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Updating eyetracking processing
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Updating eyetracking processing script to more effectively trim pupil data (providing more accurate missing data metric), completing first portion of Issue #4

Co-Authored-By: Sophie Forcier <[email protected]>
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@psokolhessner @sophieforcier
psokolhessner and sophieforcier committed Dec 10, 2024
1 parent 636ba68 commit 2e435db
Showing 1 changed file with 108 additions and 91 deletions.
199 changes: 108 additions & 91 deletions analysis/edi_et_processing.R
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Original file line number Diff line number Diff line change
Expand Up @@ -105,15 +105,94 @@ colnames(data_pupil) <- c(et_summary_data_column_names, timestamp_column_names);

#### STEP 5: SUBJECT LOOP ####
for (s in 1:number_of_subjects){

# for (s in 1:4){

# tic() # for timing this process
# s = 4; # for debugging

cat(sprintf('Starting subject CGE%03i.\n',subject_IDs[s]))
##### Loading Data #####
cat(sprintf('Starting subject EDI%03i.\n',subject_IDs[s]))
cat('Loading ET data... ')
raw_et_data = read.asc(etfn[s], samples = T, events = F)
cat('Done.\n')


cat('Loading behavioral data... ')
###### 1. Create Behavioral Event Timestamp Matrix ######
tmpdata = read.csv(rdmfn[s]);

event_timestamps = array(data = NA, dim = c(number_of_trials, length(timestamp_column_names)))
colnames(event_timestamps) <- timestamp_column_names;
event_timestamps = as.data.frame(event_timestamps)

beh_alignment_time = tmpdata$instructionStart[2]; # CHECK THIS

trial_index = is.finite(tmpdata$checkTrial) | is.finite(tmpdata$easy0difficult1);

event_timestamps$decision_start = tmpdata$choiceStart[trial_index];
event_timestamps$decision_end = tmpdata$choiceEnd[trial_index];
event_timestamps$outcome_start = tmpdata$outcomeStart[trial_index];
event_timestamps$outcome_end = tmpdata$outcomeEnd[trial_index];
event_timestamps$iti_end = tmpdata$itiEnd[trial_index];

event_timestamps[!(is.finite(tmpdata$choiceMade[trial_index])),] = NA;

event_timestamps = event_timestamps - beh_alignment_time; # align the behavioral data to the same moment
event_timestamps = event_timestamps * 1000; # into milliseconds, like eyetracking
cat('Done.\n')


###### 2. Identify and extract timestamp for practice start from ET file ######
cat('Aligning timestamps... ')
et_file_connection = file(etfn[s],'r') # open the file connection to the ASC file.

msgs = ''; # where we'll store messages
number_of_messages = 0; # set our counter

while ( TRUE ) {
line = readLines(et_file_connection, n = 1) # read a line of the file
if ( length(line) == 0 ) { # if we have read the full file and there are no more lines left, stop
break
}
# print(line) # for debugging
if ('MSG' == substr(line, 1, 3)) { # if this is a 'message' line...
number_of_messages = number_of_messages + 1; # increment the # of messages we've found
# print(line) # for debugging
msgs[number_of_messages] = line; # store the message
}
}

close(et_file_connection) # close the file connection

for (m in 1:number_of_messages){
if ('Practice Instructions Shown' == substr(msgs[m], nchar(msgs[m])-26, nchar(msgs[m]))){ # identify the msg with this text
first_ind = 5; # the first digit is always 5 characters in
last_ind = nchar(msgs[m])-68; # the "practice text shown" is always 18 characters long, preceded by a 1 char space; so last number is 20 back from end
et_alignment_time = as.numeric(substr(msgs[m], first_ind, last_ind)) # pull out the start time from that msg
}
}

####### 3. Identify and extract Validation Information/metrics from ET file #######
val_msgs = grep('VALIDATION HV', msgs);
last_val_msg = msgs[val_msgs[length(val_msgs)]];

validation_average_error = as.numeric(substr(sub(".*ERROR ","",last_val_msg),1,5));
val_msg_subset = sub(" max.*","",last_val_msg);
validation_max_error = as.numeric(substr(val_msg_subset, nchar(val_msg_subset)-4, nchar(val_msg_subset)))
val_msg_subset = sub(" ERROR.*","",last_val_msg);
validation_str = substr(val_msg_subset, nchar(val_msg_subset)-3,nchar(val_msg_subset))
if (validation_str == 'GOOD'){
validationG0F1P2 = 0
} else if (validation_str == 'FAIR'){
validationG0F1P2 = 1
} else if (validation_str == 'POOR'){
validationG0F1P2 = 2
}

cat(sprintf('alignment timestamp = %i... ', et_alignment_time))

cat('Done.\n')


# Settings:
# 1000 Hz sample rate
# 1280 (w) x 1024 (h) screen size)
Expand Down Expand Up @@ -148,6 +227,20 @@ for (s in 1:number_of_subjects){

pupil_data_raw = raw_et_data$raw$ps;
time_data = raw_et_data$raw$time; # UNCORRECTED! in milliseconds

###### 0. Align & Trim Pupil Data to the Relevant Timeline ######

# Align
time_data = time_data - et_alignment_time; # Correct all timestamps to be relative to this moment

# Trim
time_buffer = 1000;

ind_to_keep = (time_data > (event_timestamps$decision_start[1] - time_buffer)) &
(time_data < (event_timestamps$iti_end[number_of_trials] + time_buffer))

time_data = time_data[ind_to_keep];
pupil_data_raw = pupil_data_raw[ind_to_keep];

###### 1. Identify blink points ######
missing_points_ind = which(is.na(pupil_data_raw)); # index of all missing datapoints
Expand Down Expand Up @@ -241,84 +334,9 @@ for (s in 1:number_of_subjects){

downsampled_et_data = cbind(time_data_downsampled, pupil_data_extend_interp_smooth_mm_downsampled);

###### 9. Identify and extract timestamp for practice start from ET file ######
cat('Aligning timestamps... ')
et_file_connection = file(etfn[s],'r') # open the file connection to the ASC file.

msgs = ''; # where we'll store messages
number_of_messages = 0; # set our counter

while ( TRUE ) {
line = readLines(et_file_connection, n = 1) # read a line of the file
if ( length(line) == 0 ) { # if we have read the full file and there are no more lines left, stop
break
}
# print(line) # for debugging
if ('MSG' == substr(line, 1, 3)) { # if this is a 'message' line...
number_of_messages = number_of_messages + 1; # increment the # of messages we've found
# print(line) # for debugging
msgs[number_of_messages] = line; # store the message
}
}

close(et_file_connection) # close the file connection

for (m in 1:number_of_messages){
if ('Practice Instructions Shown' == substr(msgs[m], nchar(msgs[m])-26, nchar(msgs[m]))){ # identify the msg with this text
first_ind = 5; # the first digit is always 5 characters in
last_ind = nchar(msgs[m])-68; # the "practice text shown" is always 18 characters long, preceded by a 1 char space; so last number is 20 back from end
et_alignment_time = as.numeric(substr(msgs[m], first_ind, last_ind)) # pull out the start time from that msg
}
}

####### 10. Identify and extract Validation Information/metrics from ET file #######
val_msgs = grep('VALIDATION HV', msgs);
last_val_msg = msgs[val_msgs[length(val_msgs)]];

validation_average_error = as.numeric(substr(sub(".*ERROR ","",last_val_msg),1,5));
val_msg_subset = sub(" max.*","",last_val_msg);
validation_max_error = as.numeric(substr(val_msg_subset, nchar(val_msg_subset)-4, nchar(val_msg_subset)))
val_msg_subset = sub(" ERROR.*","",last_val_msg);
validation_str = substr(val_msg_subset, nchar(val_msg_subset)-3,nchar(val_msg_subset))
if (validation_str == 'GOOD'){
validationG0F1P2 = 0
} else if (validation_str == 'FAIR'){
validationG0F1P2 = 1
} else if (validation_str == 'POOR'){
validationG0F1P2 = 2
}

cat(sprintf('alignment timestamp = %i... ', et_alignment_time))

time_data = time_data - et_alignment_time; # Correct all timestamps to be relative to this moment
downsampled_et_data[,1] = downsampled_et_data[,1] - et_alignment_time # correct downsampled timestamps too!

cat('Done.\n')

##### Conduct basic trial-level analyses ######
###### 1. Create Behavioral Event Timestamp Matrix ######
tmpdata = read.csv(rdmfn[s]);

event_timestamps = array(data = NA, dim = c(number_of_trials, length(timestamp_column_names)))
colnames(event_timestamps) <- timestamp_column_names;
event_timestamps = as.data.frame(event_timestamps)

beh_alignment_time = tmpdata$pracStartTxt.started[3];

trial_index = is.finite(tmpdata$realChoiceResp.started);

event_timestamps$decision_start = tmpdata$realChoiceResp.started[trial_index];
event_timestamps$decision_end = tmpdata$isiRealFix.started[trial_index];
event_timestamps$outcome_start = tmpdata$isiRealFix.stopped[trial_index];
event_timestamps$outcome_end = tmpdata$itiRealFix.started[trial_index];
event_timestamps$iti_end = tmpdata$itiRealFix.stopped[trial_index];

event_timestamps[!(is.finite(tmpdata$choices[trial_index])),] = NA;

event_timestamps = event_timestamps - beh_alignment_time; # align the behavioral data to the same moment
event_timestamps = event_timestamps * 1000; # into milliseconds, like eyetracking

###### 2. Calculate Summary Metrics of Pupillometry #####
###### Calculate Summary Metrics of Pupillometry #####
cat('Calculating summary pupillometry measures... ')

et_summary_stats = array(data = NA, dim = c(number_of_trials, length(et_summary_data_column_names)))
Expand Down Expand Up @@ -389,7 +407,7 @@ for (s in 1:number_of_subjects){
number_of_missing_trials_proc = sum(is.na(et_summary_stats$wind1_predisp_onset_mean));
fraction_of_missing_trials_proc = number_of_missing_trials_proc/number_of_trials;

cat(sprintf('CGE%03i: RAW missing %i samples (%.1f%%); %i blinks. PROCESSED missing %i samples (%.1f%%). %i trial(s) (%.0f%%) not analyzed for missing data.\n',
cat(sprintf('EDI%03i: RAW missing %i samples (%.1f%%); %i blinks. PROCESSED missing %i samples (%.1f%%). %i trial(s) (%.0f%%) not analyzed for missing data.\n',
subject_IDs[s], number_of_missing_samples_raw, fraction_of_missing_samples_raw*100, number_of_blinks,
number_of_missing_samples_proc, fraction_of_missing_samples_proc*100, number_of_missing_trials_proc,
fraction_of_missing_trials_proc*100))
Expand Down Expand Up @@ -433,36 +451,35 @@ for (s in 1:number_of_subjects){
'pupil_data_extend_interp_smooth'
))

pdf(sprintf('%s/plots/cge%03i_processed_pupil_plot.pdf',config$path$data$processed, subject_IDs[s]))
plot(pupil_data_extend_interp_smooth_mm, type = 'l', main = sprintf('Processed pupil data for CGE%03i', subject_IDs[s]),
pdf(sprintf('%s/plots/edi%03i_processed_pupil_plot.pdf',config$path$data$processed, subject_IDs[s]))
plot(pupil_data_extend_interp_smooth_mm, type = 'l', main = sprintf('Processed pupil data for EDI%03i', subject_IDs[s]),
xlab = 'milliseconds', ylab = 'pupil diameter (mm)')
dev.off()

data_pupil = rbind(data_pupil, cbind(et_summary_stats, event_timestamps));

dir.create(file.path(config$path$data$processed, sprintf('edi%03i',subject_IDs[s])), showWarnings = FALSE)

save(pupil_data_extend_interp_smooth_mm, time_data, et_summary_stats, event_timestamps, blink_data,
file=sprintf('%s/cge%03i/cge%03i_et_processed_%s.RData',config$path$data$processed,subject_IDs[s],subject_IDs[s],format(Sys.Date(), format="%Y%m%d")))
file=sprintf('%s/edi%03i/edi%03i_et_processed_%s.RData',config$path$data$processed,subject_IDs[s],subject_IDs[s],format(Sys.Date(), format="%Y%m%d")))
save(downsampled_et_data, event_timestamps, et_summary_stats,
file=sprintf('%s/cge%03i/cge%03i_et_processed_downsampled_%s.RData',config$path$data$processed,subject_IDs[s],subject_IDs[s],format(Sys.Date(), format="%Y%m%d")))
file=sprintf('%s/edi%03i/edi%03i_et_processed_downsampled_%s.RData',config$path$data$processed,subject_IDs[s],subject_IDs[s],format(Sys.Date(), format="%Y%m%d")))

cat(sprintf('Finished with subject CGE%03i.\n\n',subject_IDs[s]))
cat(sprintf('Finished with subject EDI%03i.\n\n',subject_IDs[s]))
}


setwd(config$path$data$processed); # set path to processed to save out the summary data

write.csv(data_pupil, file=sprintf('cge_processed_eyetracking_data_%s.csv',format(Sys.Date(), format="%Y%m%d")),
write.csv(data_pupil, file=sprintf('edi_processed_eyetracking_data_%s.csv',format(Sys.Date(), format="%Y%m%d")),
row.names = F);
write.csv(pupil_QA_metrics, file = sprintf('cge_pupil_QA_metrics_%s.csv',format(Sys.Date(), format="%Y%m%d")),
write.csv(pupil_QA_metrics, file = sprintf('edi_pupil_QA_metrics_%s.csv',format(Sys.Date(), format="%Y%m%d")),
row.names = F);

setwd(config$path$data$raw); # reset the path to raw.

timeElapsed = toc();
timeElapsed = toc(quiet = T);

cat(sprintf('Processing took %s.\n',substr(timeElapsed$callback_msg, start = 0, stop = nchar(timeElapsed$callback_msg)-8)))
cat('Finished processing eye-tracking data.\n\n\n')

# For Future Eye-Tracking Analysis Development ####
# - store baseline-corrected pupil diameter traces for event segments (i.e. dec; otc)

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