALSE)
}
tmp_ft <- R.matlab::readMat(file_name)
tmp_ft <- tmp_ft[[1]]
struct_names <- rownames(tmp_ft)
if ("pos" %in% struct_names) {
stop("Import of source data is not currently supported.")
}
if (is.null(participant_id)) {
participant_id <- basename(tools::file_path_sans_ext(file_name))
}
if (is.null(recording)) {
recording <- basename(tools::file_path_sans_ext(file_name))
}
if ("dimord" %in% struct_names) {
dimensions <- proc_dimord(unlist(tmp_ft["dimord", ,],
use.names = FALSE))
if ("frequency" %in% dimensions) {
return(ft_freq(tmp_ft,
dimensions,
struct_names = struct_names,
participant_id = participant_id,
recording = recording,
verbose = verbose))
}
} else {
# work out if the file stores components (from ICA or PCA) or "raw" data
if ("unmixing" %in% struct_names) {
return(ft_comp(tmp_ft))
} else {
return(ft_raw(tmp_ft,
struct_names = struct_names,
participant_id = participant_id,
recording = recording,
verbose = verbose))
}
}
}
read_bdf_header <- function(file_name) {
file_read <- file(file_name, "rb")
pos <- seek(file_read, 1)
sys_type <- readChar(file_read, 7)
if (sys_type == "BIOSEMI") {
samp_bits <- 24
} else {
samp_bits <- 16
}
patient_id <- readChar(file_read, 80)
recording_id <- readChar(file_read, 80)
recording_date <- readChar(file_read, 8)
recording_time <- readChar(file_read, 8)
n_bytes <- readChar(file_read, 8)
format_version <- readChar(file_read, 44)
n_records <- readChar(file_read, 8)
dur_records <- readChar(file_read, 8)
n_chan <- as.integer(readChar(file_read, 4))
chan_labels <- readChar(file_read, n_chan * 16)
chan_labels <- unlist(strsplit(chan_labels, " "))
chan_labels <- chan_labels[chan_labels != ""]
if (length(chan_labels) != n_chan) {
stop("Chan_label import went wrong!")
}
chan_types <- readChar(file_read, n_chan * 80)
chan_types <- unlist(strsplit(chan_types, " "))
chan_types <- chan_types[chan_types != ""]
chan_units <- readChar(file_read, n_chan * 8)
chan_units <- unlist(strsplit(chan_units, " "))
chan_units <- chan_units[chan_units != ""]
phys_mins <- readChar(file_read, n_chan * 8)
phys_mins <- as.integer(unlist(strsplit(phys_mins, " ")))
phys_max <- readChar(file_read, n_chan * 8)
phys_max <- as.integer(unlist(strsplit(phys_max, " ")))
dig_mins <- readChar(file_read, n_chan * 8)
dig_mins <- unlist(strsplit(gsub("-", " -", dig_mins), " "))
dig_mins <- as.integer(dig_mins)
dig_mins <- dig_mins[!is.na(dig_mins)]
dig_max <- readChar(file_read, n_chan * 8)
dig_max <- as.integer(unlist(strsplit(gsub("-", " -", dig_max), " ")))
prefilt <- readChar(file_read, n_chan * 80)
prefilt <- remove_empties(prefilt, " ")
n_samp_rec <- readChar(file_read, n_chan * 8) # sampling rate
n_samp_rec <- remove_empties(n_samp_rec)
reserved <- readChar(file_read, n_chan * 32)
close(file_read)
list(sys_type = sys_type,
samp_bits = samp_bits,
patient_id = patient_id,
recording_id = recording_id,
recording_time = recording_time,
n_chans = n_chan,
n_records = as.integer(n_records),
record_dur = as.integer(dur_records),
chan_labels = chan_labels,
chan_types = chan_types,
chan_units = chan_units,
phys_mins = phys_mins,
phys_max = phys_max,
dig_mins = dig_mins,
dig_max = dig_max,
prefiltering = prefilt,
srate = as.integer(n_samp_rec))
}
remove_empties <- function(strings, char_match = " ") {
strings <- unlist(strsplit(strings, char_match))
strings[strings != ""]
}
read_bdf_data <- function(file_name, headers) {
sig_file <- file(file_name,
"rb")
#skip headers
start_pos <- (headers$n_chans + 1) * 256
pos <- seek(sig_file,
start_pos)
bytes_per_rec <- headers$samp_bits / 8
n_chans <- headers$n_chans
sig_length <- headers$srate[[1]] * bytes_per_rec * n_chans # length of one record in bytes
rec_length <- headers$srate[[1]]
rec_size <- sig_length / 256 # calc size in kilobytes in memory
records_per <- min(floor(20000 / rec_size), headers$n_records) # read up to 20000 kb at once
gains <- (headers$phys_max - headers$phys_min) / (headers$dig_max - headers$dig_mins)
offsets <- headers$phys_mins - gains * headers$dig_mins
sig_out <- matrix(0,
nrow = headers$srate[[1]] * headers$n_records,
ncol = n_chans)
chunk_size <- rec_length * records_per
n_chunks <- floor(headers$n_records / records_per)
remaining <- headers$n_records %% records_per
first_record <- integer(sig_length * records_per)
shifted <- integer(sig_length * records_per)
for (records in 1:n_chunks) {
first_record <- readBin(sig_file,
"raw",
n = sig_length * records_per,
endian = "little")
shifted <- as.integer(first_record) * as.integer(2^c(0, 8, 16))
shifted <- array(shifted,
dim = c(3,
headers$srate[[1]],
n_chans,
records_per))
shifted <- colSums(shifted)
shifted <- matrix(aperm(shifted,
c(1, 3, 2)),
nrow = prod(dim(shifted)[c(1, 3)]))
modifier <- chunk_size * (records - 1)
sig_out[(1:chunk_size) + modifier, ] <- shifted
}
if (remaining > 0) {
final_records <- readBin(sig_file,
"raw",
n = sig_length * remaining,
endian = "little")
shifted <- as.integer(final_records) * as.integer(2^c(0, 8, 16))
shifted <- array(shifted, dim = c(3,
headers$srate[[1]],
n_chans,
remaining))
shifted <- colSums(shifted)
modifier <- nrow(sig_out) - remaining * headers$srate[[1]] + 1
sig_out[modifier:nrow(sig_out), ] <- matrix(aperm(shifted,
c(1, 3, 2)),
nrow = prod(dim(shifted)[c(1, 3)]))
}
# Correct for sign
sig_out <- sig_out - bitwShiftL(bitwShiftR(sig_out,
23),
24)
sig_out[, 1:(n_chans - 1)] <- sig_out[, 1:(n_chans - 1)] * gains[1] + offsets[1]
close(sig_file)
sig_out
}
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