149 lines
5.6 KiB
R
149 lines
5.6 KiB
R
# List of all sensor names/positions (in chanel order) to ensure all entries
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# are places at the same sensor x time position in the matrix layout
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sensors <- c(
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"FP1", "FP2", "F7", "F8", "AF1", "AF2", "FZ", "F4", "F3", "FC6", "FC5",
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"FC2", "FC1", "T8", "T7", "CZ", "C3", "C4", "CP5", "CP6", "CP1", "CP2",
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"P3", "P4", "PZ", "P8", "P7", "PO2", "PO1", "O2", "O1", "X", "AF7",
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"AF8", "F5", "F6", "FT7", "FT8", "FPZ", "FC4", "FC3", "C6", "C5", "F2",
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"F1", "TP8", "TP7", "AFZ", "CP3", "CP4", "P5", "P6", "C1", "C2", "PO7",
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"PO8", "FCZ", "POZ", "OZ", "P2", "P1", "CPZ", "nd", "Y"
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)
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tmpdir <- tempdir()
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untar("eeg_full.tar", exdir = tmpdir) # uncompress
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subjects <- untar("eeg_full.tar", list = TRUE) # file names (only read)
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subjects <- `names<-`(vector("list", length(subjects)), substr(subjects, 1, 11))
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for (i in seq_along(subjects)) {
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subject <- names(subjects)[i]
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# Decompressed folder of trials for current subject
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untar(file.path(tmpdir, sprintf("%s.tar.gz", subject)), exdir = tmpdir)
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# Iterate all trial files of current subject
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X <- lapply(list.files(file.path(tmpdir, subject), full.names = TRUE),
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function(trial) {
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# Read leading meta data lines and data from gz compressed CSV file
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conn <- gzfile(trial)
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meta <- readLines(conn, 4)
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# If there are less than 4 entries in meta, the file does NOT contain
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# any data -> error, ignore trial
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if (length(meta) < 4) {
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return(NULL)
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}
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# Read data (measurements)
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data <- read.csv(conn,
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header = FALSE, sep = " ", comment.char = "#",
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col.names = c("trial", "sensor", "time", "volts"),
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colClasses = c("integer", "character", "integer", "numeric")
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)
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# Compute index of every entry (ensures same
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# placement of measurements in its matrix representation)
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data$sensor <- factor(data$sensor, levels = sensors)
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idx <- (as.integer(data$sensor) - 1L) * 256L + data$time + 1L
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# Check if every sensor at every time point is present to ensure
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# no interleaving or shifted data in final 3D array
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if ((length(idx) != 64 * 256) || any(sort(idx) != seq_along(idx))) {
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return(NULL)
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}
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# Return measurements in standardized order and attach meta info
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structure(data$volts[idx], meta = meta[4])
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}
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)
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# Count nr. of errors (return NULL) and remove from data
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file_error_idx <- which(sapply(X, is.null))
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if (length(file_error_idx)) {
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X[file_error_idx] <- NULL
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}
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# Extract meta information
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meta <- sapply(X, attr, "meta")
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# Check for error notification in meta data and drop them as well
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notice_error_idx <- grep(".*err.*", meta)
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if (length(notice_error_idx)) {
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X[notice_error_idx] <- NULL
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meta <- meta[-notice_error_idx]
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}
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# Split into trial condition and trial nr.
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condition <- factor(
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sub(".*(S1 obj|S2 match|S2 nomatch).*", meta, replacement = "\\1"),
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levels = c("S1 obj", "S2 match", "S2 nomatch")
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)
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# Concatinate individual trials in a 3D array (based on standardized order)
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X <- matrix(unlist(X), 256 * 64)
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# Track for reporting the nr. of non-finite values
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nr_non_finite <- sum(!is.finite(X))
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# Compute mean over trials grouped by trial condition
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X <- c(
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rowMeans(X[, condition == "S1 obj", drop = FALSE], na.rm = TRUE),
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rowMeans(X[, condition == "S2 match", drop = FALSE], na.rm = TRUE),
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rowMeans(X[, condition == "S2 nomatch", drop = FALSE], na.rm = TRUE)
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)
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# store mean of trials of current subject in list of subjects
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subjects[[subject]] <- X
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# Remove/Delete subject file and decompressed folder
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unlink(file.path(tmpdir, subject), recursive = TRUE)
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unlink(file.path(tmpdir, sprintf("%s.tar.gz", subject)))
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# Report progress
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cat(sprintf(
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"%5d/%d - Nr. trials: %3d = %3d + %3d + %3d%s%s\n",
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i, length(subjects),
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length(condition),
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sum(condition == "S1 obj"),
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sum(condition == "S2 match"),
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sum(condition == "S2 nomatch"),
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if (nr_non_finite) sprintf(", Nr. non-finite: %d", nr_non_finite) else "",
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if (length(file_error_idx) && length(notice_error_idx)) {
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sprintf(", %d file and %d notice errors (%s) -> trials dropped",
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length(file_error_idx), length(notice_error_idx), subject
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)
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} else if (length(file_error_idx)) {
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sprintf(", %d file errors (%s) -> trials dropped",
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length(file_error_idx), subject
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)
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} else if (length(notice_error_idx)) {
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sprintf(", %d notice errors (%s) -> trials dropped",
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length(notice_error_idx), subject
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)
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} else {
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""
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}
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))
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}
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# Combine subjects in single 4D tensor (time x sensor x condition x subject)
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X <- array(
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unlist(subjects),
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dim = c(time = 256, sensor = 64, condition = 3, subject = length(subjects)),
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dimnames = list(
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time = 1:256,
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sensor = sensors,
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condition = c("S1 obj", "S2 match", "S2 nomatch"),
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subject = names(subjects)
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)
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)
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# Extract alcoholic or control labels for each subject
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y <- factor(
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substr(names(subjects), 4, 4),
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levels = c("c", "a"),
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labels = c("control", "alcoholic")
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)
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names(y) <- names(subjects)
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# Save full processed EEG dataset as R data file
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saveRDS(list(X = X, y = y), file = "eeg_data_3d.rds")
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saveRDS(list(X = X[, , "S1 obj", ], y = y), file = "eeg_data_2d.rds")
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