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tutorial_preproc_edf_sub.R
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## trials that were aborted due to badgazes or badpresses will not contain all
## information (e.g., no info about a response because there's no response).
## define a function to simply assign NA in those cases
ie <- function(type, ...) {
if (type == 'd') {
type = 'double'
} else if (type == 'l') {
type = 'logical'
} else if (type == 'c') {
type = 'character'
} else if (type == 'i') {
type = 'integer'
}
if (!exists(as.character(as.list(quote(...))))) {
return(as(NA, type))
} else {
inp = list(...)
return(inp[[1]])
}
}
preprocess_edf <- function(fname, P, verbose = TRUE) {
# check if there are more files for this subject (i.e., a session was interrupted)
id = str_extract(fname, '(?<=ROSET)\\d+')
extrafiles <- list.files(P$RDATpath, paste0('ROSE[A-S]', id), full.names = T)
# load the current subject file:
load(paste0(P$RDATpath, fname, ".Rdat"))
if (length(extrafiles) > 0) {
extrafiles <- str_sort(extrafiles)
initdat <- curdat
for (i in 1:length(extrafiles)) {
load(extrafiles[i])
# increase all extra files' time vectors by an arbitrary constant to avoid
# conflicts
curdat$samples$time = curdat$samples$time + i*1e15
curdat$blinks$sttime = curdat$blinks$sttime + i*1e15
curdat$blinks$entime = curdat$blinks$entime + i*1e15
curdat$saccades$sttime = curdat$saccades$sttime + i*1e15
curdat$saccades$entime = curdat$saccades$entime + i*1e15
curdat$fixations$sttime = curdat$fixations$sttime + i*1e15
curdat$fixations$entime = curdat$fixations$entime + i*1e15
curdat$ttl$sttime = curdat$ttl$sttime + i*1e15
curdat$messages$time = curdat$messages$time + i*1e15
initdat$samples <- rbind(initdat$samples, curdat$samples)
initdat$blink <- rbind(initdat$blinks, curdat$blinks)
initdat$saccades <- rbind(initdat$saccades, curdat$saccades)
initdat$fixations <- rbind(initdat$fixations, curdat$fixations)
initdat$ttl <- rbind(initdat$ttl, curdat$ttl)
initdat$messages <- rbind(initdat$messages, curdat$messages)
}
curdat <- initdat
initdat <- NULL
}
# convert to data.table for more efficient syntax, speed, and RAM use.
samples <- data.table(curdat$samples) #curdat is the data.table of the single subject's raw data
curdat$samples <- NULL # save RAM
setkey(samples, time) # keys samples by time (speed)
## Find out which eye was used and delete the nonsense columns of the other eye
if (curdat$recordingInfo$eye == 2) {
setnames(samples, c("Time", "Flags", "remove1", "remove2", "remove3", "X",
"Y", "Dil", "Blink", "Fixation", "Saccade"))
} else if (curdat$recordingInfo$eye == 1) {
setnames(samples, c("Time", "Flags", "X", "Y", "Dil", "remove1", "remove2",
"remove3", "Blink", "Fixation", "Saccade"))
} else {
stop("I cannot determine which Eye has been tracked\n")
}
# delete the nonsense columns of the other eye
samples[, c('remove1', 'remove2', 'remove3') := NULL]
## Assign trial information to all the samples. Register the start time of a trial,
## store the relevant variables, and then when we know the end time, assign these variables
## to the output data.table "samples".
numTrial <- 0
# in case this loop gives the warning "input string 1 is invalid in this
# locale", use Sys.setlocale(locale="C")
## now we loop over all messages, including all the stuff that's auto-sent
## by eyelink.
foundNewTrl <- FALSE
trial_end_reached <- FALSE
if (verbose) {
cat(sprintf('\nNow working on subject %s\n', fname))
cat('parsing messages:\n')
pb = txtProgressBar(0, nrow(curdat$messages), style = 3,
title = 'parsing messages')
}
tmp <- maketmp()
for (i in 1:nrow(curdat$messages)) {
if (verbose) {setTxtProgressBar(pb, i)}
# store the current message in e
e <- curdat$messages[i,]
# split it by tabs
msgSplit <- strsplit(e$msg,"\\\\t")[[1]]
# our special informative messages have a common '>' as first character
# skip iteration if not present
if (!str_starts(msgSplit[1], '>')) {next}
# we only get here if it's a special message. now we need to check which
# one. That's usually written in the second argument. e.g., if you sent
# something like EyelinkSendTabMsg('TrlOn',ID,itrial) in Matlab, you'd be
# looking for 'TrlOn'
if (msgSplit[1] == '>trial_on') {
# yay! we found the onset of a new trial
numTrial <- numTrial + 1
# if (verbose) {
# cat("Working on trial ", numTrial, "\t of file:", fname,
# "\t on worker:", Sys.getpid(), "\n")
# }
# as the messages are already aligned with the eyetrackig data, we can
# use the eyetrackers time vector to store the absolute time when this
# message was sent.
tmp$startT <- as.numeric(e$time)
foundNewTrl <- TRUE
tmp$badgaze <- FALSE
tmp$badpress <- FALSE
}
## once we found that starting message, we keep on looping, trying to find
## more messages for the current trial.
if (foundNewTrl) {
if (msgSplit[1] == '>faulty_fix') {
# subject couldn't fix in time
numTrial <- numTrial - 1
foundNewTrl <- FALSE
}
if (msgSplit[1] == ">targets_on") {
tmp$TarOn <- as.numeric(e$time)
}
if (msgSplit[1] == ">targets_off") {
tmp$TarOff <- as.numeric(e$time)
}
if (msgSplit[1] == ">saccue_on") {
tmp$SaccueOn <- as.numeric(e$time)
}
if (msgSplit[1] == ">saccue_off") {
tmp$SaccueOff <- as.numeric(e$time)
}
if (msgSplit[1] == ">sacc_on") {
tmp$SaccOn <- as.numeric(e$time)
}
if (msgSplit[1] == ">probe_fixed") {
tmp$probeFixed <- as.numeric(e$time)
}
if (msgSplit[1] == ">probe_on") {
tmp$probeOn <- as.numeric(e$time)
}
if (msgSplit[1] == ">response") {
tmp$RespOn <- as.numeric(e$time)
}
if (msgSplit[1] == ">badgaze") {
tmp$badgaze_T <- as.numeric(e$time)
tmp$badgaze <- TRUE
}
if (msgSplit[1] == ">badpress") {
tmp$badpress_T <- as.numeric(e$time)
tmp$badpress <- TRUE
}
## The messages "TrialInfo", "probe_color_rgb", "target_color_rgb", &
## 'target_color_idx' are special. They contain tab-delimited info
## on the trial itself
if (msgSplit[1] == ">TrialInfo") {
tmp$InfoTime <- as.numeric(e$time)
if (tmp$badgaze || tmp$badpress) {
tmp$SubjName <- msgSplit[4]
tmp$SubjNr <- as.integer(str_extract(msgSplit[4], '(?<=ROSET)\\d+'))
tmp$isFemale <- msgSplit[5] == 'W'
tmp$visioncorrected <- as.logical(as.integer(msgSplit[6]))
tmp$age <- as.integer(msgSplit[7])
tmp$DominantEyeIsRight <- msgSplit[8] == 'R'
tmp$trialNr <- as.integer(msgSplit[9])
tmp$change <- as.numeric(msgSplit[10])
tmp$changedeg <- as.numeric(msgSplit[11])
tmp$probedUpperHalf <- msgSplit[12] == '1'
cues <- c('L', 'C', 'R')
tmp$saccue <- cues[as.integer(msgSplit[13])]
cues <- c('MemU', 'MemR', 'MemD', 'MemL', 'MemV', 'MemH')
tmp$tarcue <- cues[as.integer(msgSplit[14])]
tmp$trialIdx <- as.integer(msgSplit[15])
tmp$T_trl_on <- as.numeric(msgSplit[16])
trial_end_reached = TRUE
} else {
tmp$SubjName <- msgSplit[2]
tmp$SubjNr <- as.integer(str_extract(msgSplit[2], '(?<=ROSET)\\d+'))
tmp$isFemale <- msgSplit[3] == 'W'
tmp$visioncorrected <- as.logical(as.integer(msgSplit[4]))
tmp$age <- as.integer(msgSplit[5])
tmp$DominantEyeIsRight <- msgSplit[6] == 'R'
tmp$trialNr <- as.integer(msgSplit[7])
tmp$change <- as.numeric(msgSplit[8])
tmp$changedeg <- as.numeric(msgSplit[9])
tmp$probedUpperHalf <- msgSplit[10] == '1'
tmp$respondedUpperHalf <- msgSplit[11] == '1'
tmp$responseCorrect <- msgSplit[12] == '1'
tmp$RT <- as.numeric(msgSplit[13])
cues <- c('L', 'C', 'R')
tmp$saccue <- cues[as.integer(msgSplit[14])]
cues <- c('MemU', 'MemR', 'MemD', 'MemL', 'MemV', 'MemH')
tmp$tarcue <- cues[as.integer(msgSplit[15])]
tmp$threshEstim <- as.numeric(msgSplit[16])
tmp$threshSD <- as.numeric(msgSplit[17])
tmp$trialCompleted <- as.logical(as.integer(msgSplit[18]))
tmp$trialIdx <- as.integer(msgSplit[19])
tmp$T_trl_on <- as.numeric(msgSplit[20])
tmp$T_probe_on <- as.numeric(msgSplit[21])
tmp$T_sacue_off <- as.numeric(msgSplit[22])
tmp$T_sacue_on <- as.numeric(msgSplit[23])
tmp$T_targets_off <- as.numeric(msgSplit[24])
tmp$T_targets_on <- as.numeric(msgSplit[25])
tmp$T_fix_on <- as.numeric(msgSplit[26])
tmp$sacc_rt <- as.numeric(msgSplit[27])
tmp$T_sacc_on <- as.numeric(msgSplit[28])
tmp$T_probe_fix_on <- as.numeric(msgSplit[29])
tmp$fix_rt <- as.numeric(msgSplit[30])
tmp$T_feedb_on <- as.numeric(msgSplit[31])
tmp$T_feedb_off <- as.numeric(msgSplit[32])
tmp$endT <- as.numeric(e$time)
}
}
if (msgSplit[1] == ">probe_color_rgb") {
tmp$upR <- as.numeric(msgSplit[2])
tmp$upG <- as.numeric(msgSplit[3])
tmp$upB <- as.numeric(msgSplit[4])
tmp$downR <- as.numeric(msgSplit[5])
tmp$downG <- as.numeric(msgSplit[6])
tmp$downB <- as.numeric(msgSplit[7])
}
if (msgSplit[1] == ">target_color_rgb") {#pos, up/do, r/g/b, now upuprgb,updownrgb,rigturgb etc
tmp$UupR <- as.numeric(msgSplit[2])
tmp$UupG <- as.numeric(msgSplit[3])
tmp$UupB <- as.numeric(msgSplit[4])
tmp$UdownR <- as.numeric(msgSplit[5])
tmp$UdownG <- as.numeric(msgSplit[6])
tmp$UdownB <- as.numeric(msgSplit[7])
tmp$RupR <- as.numeric(msgSplit[8])
tmp$RupG <- as.numeric(msgSplit[9])
tmp$RupB <- as.numeric(msgSplit[10])
tmp$RdownR <- as.numeric(msgSplit[11])
tmp$RdownG <- as.numeric(msgSplit[12])
tmp$RdownB <- as.numeric(msgSplit[13])
tmp$DupR <- as.numeric(msgSplit[14])
tmp$DupG <- as.numeric(msgSplit[15])
tmp$DupB <- as.numeric(msgSplit[16])
tmp$DdownR <- as.numeric(msgSplit[17])
tmp$DdownG <- as.numeric(msgSplit[18])
tmp$DdownB <- as.numeric(msgSplit[19])
tmp$LupR <- as.numeric(msgSplit[20])
tmp$LupG <- as.numeric(msgSplit[21])
tmp$LupB <- as.numeric(msgSplit[22])
tmp$LdownR <- as.numeric(msgSplit[23])
tmp$LdownG <- as.numeric(msgSplit[24])
tmp$LdownB <- as.numeric(msgSplit[25])
}
## the "target_color_idx" message is the last message in a trial and
## hence marks the end of a trial Because it marks the end of a trial,
## whenever we catch this message we add all our temporary variables to
## our output data.table
if (msgSplit[1] == ">target_color_idx") {
tmp$tarcolidxUu <- as.integer(msgSplit[2])
tmp$tarcolidxRu <- as.integer(msgSplit[3])
tmp$tarcolidxDu <- as.integer(msgSplit[4])
tmp$tarcolidxLu <- as.integer(msgSplit[5])
tmp$tarcolidxUd <- as.integer(msgSplit[6])
tmp$tarcolidxRd <- as.integer(msgSplit[7])
tmp$tarcolidxDd <- as.integer(msgSplit[8])
tmp$tarcolidxLd <- as.integer(msgSplit[9])
trial_end_reached = TRUE
}
# add information coded in messages in long format to our 1000Hz samples-DT
# we now start & end time of the current trial in eyetracker-time,
# so we assign all behavioral values to the respective part of the
# huge data.table
if (trial_end_reached) {
# when a session was aborted in between, we might have received messages
# but don't have data for the last trial before it aborted. in that case
# add some rows
if (nrow(samples[Time %between% list(tmp$startT, tmp$InfoTime),] ) == 0){
tmpsamples <- data.table(Time = tmp$startT:tmp$InfoTime)
samples <- merge(samples, tmpsamples, by = 'Time', all = T)
}
samples[Time %between% list(tmp$startT, tmp$InfoTime), ':='(
Subject = tmp$SubjName,
ID = tmp$SubjNr,
isFemale = tmp$isFemale,
visioncorrected = tmp$visioncorrected,
age = tmp$age,
DominantEyeIsRight = tmp$DominantEyeIsRight,
Trial = tmp$trialNr,
change = tmp$change,
changedeg = tmp$changedeg,
probedUpperHalf = tmp$probedUpperHalf,
respondedUpperHalf = tmp$respondedUpperHalf,
responseCorrect = tmp$responseCorrect,
RT = tmp$RT,
saccadecue = tmp$saccue,
targetcue = tmp$tarcue,
Threshold_Estimate = tmp$threshEstim,
Threshold_SD = tmp$threshSD,
Trial_Completed = tmp$trialCompleted,
Trial_idx = tmp$trialIdx,
T_trial_on = tmp$T_trl_on,
T_probe_on = tmp$T_probe_on,
T_sacue_off = tmp$T_sacue_off,
T_sacue_on = tmp$T_sacue_on,
T_targets_off = tmp$T_targets_off,
T_targets_on = tmp$T_targets_on,
T_fix_on = tmp$T_fix_on,
Saccadic_RT = tmp$sacc_rt,
T_sacc_on = tmp$T_sacc_on,
T_probe_fixation_on = tmp$T_probe_fix_on,
Fixation_RT = tmp$fix_rt,
T_feedback_on = tmp$T_feedb_on,
T_feedback_off = tmp$T_feedb_off,
Probe_col_up_R = tmp$upR,
Probe_col_up_G = tmp$upG,
Probe_col_up_B = tmp$upB,
Probe_col_down_R = tmp$downR,
Probe_col_down_G = tmp$downG,
Probe_col_down_B = tmp$downB,
Target_col_Up_up_R = tmp$UupR,
Target_col_Up_up_G = tmp$UupG,
Target_col_Up_up_B = tmp$UupB,
Target_col_Up_down_R = tmp$UdownR,
Target_col_Up_down_G = tmp$UdownG,
Target_col_Up_down_B = tmp$UdownB,
Target_col_R_up_R = tmp$RupR,
Target_col_R_up_G = tmp$RupG,
Target_col_R_up_B = tmp$RupB,
Target_col_R_down_R = tmp$RdownR,
Target_col_R_down_G = tmp$RdownG,
Target_col_R_down_B = tmp$RdownB,
Target_col_Down_up_R = tmp$DupR,
Target_col_Down_up_G = tmp$DupG,
Target_col_Down_up_B = tmp$DupB,
Target_col_Down_down_R = tmp$DdownR,
Target_col_Down_down_G = tmp$DdownG,
Target_col_Down_down_B = tmp$DdownB,
Target_col_L_up_R = tmp$LupR,
Target_col_L_up_G = tmp$LupG,
Target_col_L_up_B = tmp$LupB,
Target_col_L_down_R = tmp$LdownR,
Target_col_L_down_G = tmp$LdownG,
Target_col_L_down_B = tmp$LdownB,
Target_col_deg_Up_up = tmp$tarcolidxUu,
Target_col_deg_R_up = tmp$tarcolidxRu,
Target_col_deg_Down_up = tmp$tarcolidxDu,
Target_col_deg_L_up = tmp$tarcolidxLu,
Target_col_deg_Up_down = tmp$tarcolidxUd,
Target_col_deg_R_down = tmp$tarcolidxRd,
Target_col_deg_Down_down = tmp$tarcolidxDd,
Target_col_deg_L_down = tmp$tarcolidxLd,
ET_T_trial_on = tmp$startT - tmp$TarOn,
ET_T_Target_on = tmp$TarOn - tmp$TarOn,
ET_T_Target_off = tmp$TarOff - tmp$TarOn,
ET_T_Saccadecue_on = tmp$SaccueOn - tmp$TarOn,
ET_T_Saccadecue_off = tmp$SaccueOff - tmp$TarOn,
ET_T_Saccade_on = tmp$SaccOn - tmp$TarOn,
ET_T_Probe_fixation_on = tmp$probeFixed - tmp$TarOn,
ET_T_Probe_on = tmp$probeOn - tmp$TarOn,
ET_T_Response = tmp$RespOn - tmp$TarOn,
ET_T_InfoTime = tmp$InfoTime - tmp$TarOn,
RelTime = Time - tmp$TarOn
)]
# We set all values back to NA after a trial to avoid writinbg data from
# a previous trial into the next one
rm(tmp)
tmp <- maketmp()
rm('msgSplit')
# we're done with the trial, so set this to false again, until we find
# another trial start
foundNewTrl = FALSE
trial_end_reached = FALSE
}
}
}
############################################################################
######################## STEP 5: Massage Eyetracking Data ##################
############################################################################
## add mean and peak velocity per saccade
# set keys, so data.table can do a fast vector scan
setkey(samples, Time)
if (verbose) cat("\nAdding saccade velocity to samples Data.Table\n")
saccades <- as.data.table(curdat$saccades)
setkey(saccades, sttime, entime)
# extract only those saccades that happened during a trial
saccades <- saccades[sttime %in% samples[!is.na(ID), Time] |
entime %in% samples[!is.na(ID), Time],]
# melt the saccades data.table, so we have a column "Time", matching the one
# of the samples data.table
saccades <- melt(saccades, measure.vars = c('sttime', 'entime'),
variable.name = 'StartEnd', value.name = 'Time')
# enumerate the saccades
saccades[StartEnd == 'sttime', IthSaccadeThisSubject := .I]
# we don't need the endtime information, it's already included in samples'
# "Saccade" column
saccades <- saccades[StartEnd == 'sttime']
# remove all redundant columns
set(saccades, j = c('gstx', 'gsty', 'genx', 'geny', 'StartEnd'), value = NULL)
names(saccades) <- c(
'AverageVelocity', 'PeakVelocity', 'Time', 'IthSaccadeThisSubject')
# merge with samples
cols <- c('AverageVelocity', 'PeakVelocity', 'IthSaccadeThisSubject')
samples <- merge(samples, saccades, by = 'Time', all.x = TRUE)
# fill NAs with "last observation carried forward"
samples[Saccade == 1,
(cols) := lapply(.SD, nafill, type = "locf"), .SDcols = cols]
## add TTL to long format
if (verbose) cat("Adding TTL triggers to samples data.table\n")
curdat$ttl <- as.data.table(curdat$ttl)
names(curdat$ttl) <- c('Time', 'TTL')
samples = merge(samples, curdat$ttl, all.x = TRUE)
## remove temporary variables
rm('curdat', 'saccades')
## Remove the samples that weren't between a TrialStart and a TrialEnd
## message (and hence don't have a subject-Nr). Conserves RAM & HDD
setkey(samples, ID, Trial, Time)
samples <- na.omit(samples, cols = 'ID')
if (verbose) {cat('Detecting and expanding blinks\n')}
samplesB <- blink.detect(samples, SR_Blink_Col = 'Blink',
use_SR = TRUE, verbose = verbose,
expandblinks = 0)
if (verbose) {
cat('Performing cubic spline interpolation for dilation during blinks\n')
}
samplesB <- blink.interpolate(samplesB)
if (verbose) {
cat('Baseline correction of pupil dilation data [baseline-period:',
P$baselineperiod[1], '-', P$baselineperiod[2], 'ms]\n')
}
samples <- normalize(samplesB, samplesB$RelTime >= P$baselineperiod[1] &
samplesB$RelTime <= P$baselineperiod[2])
## We don't need the absolute time. So call the time relative to trial onset
## 'Time'. Likewise overwrite non-normalized Dilation with the normalized one.
samples[, ':='(Time = RelTime, Dil = DilN)]
## Downsample data to outHz (as specified at the top). My fast_downsample()
## function is vectorized and therefore about 600x faster than
## wmR::my_downsample() and pR::downsample(). However, the input data.table
## needs a column called 'Trial'. If you don't have trials, simply add this
## column and make it all <1>.
if (verbose) {cat('downsampling...\n')}
BY = colnames(samples)
BY = BY[!(BY %in% c('Dil', 'DilN', 'X', 'Y', 'Flags', 'Blink', 'DilDiff',
'Fixation', 'Saccade', 'TTL', 'RelTime', 'Baseline',
'IthSaccadeThisSubject', 'AverageVelocity',
'PeakVelocity', 'Time','Subject'))]
samples <- fast_downsample(samples, by = BY, Hz = P$outHz, useref = TRUE)
return(samples)
}
maketmp <- function(){
return(data.table(startT = NA_real_, badgaze = NA, badpress = NA,
TarOn = NA_real_, TarOff = NA_real_, SaccueOn = NA_real_,
SaccueOff = NA_real_, SaccOn = NA_real_,
probeFixed = NA_real_, probeOn = NA_real_, RespOn = NA_real_,
badgaze_T = NA_real_, badgaze = NA, badpress_T = NA_real_,
badpress = NA, InfoTime = NA_real_, SubjName= NA_character_,
SubjNr = NA_integer_, isFemale = NA, visioncorrected = NA,
age = NA_integer_, DominantEyeIsRight = NA,
trialNr = NA_integer_, change = NA_real_,
changedeg = NA_real_, probedUpperHalf = NA,
respondedUpperHalf = NA, responseCorrect = NA,
RT = NA_real_, saccue = NA_integer_, tarcue = NA_integer_,
threshEstim = NA_real_, threshSD = NA_real_,
trialCompleted = NA, trialIdx = NA_integer_,
T_trl_on = NA_real_, T_probe_on = NA_real_,
T_sacue_off = NA_real_, T_sacue_on = NA_real_,
T_targets_off = NA_real_, T_targets_on = NA_real_,
T_fix_on = NA_real_, sacc_rt = NA_real_, T_sacc_on = NA_real_,
T_probe_fix_on = NA_real_, fix_rt = NA_real_,
T_feedb_on = NA_real_, T_feedb_off = NA_real_,
endT = NA_real_, upR = NA_real_, upG = NA_real_,
upB = NA_real_, downR = NA_real_, downG = NA_real_,
downB = NA_real_, UupR = NA_real_, UupG = NA_real_,
UupB = NA_real_, UdownR = NA_real_, UdownG = NA_real_,
UdownB = NA_real_, RupR = NA_real_, RupG = NA_real_,
RupB = NA_real_, RdownR = NA_real_, RdownG = NA_real_,
RdownB = NA_real_, DupR = NA_real_, DupG = NA_real_,
DupB = NA_real_, DdownR = NA_real_, DdownG = NA_real_,
DdownB = NA_real_, LupR = NA_real_, LupG = NA_real_,
LupB = NA_real_, LdownR = NA_real_, LdownG = NA_real_,
LdownB = NA_real_, tarcolidxUu = NA_integer_,
tarcolidxRu = NA_integer_, tarcolidxDu = NA_integer_,
tarcolidxLu = NA_integer_, tarcolidxUd = NA_integer_,
tarcolidxRd = NA_integer_, tarcolidxDd = NA_integer_,
tarcolidxLd = NA_integer_))
}