In livioivil/eegusta: EEG Utilities and STatistical Analysis: eeguana-based tools for statistical analysis of EEG data

library(tidyverse)

#see also Import_and_visualize_GroupLevel.Rmd
load(file="../data-raw/all_erp.Rdata")

#elec_position <- import_chans("canali.ced")

Prepare the data.frame ready to be modeled

Starting from the list of all the individual data files (r all_erp), we do the following steps: - Bind the list in a single data.frame - Create two virtual channels (ROI - Region Of Interest) for the left and right posterior electrodes - Split the variable r condition, into two new variables to be able to model the type of stimulus (Car or Face) and its visibility (Normal or Scrambled) - Filter the data into the time window of interest (140-180 ms) - Summarise the mean amplitude for each subject, condition and ROI

D = all_erp %>% 
  lapply(., pivot_longer, -time, names_to = "elec", values_to = "amplitude") %>% 
  bind_rows(.id = "tmp_ID") %>% 
  separate(tmp_ID, c("ID", "condition"), "_") %>% 
<<<<<<< Updated upstream
  # Creates the ROI (two posterior ROI, one on the left and one on the right)
  mutate(roi = case_when(
    elec %in% c("PO8", "O2", "P8") ~ "pos_dx",
    elec %in% c("PO7", "O1", "P7") ~ "pos_sx",
  )) %>%
  drop_na() %>%
  # Creates two new variables stimulus and modality. Stimulus contains info about the type 
  # of stimulus (Car or Face). modality contains info about the visbility (Normal or Scrambled)
  mutate(
    modality = case_when(
      str_detect(condition, "Scrambled") ~ "Scrambled",
      !str_detect(condition, "Scrambled") ~ "Normal"
    ),
    stimulus = case_when(
      str_detect(condition, "Cars") ~ "Cars",
      str_detect(condition, "Faces") ~ "Faces"
    )
  ) %>% 
  # Removes the variable condition. We don't need it anymore!
  select(-condition) %>%
  # Filter the time points of interest
  filter(time < 180, time > 140) %>% 
  # Creates mean value per each ID, roi, stimulus and modality
  group_by(ID, stimulus, modality, roi) %>%
  summarise(M = mean(amplitude)) %>% 
  ungroup()

head(D)
=======
  filter(time > 140, time < 180) %>% 
  mutate(electrode = elec) %>% 
  select(-elec) %>% 
  group_by(ID, condition,electrode) %>%
  summarise(amplitude = mean(amplitude))



dim(D)
# just to avoid visualization issues of str()
# attr(D,"groups") <- NULL
# check the dataset:
# str(D)

# check results:
# head(table(D$ID,D$electrode,D$condition))
#should be all ones

# pari a dx e dispari a sx
# anteriore sx F3, FC3 F7
# anteriore dx F4, FC4 F8
# posteriore sx P7, PO7 O1
# posteriore dx P8, PO8 O2

# D=data.frame(D)
# D$ROI=factor(D$electrode)
# levels(D$ROI)=gsub("F3","Ant_Left",levels(D$ROI))
# levels(D$ROI)=gsub("FC3","Ant_Left",levels(D$ROI))
# levels(D$ROI)=gsub("C7","Ant_Left",levels(D$ROI))
# 
# levels(D$ROI)=gsub("F4","Ant_Right",levels(D$ROI))
# levels(D$ROI)=gsub("FC4","Ant_Right",levels(D$ROI))
# levels(D$ROI)=gsub("C8","Ant_Right",levels(D$ROI))
# 
# levels(D$ROI)=gsub("P7","Post_Left",levels(D$ROI))
# levels(D$ROI)=gsub("PO7","Post_Left",levels(D$ROI))
# levels(D$ROI)=gsub("O1","Post_Left",levels(D$ROI))
# 
# levels(D$ROI)=gsub("P8","Post_Right",levels(D$ROI))
# levels(D$ROI)=gsub("PO8","Post_Right",levels(D$ROI))
# levels(D$ROI)=gsub("O2","Post_Right",levels(D$ROI))


# Create ROIs
D <- D %>%
  mutate(ROI = case_when(
    electrode %in% c("F3","FC3","F7") ~ "ant_sx",
    electrode %in% c("F4","FC4","F8") ~ "ant_dx",
    electrode %in% c("P7","PO7","O1") ~ "pos_sx",
    electrode %in% c("P8","PO8","O2") ~ "pos_dx"
  )) %>%
  drop_na() %>%
  group_by(ID, condition, ROI) %>%
  summarise(amplitude = mean(amplitude))
levels(D$ROI)[!(levels(D$ROI)%in%c("Ant_Left","Ant_Right","Post_Left","Post_Right"))]=NA

unique(D$ROI)

Modello Misure Ripetute

Linear Mixed Models

library(lmerTest)
D$condition=factor(D$condition)
contrasts(D$condition)<-contr.sum
D$ROI <- factor(D$ROI)
contrasts(D$ROI)<-contr.sum
mod=lmer(amplitude~ condition*ROI +(1|ID) ,data=D)
summary(mod)
plot(as.numeric(D$ID),residuals(mod))

plot(mod)
>>>>>>> Stashed changes

livioivil/eegusta documentation built on May 6, 2024, 9:20 p.m.