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No Unique Magnocellular Facilitation in Parafoveal Processing: A Combined EEG and Eye Tracking Study.

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  1. [1] § MATERIALS AND METHODS › EEG Preprocessing ↔ Codes/Eye/preprocessing_EMdata_sf_OSF.R, lines 323–377 · score 0.56 · baseline corrected, EEG signals, MATLAB, onset, preprocessing, EYE
  2. [2] § MATERIALS AND METHODS › EEG Preprocessing ↔ Codes/Eye/preprocessing_EMdata_color_OSF.R, lines 344–390 · score 0.56 · baseline corrected, EEG signals, MATLAB, onset, preprocessing, EYE

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The authors' code

R · 378 lines · 19 KB · no license · 1 match

  3. #### 0. Choose your working directory ####
  4. #In this folder you need to have Fixation report, Interest Area report and Saccade report in .csv format.
  5. #The reports were obtained with DataViewer (SR Research), after having performed the 4 stages cleaning procedure (stage 4: 50, 800ms)
  6. #and having set up 2 Messages.
  7. #library()
  8. library(stringr)
  9. getwd()
  10. setwd("S:/GRF_magno/preview/data/eye data/formal/sf/out/sf/Output")
  12. #### 1. Load Fixation Report ####
  13. FixRep = read.csv("fix_report.csv", sep = ",")
  14. head(FixRep)
  15. str(FixRep)
  18. b <-FixRep$CURRENT_FIX_INTEREST_AREA_ID #at the moment the values in CURRENT_FIX_INTEREST_AREA_ID vary in length. They could be comprised of 0, 1 or 2 digits (e.g., ., 3, 11)
  19. FixRep$CURRENT_FIX_INTEREST_AREA_ID<- sprintf("%02s", b) #with this command, the values in CURRENT_FIX_INTEREST_AREA_ID will all be comprised of 2 digits (e.g., " .", " 3", "11")
  20. FixRep$CURRENT_FIX_INTEREST_AREA_ID
  22. FixRep$ID2 <- paste(FixRep$RECORDING_SESSION_LABEL, FixRep$CURRENT_FIX_INTEREST_AREA_ID, sep="") #with this command we create a unique idenifier for participant item and interest area.
  23. FixRep$ID2
  24. ### Consider only the EXPERIMENTAL trials ###
  25. FixRep <- FixRep[!grepl("res",FixRep$preview_stim),]
  26. levels(droplevels(as.factor(FixRep$preview_stim)))
  27. write.table(FixRep, file = "FixRep.xls", row.names=FALSE)
  29. #### 2. CONSECUTIVE FIXATIONS ####
  30. FixRepMod <- FixRep
  31. FixRepMod$PREVIOUS_FIX_INTEREST_AREA_ID <- as.numeric(as.character(FixRepMod$PREVIOUS_FIX_INTEREST_AREA_ID))
  32. FixRepMod$CURRENT_FIX_INTEREST_AREA_ID <- as.numeric(as.character(FixRepMod$CURRENT_FIX_INTEREST_AREA_ID))
  33. FixRepMod$NEXT_FIX_INTEREST_AREA_ID <- as.numeric(as.character(FixRepMod$NEXT_FIX_INTEREST_AREA_ID))
  36. # #If you want to double check the new column we have created, run the following line of code:
  37. write.table(FixRepMod, file = "FixRepMod.xls", row.names=FALSE)
  39. #### 3. Consider only pre-target and target words (FixRep) ####
  40. FixRepMod2 <- FixRepMod[grepl("target_small",FixRepMod$CURRENT_FIX_INTEREST_AREA_LABEL),]
  42. #### 4. Load Interest Area Report ####
  43. IARep = read.csv("IA_report.csv", sep = ",")
  44. head(IARep)
  45. str(IARep)
  48. d <-IARep$IA_ID #at the moment the values in IA_ID vary in length. They could be comprised of 1 or 2 digits (e.g., 1, 12)
  49. IARep$IA_ID<- sprintf("%02s", d) #with this command, the values in IA_ID will all be comprised of 2 digits (e.g., 01, 12)
  50. IARep$IA_ID
  52. IARep$ID2 <- paste(IARep$RECORDING_SESSION_LABEL, IARep$IA_ID, sep="") #with this command we create a unique idenifier for participant item and interest area.
  53. IARep$ID2
  55. ### Consider only the EXPERIMENTAL trials ###
  56. #Note: the column 'trial_aborted' different from zero means that the fixation on the initial cross on the left side of the screen could not be registered by the Eyelink, so the trial was presented again.
  57. IARep <- IARep[!grepl("target",IARep$preview_stim),]
  58. #levels(droplevels(IARep$preview_stim))
  59. levels(droplevels(as.factor(IARep$preview_stim)))
  61. #### 5. Consider only pre-target and target words (IARep) ####
  62. IARep2 <- IARep[grepl("target_small",IARep$IA_LABEL),]
  64. #### 6. Merge the fixation report and the interest area report based on the common ID2 but keeping all the rows from the fixation report ####
  65. RepMod <- merge(FixRepMod2, IARep2, all.x=TRUE)
  66. RepMod <- RepMod[order(RepMod$RECORDING_SESSION_LABEL, RepMod$trial.no, RepMod$CURRENT_FIX_INDEX),]
  68. #write.table(RepMod, file = "RepMod.xls", row.names=FALSE)
  70. #### 7. BLINKS ####
  71. #Identify those sequences wehere there was a BLINK in any of the areas of the pair during first pass reading.
  72. RepMod$BlinksRemove <- ifelse(RepMod$CURRENT_FIX_BLINK_AROUND!="NONE" & RepMod$CURRENT_FIX_INTEREST_AREA_RUN_ID ==1, 1, 0)
  74. #### 8. WORD SKIPPING ####
  75. #Identify SKIPS in any of the areas of the pair during first pass reading.
  76. RepMod$Skip <- ifelse(RepMod$IA_SKIP == 1 & RepMod$CURRENT_FIX_INTEREST_AREA_RUN_ID ==1, 1, 0)
  78. RepModified<-RepMod
  82. #### 11. SELECTION OF INTERESTING FIXATIONS (part 1) ####
  83. #CLEAN the data from all the fixations that we cannot keep based on the previous code and criteria.
  84. RepClean <-RepModified
  86. #First we extract only the pretarget and target words for the first pair.
  87. RepCleanFirst <- RepClean
  88. RepCleanFirst <- RepCleanFirst[grepl("target_small",RepCleanFirst$CURRENT_FIX_INTEREST_AREA_LABEL),]
  89. #Then we select to keep only those observations where there was not a skip (i.e., with value of zero).
  90. RepCleanFirst <-RepCleanFirst[(RepCleanFirst$Skip==0),]
  91. RepCleanFirst <-RepCleanFirst[(RepCleanFirst$IA_FIRST_FIX_PROGRESSIVE==1),]
  93. #If you want to check the file
  94. write.table(RepCleanFirst, file = "RepCleanFirst.xls", row.names=FALSE)
  97. #Now merge pretarget and target words for first and second pairs.
  98. RepClean2 <-RepCleanFirst
  99. #And order by participant, presentation order of the trials and fixation index.
  101. RepClean2 <- RepClean2[with(RepClean2, order(RECORDING_SESSION_LABEL, CURRENT_FIX_INDEX)),]
  103. #Save the file
  104. #write.table(RepClean2, file = "RepClean2.xls", row.names=FALSE)
  106. #Keep only first-pass reading
  107. RepClean3 <-RepClean2[grepl("1",RepClean2$CURRENT_FIX_INTEREST_AREA_RUN_ID),]
  108. RepClean3<-RepClean3[grepl("1",RepClean3$CURRENT_FIX_RUN_INDEX),]
  111. #write.table(RepClean3, file = "RepClean3.xls", row.names=FALSE)
  113. #Select unique IA_LABEL per participant per trial. So, only one pretarget and one target per pair.
  114. RepClean4<-RepClean3
  117. results_target <-RepClean4[grepl("target_small",RepClean4$IA_LABEL),]
  119. #Save the file
  120. write.table(results_target, file = "EMdata_target.xls", row.names=FALSE)
  122. #You can check how many trials per participant per condition.
  123. B <-as.data.frame.matrix(table(results_target$RECORDING_SESSION_LABEL, results_target$targte_trigger))
  124. colMeans(B) #mean of each condition
  125. rowMeans(B) #mean of each participant
  127. mean(as.matrix(B)) #mean of the whole data frame (all participants, all conditions).
  129. #Save the file
  130. write.table(B, file = "ObservationsPerPptCond.xls", row.names=TRUE)
  132. ####################################################################################################################################################
  133. #### #### 11. SELECTION OF INTERESTING FIXATIONS (part 2) ####
  134. #After the cleaning of the data, follow this script in order to synchronize the events as recorded in the eye-tracking system (Eyelink 1000) and
  135. #in the EEG recording (NeuroScan 4.5).
  137. #Create files where instead of the TTL/condition, we have the exact code that represents nitem/trial + condition.
  139. #### 11.0. Set your working directory ####
  140. setwd("S:/GRF_magno/preview/data/eye data/formal/sf/out/sf/Output")
  142. #### 11.1. Open the Message report as extracted wih DataViewer ####
  143. MsgRep = read.csv("msg_report.csv", sep = ",")
  145. ### Consider only the EXPERIMENTAL trials ###
  146. #Note: the column 'trial_aborted' different from zero means that the fixation on the initial cross on the left side of the screen could not be registered by the Eyelink, so the trial was presented again.
  148. MsgRep <- MsgRep[!grepl( "res" , MsgRep$preview_trigger ), ]
  149. levels(droplevels(as.factor(MsgRep$preview_stim)))
  151. head(MsgRep)
  152. str(MsgRep)
  154. #### 11.2. Keep only the rows of the Message report that recorded the sending of TTLs ####
  155. #This will be important for the merging with the event file from the EEG recording.
  156. MsgRep <- MsgRep[grepl( "TRIAL_PREVIEW|KEYWORD 10|KEYWORD 99|KEYWORD 101|KEYWORD 102|KEYWORD 103|KEYWORD 104|KEYWORD 105|KEYWORD 106|KEYWORD 111|KEYWORD 112|KEYWORD 113|KEYWORD 114|KEYWORD 115|KEYWORD 116|KEYWORD 200|CROSSED|CHANGED" , MsgRep$CURRENT_MSG_TEXT ), ]
  157. #Note: the number of observations to be correct needs to be equal to the Number of participants x Number of experimental trials x Number TTLon/off (2).
  159. write.table(MsgRep, "MsgRep_keyword.csv",sep=",")
  161. #### 11.3. Open EvF for each participant and create one big new EvF of all participants ####
  162. setwd("S:/GRF_magno/preview/data/eeg/sf/out/event") #only store the relevant files in this folder
  163. files <- list.files(pattern = ".csv")
  164. library(data.table)
  166. data_list <- list() #this should be equal to the number of participants being analysed.
  168. for(i in 1:length(files)){
  169. file_name <- files[i]
  170. d = fread(file_name, sep=",")
  172. #Change columns names
  173. colnames(d) <- c("number","latency","type","duration","order")
  175. #Split the string by "_"
  176. filenames_vec <- strsplit(file_name, split = "_")[[1]]
  178. #Create new column to store the information
  179. d$RECORDING_SESSION_LABEL <- filenames_vec[1]
  180. data_list[[i]] <- d
  181. }
  182. EvF_all_data <- rbindlist(data_list)
  183. fwrite(EvF_all_data, "S:/GRF_magno/preview/data/eye data/formal/sf/out/sf/Output/EvF_all_data.csv")
  185. #### 11.4. Set back the working directory for synchronization and open Event file ####
  186. setwd("S:/GRF_magno/preview/data/eye data/formal/sf/out/sf/Output")
  187. EvF_col = read.csv("EvF_all_data.csv", sep = ",")
  188. head(EvF_col)
  189. str(EvF_col)
  190. #The column 'Presentation' refers to the time in the EEG recording when a TTL was recorded.
  192. ### Keep in the Event file only the experimental trials ###
  193. EvF_col <- EvF_col[EvF_col$type != "200",]
  194. head(EvF_col)
  197. write.table(EvF_col, "EvF_col.xls",sep="\t", row.names=FALSE)
  200. #### 11.5. Consider only the TTLon in the EvF ####
  201. #So only the values 1,2,3,4,5,6. No, the TTLoff 101,102,103,104,105,106.
  202. setwd("S:/GRF_magno/preview/data/eye data/formal/sf/out/sf/Output")
  203. MsgRep = read.csv("MsgRep_keyword.csv", sep = ",")
  205. # MsgRep <- MsgRep [grepl( "KEYWORD 111", MsgRep$CURRENT_MSG_TEXT )|grepl( "KEYWORD 222", MsgRep$CURRENT_MSG_TEXT ), ] #the number of observations here should be equal to Number of participants participants by Number of experimental trials.
  206. # MsgRep$CURRENT_MSG_TEXT#the number of observations here should be equal to Number of participants participants by Number of experimental trials.
  208. MsgRep <- MsgRep [grepl( "KEYWORD 111|KEYWORD 112|KEYWORD 113|KEYWORD 114|KEYWORD 115|KEYWORD 116", MsgRep$CURRENT_MSG_TEXT ), ] #the number of observations here should be equal to Number of participants participants by Number of experimental trials.
  209. MsgRep$CURRENT_MSG_TEXT#the number of observations here should be equal to Number of participants participants by Number of experimental trials.
  213. setwd("S:/GRF_magno/preview/data/eye data/formal/sf/out/sf/Output")
  214. EvF_col = read.csv("EvF_all_data.csv", sep = ",")
  216. head(EvF_col)
  219. ### Create an Identifier for both files
  221. EvF_col$trials_run <- as.numeric(as.character(EvF_col$order))
  222. EvF_col$trials_run<- sprintf("%03d", EvF_col$order)
  224. EvF_col$Identifier <- paste(EvF_col$RECORDING_SESSION_LABEL, EvF_col$trials_run, sep="")
  226. head(EvF_col)
  227. head(EvF_col$Identifier)
  231. names(MsgRep)
  232. MsgRep$trial.no<- as.numeric(as.character(MsgRep$trial.no))
  233. MsgRep$trial.no<- sprintf("%03d", MsgRep$trial.no)
  235. MsgRep$Identifier <- paste(MsgRep$RECORDING_SESSION_LABEL,MsgRep$trial.no, sep="")
  236. head(MsgRep)
  237. head(MsgRep$Identifier)
  238. MsgRep <- MsgRep[!grepl( "res" , MsgRep$preview_stim ), ]
  239. levels(droplevels(as.factor(MsgRep$preview_stim)))
  243. #### 11.6. Merge Message report with the EvF, keeping all the columns ####
  244. MSGmergeEvF <- merge(EvF_col, MsgRep, by= "Identifier", all.y=TRUE)
  245. head(MSGmergeEvF$Identifier)
  247. MSGmergeEvF2 <- MSGmergeEvF
  249. MSGmergeEvF2 <- MSGmergeEvF2[grepl( "111|112|113|114|115|116", MSGmergeEvF2$type ), ] #the number of observations here should be equal to Number of participants participants by Number of experimental trials.
  251. ### Save the new file.
  252. write.table(MSGmergeEvF2, "MSGmergeEvF2.txt",sep="\t", row.names=FALSE)
  254. #### 11.7. Load the files obtained from the preprocessing of EM data ####
  255. #Please note that here you need to manually save the files "EMdata_pretarget.xls" and "EMdata_target.xls" as .csv files.
  256. setwd("S:/GRF_magno/preview/data/eye data/formal/sf/out/sf/Output")
  257. Rep_target = read.csv("EMdata_target.csv", sep = ",")
  260. Rep_target$trial.no<- as.numeric(as.character(Rep_target$trial.no))
  261. Rep_target$trial.no<- sprintf("%03d", Rep_target$trial.no)
  264. Rep_target$Identifier <- paste(Rep_target$RECORDING_SESSION_LABEL, Rep_target$trial.no, sep="")
  265. Rep_target$Identifier
  266. Rep_target$ID3 <- paste(Rep_target$RECORDING_SESSION_LABEL, sep="")
  267. Rep_target$ID3
  268. write.table(Rep_target, "Rep_target.csv",sep=",")
  270. #### 11.8. Merge the files from EM preprocessing with messageReport/Evf ####
  271. ###Merge each of these files with the MSGmergeEvF, in order to have only the trials needed.
  272. #It will give all the columns from Rep_XXXtarget, but only the specified columns from MSGmergeEvF.
  273. #Only the rows in common (with same ID3 in the 2 files) will be included.
  277. MSGmergeEvF3 <-MSGmergeEvF2[,c("trials_run","order","latency", "type","duration","Identifier","CURRENT_MSG_BLINK_DURATION","CURRENT_MSG_BLINK_END","CURRENT_MSG_BLINK_INDEX","CURRENT_MSG_BLINK_START","CURRENT_MSG_FIX_DURATION","CURRENT_MSG_FIX_END","CURRENT_MSG_FIX_INDEX", "CURRENT_MSG_FIX_START","CURRENT_MSG_FIX_X","CURRENT_MSG_FIX_Y","CURRENT_MSG_INDEX","CURRENT_MSG_INTEREST_AREAS","CURRENT_MSG_INTEREST_AREA_INDEX","CURRENT_MSG_INTEREST_AREA_LABEL","CURRENT_MSG_IS_RT_END","CURRENT_MSG_IS_RT_START","CURRENT_MSG_LABEL","CURRENT_MSG_SAC_AMPLITUDE","CURRENT_MSG_SAC_AVG_VELOCITY","CURRENT_MSG_SAC_DURATION","CURRENT_MSG_SAC_END_TIME","CURRENT_MSG_SAC_END_X","CURRENT_MSG_SAC_END_Y","CURRENT_MSG_SAC_INDEX","CURRENT_MSG_SAC_PEAK_VELOCITY","CURRENT_MSG_SAC_START_TIME","CURRENT_MSG_SAC_START_X","CURRENT_MSG_SAC_START_Y","CURRENT_MSG_TEXT","CURRENT_MSG_TIME","CURRENT_MSG_X_POSITION","CURRENT_MSG_Y_POSITION","preview_stim")]
  278. Rep_target_code <- merge(Rep_target, MSGmergeEvF2, by="Identifier", all.x=TRUE)
  279. Rep_target_code <- Rep_target_code[order(Rep_target_code$RECORDING_SESSION_LABEL,Rep_target_code$latency),]
  281. write.table(Rep_target_code, "Rep_target_code.txt",sep="\t", row.names=FALSE)
  283. #### 11.9. Create three new columns (first saccade onset, first fixation onset, first fixation duration) for each IA file ####
  284. #Divide the values by 1000, in order to transform the values into seconds, then multiply by your sampling rate (in our case 1000 Hz).
  285. #This is important to see these values in the EEG epochs of 1 second. (NOTE: The column Presentation, in the
  286. #EEG event file is in datapoints, so we need to transform the EMs measures into the same unit - in our case milliseconds.)
  290. #2)first fixation onset
  291. Rep_target_code$IA_FIRST_FIXATION_TIME <- as.numeric(as.character(Rep_target_code$IA_FIRST_FIXATION_TIME))
  292. Rep_target_code$first_fixation_onset <- (Rep_target_code$IA_FIRST_FIXATION_TIME - Rep_target_code$CURRENT_MSG_TIME)
  293. head(Rep_target_code$first_fixation_onset)
  294. #3)first fixation duration - We have it already
  295. head(Rep_target_code$IA_FIRST_FIXATION_DURATION)
  296. write.table(Rep_target_code, "Rep_target_code2.txt",sep="\t", row.names=FALSE)
  298. #### 11.10. Create a new file only with the columns needed. ####
  301. Rep_target_complete <- Rep_target_code[,c("RECORDING_SESSION_LABEL","trial.no.x", "targte_trigger.x","order", "CURRENT_MSG_TEXT","latency","first_fixation_onset", "IA_LABEL","IA_ID",
  302. "CURRENT_FIX_INTEREST_AREA_RUN_ID","IA_LEGAL", "IA_FIRST_FIX_PROGRESSIVE", "IA_FIRST_FIXATION_DURATION",
  303. "IA_FIRST_RUN_DWELL_TIME","IA_FIXATION_COUNT","preview_stim.x")]
  304. head(Rep_target_complete)
  306. write.table(Rep_target_complete, file = "Rep_target_complete.txt",sep="\t",row.names=FALSE)
  308. #### 11.11. Now we need one file with saccade and fixation onset based on original Presentation time ####
  309. #Create event files for each IA, with Fixation onset as Presentation and Fixation duration as Latency.
  310. #It works with the correct order of TTL
  313. Rep_target <- Rep_target_complete
  314. colnames(Rep_target)
  315. colnames(Rep_target)[6] <- "urPresentation" #original Presentation time of TTLon
  316. #Rep_target$urPresentation<- as.numeric(as.character(Rep_target$urPresentation))
  317. #Rep_target$first_fixation_onset<- as.numeric(as.character(Rep_target$first_fixation_onset))
  318. Rep_target$TimeLockFixOnset <-Rep_target$urPresentation + Rep_target$first_fixation_onset
  319. head(Rep_target)
  321. write.table(Rep_target, file = "Rep_target.txt",sep="\t",row.names=FALSE)
  323. #### 13.12. Now we need one EvF for each participant, as we will need to load it separately for each participant in the EEG data ####
  324. ###Save in separate files according to participant's number
  326. setwd("S:/GRF_magno/preview/data/eye data/formal/sf/out/sf/Output/event_syn")
  327. for (ppt in unique(Rep_target$RECORDING_SESSION_LABEL)) {
  328. write.csv(Rep_target[Rep_target$RECORDING_SESSION_LABEL == ppt,], file = paste0("EvF_target_", ppt, ".csv"),row.names=FALSE)
  329. }
  331. #### 11.13. FINAL EvF TO IMPORT IN MATLAB ####
  332. #Combine pretarget and target files in a format that can be used for Baseline Correction of EEG signal
  334. colnames(Rep_target)
  335. #the names of the columns need to be exactly the same, as well as their class
  338. class(Rep_target$IA_SPILLOVER)
  339. head(Rep_target$IA_SPILLOVER)
  340. Rep_target$IA_SPILLOVER <- as.integer(as.character(Rep_target$IA_SPILLOVER))
  341. head(Rep_target$IA_SPILLOVER)
  343. newEvF_preT <- Rep_target
  344. head(newEvF_preT)
  346. #Order by TimeLockFixOnset
  347. newEvF_preT <- newEvF_preT[order(newEvF_preT$RECORDING_SESSION_LABEL,newEvF_preT$TRIAL_INDEX,newEvF_preT$TimeLockFixOnset),] #
  348. head(newEvF_preT)
  350. #Create new Column BaseLineOnset with prefonset and tfonset
  351. newEvF_preT$BaseLineOnset <-ifelse(grepl("KEYWORD 111",newEvF_preT$CURRENT_MSG_TEXT), "unnonrep",
  352. ifelse(grepl("KEYWORD 112",newEvF_preT$CURRENT_MSG_TEXT), "unrep",
  353. ifelse(grepl("KEYWORD 113",newEvF_preT$CURRENT_MSG_TEXT), "pnonrep",
  354. ifelse(grepl("KEYWORD 114",newEvF_preT$CURRENT_MSG_TEXT), "prep",
  355. ifelse(grepl("KEYWORD 115",newEvF_preT$CURRENT_MSG_TEXT), "mnonrep",
  356. ifelse(grepl("KEYWORD 116",newEvF_preT$CURRENT_MSG_TEXT), "mrep",NA))))))
  358. newEvF_preT <- newEvF_preT[grepl("KEYWORD 111|KEYWORD 112|KEYWORD 113|KEYWORD 114|KEYWORD 115|KEYWORD 116",newEvF_preT$CURRENT_MSG_TEXT), ]
  359. head(newEvF_preT)
  361. write.table(newEvF_preT, file = "newEvF_preT_complete.txt",sep="\t",row.names=FALSE)
  363. ###Save in separate files according to participant's number
  364. for (ppt in unique(newEvF_preT$RECORDING_SESSION_LABEL)) {
  365. write.csv(newEvF_preT[newEvF_preT$RECORDING_SESSION_LABEL == ppt,], file = paste0("newEvF_preT_", ppt, ".csv"),row.names=FALSE)
  366. }
  368. #### The format of the EvF in NeuroScan ####
  369. #The EvF in Neuroscan has a particular format and contains 6 columns:
  370. #1st: ordinal number of the event (1st event =1, 2nd event =2, etc.). NOTE: It has to start with 1 and have all the sequence of numbers.
  371. #2nd: actual event code. Thus you can specify multiple codes in the file. So, it can be TTL, condition, CODE, etc.
  372. #3rd: The next 3 columns can be used to associate response information with the event; this column is the code of the response itself.
  373. #4th: This column can be used to show the accuracy of the response (1=correct response; 0=incorrect response; -1=incorrect rejection to respond).
  374. #5th: This column can be used to indicate the latency of said response to the stimulus, in seconds.
  375. #6th: The most important - this lists the actual location of the event. This is in data points, and indicates the EXACT onset of the event from the beginning of the data. This means that
  376. #depending on your sampling rate you need to calculate the exact time and transform these numbers. If your EEG sampling rate is 1000 Hz, this column coincides with milliseconds.
  377. #No column names should appear.

preprocessing_EMdata_sf_OSF.R, no license · at the source

Overview

Authors: Xin Huang1,2, Brian W L Wong2,3, Werner Sommer4,5,6,7, Olaf Dimigen8, Urs Maurer2,9
ORCID iDs: Xin Huang, Brian W L Wong, Werner Sommer, Olaf Dimigen, Urs Maurer
  1. School of Psychology, Nanjing Normal University, Nanjing, China
  2. Department of Psychology, The Chinese University of Hong Kong, Hong Kong, China
  3. BCBL, Basque Center on Brain, Language and Cognition, Donostia–San Sebastián, Spain
  4. Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
  5. Department of Physics and Life Science Imaging Center, Hong Kong Baptist University, Hong Kong, China
  6. Department of Psychology, Zhejiang Normal University, Jin Hua, China
  7. Faculty of Education, National University of Malaysia, Kuala Lumpur, Malaysia
  8. Department of Experimental Psychology, University of Groningen, Groningen, The Netherlands
  9. Brain and Mind Institute, The Chinese University of Hong Kong, Hong Kong, China
Institutions: Nanjing Normal University (China); Chinese University of Hong Kong (Hong Kong SAR China); Basque Center on Cognition, Brain and Language (Spain); Zhejiang Normal University (China); Hong Kong Baptist University (Hong Kong SAR China); Humboldt-Universität zu Berlin (Germany); National University of Malaysia (Malaysia); University of Groningen (Netherlands)
Journal: Neurobiology of language (Cambridge, Mass.), volume 7, article NOL.a.223
Dates: received 4 March 2025; accepted 6 November 2025; published online 16 March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/nol.a.223 · PMID 41918575 · PMCID PMC13035401 · OpenAlex W7105601244
Open access: gold, a free copy (OpenAlex)
Preprint: osf.io/abrpc
Status: code verified
Categories: EEG (modality)
Methods: Spectral & time-frequency, Smoothing, state filtering, decompositions, Statistics, fMRI & imaging, Physiology & signal measures
Keywords: combined EEG and eye tracking, luminance contrast, magnocellular, parvocellular, spatial frequency, visual word recognition
Topic: Neural and Behavioral Psychology Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Hong Kong Government (RGC-GRF 14616418)
Citations: not cited yet (Europe PMC); 119 references in the paper

Abstract

Rapidly processed magnocellular (M) information may facilitate visual object recognition but its role in reading is unclear. A previous study with Chinese characters and masked foveal primes did not find a unique role of the M system as compared to the parvocellular (P) system in mediating repetition effects. As M cells are better represented in the parafoveal visual field, the present study tested whether the M and P systems contribute differentially to parafoveal processing during reading. We combined EEG recordings and eye tracking to measure parafoveal preview effects in fixation-related potentials, using the boundary paradigm. In two experiments, we contrasted high versus low spatial frequency previews and luminance versus color contrast previews and also included standard previews as a manipulation check. As expected, the N250 component was diminished after valid as compared to invalid normal previews, especially over the left hemisphere. We also obtained left-lateralized preview effects for the N250 component for both M- and P-biased previews in both experiments. In the experiment involving a spatial frequency manipulation, P-biased preview effects tended to be larger than M-biased preview effects over the left hemisphere, but not over the right hemisphere. No interactions with preview validity were found for the luminance contrast manipulation. This null effect was supported by a Bayesian analysis. Taken together, these results indicate that the M pathway does not exclusively mediate the preview effect, even for stimuli presented in the parafovea. Instead, both M- and P-based information appear to contribute to early, left-lateralized neural processes underlying visual word recognition.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

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OSF abrpc

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Languages: MATLAB (2), R (2)
Size: 8 files, 4 scripts
Software Heritage: not checked
Found in: “Data and Code Availability Statements”
Holds: 2 notebooks
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: data.table (2 files), tidyverse (2 files)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
4 files
At the source: osf.io/abrpc/

The paper's code and data availability statement is in the Data section.

Tracing map

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  • 2 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

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Data and Code Availability Statements

All data and code are publicly available via the Open Science Framework at https://osf.io/abrpc/.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 5 authors, 6 keywords, 1 funder, 108 references.

Cite

This paper

Huang, X., Wong, B. W. L., Sommer, W., Dimigen, O., & Maurer, U. (2026). No Unique Magnocellular Facilitation in Parafoveal Processing: A Combined EEG and Eye Tracking Study. Neurobiology of language (Cambridge, Mass.), 7, NOL.a.223. https://doi.org/10.1162/nol.a.223

BibTeX

@article{huang2026no,
author = {Huang, Xin and Wong, Brian W L and Sommer, Werner and Dimigen, Olaf and Maurer, Urs},
title = {{No Unique Magnocellular Facilitation in Parafoveal Processing: A Combined EEG and Eye Tracking Study}},
journal = {Neurobiology of language (Cambridge, Mass.)},
year = {2026},
month = mar,
volume = {7},
pages = {NOL.a.223},
publisher = {MIT Press},
issn = {2641-4368},
doi = {10.1162/nol.a.223},
url = {https://doi.org/10.1162/nol.a.223},
pmid = {41918575},
pmcid = {PMC13035401}
}

RIS

TY - JOUR
AU - Huang, Xin
AU - Wong, Brian W L
AU - Sommer, Werner
AU - Dimigen, Olaf
AU - Maurer, Urs
TI - No Unique Magnocellular Facilitation in Parafoveal Processing: A Combined EEG and Eye Tracking Study
T2 - Neurobiology of language (Cambridge, Mass.)
J2 - Neurobiol Lang (Camb)
PY - 2026
DA - 2026/03/16
VL - 7
SP - NOL.a.223
SN - 2641-4368
PB - MIT Press
DO - 10.1162/nol.a.223
UR - https://doi.org/10.1162/nol.a.223
LA - en
ER -

CSL-JSON

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The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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