In pab2163/TOT_ERP: totERP Data Package

ERP Analysis Script

This script takes in the data output from ERPLAB (http://www.erpinfo.org/erplab.html) in which preprocessing has already been done, and average amplitudes 250-700ms for each electrode for each condition have been calculated by subject.

require(tidyverse)
require(afex)
require(ggplot2)

Load Data In / Recode

# Use data from package
library(totERP)
str(erpSubjectAveraged)
head(erpSubjectAveraged)


erpSubjectAveraged$tot <- dplyr::recode(erpSubjectAveraged$bini, '1' ='TOT', '2' ='NO TOT')
View(erpSubjectAveraged)

value - Averaged Amplitude 250-700ms post-onset. Grouped by subject, electrode, tot from ERPlab chindex - number for each electrode chlabel - label for each electrode bini - bins for TOT. 1 = tot, 2 = no tot ERPset - subject id #. Each subject is one ERPset

Specify Which Electrodes

target9 <- c("Cz", "C1", "C2", "CPz", "CP1", "CP2", "Pz", "P1", "P2")
target12 <- c("Cz", "C1", "C2", "C4", "CPz", "CP1", "CP2", "CP4", "Pz", "P1", "P2", "P4")

# Specify data frames for 9 centro-parietal electrodes, and 12 centro-parietal electrodes including 3 more to the right hemisphere (respectively)
erpSubjectAveraged_middle9 <- filter(erpSubjectAveraged, chlabel %in% target9) 
erpSubjectAveraged_12 <- filter(erpSubjectAveraged, chlabel %in% target12)

ANOVAS

On 9 middle electrodes and as a check, those 9 plus 3 more to the right (12)

# Anova for middle 9 electrodes

erpSubjectAveraged_middle9$bini <- as.factor(erpSubjectAveraged_middle9$bini)
erpSubjectAveraged_middle9$chlabel <- as.factor(erpSubjectAveraged_middle9$chlabel)
erpSubjectAveraged_middle9$ERPset <- as.factor(erpSubjectAveraged_middle9$ERPset)


fit9middle <- aov_4(value ~ bini * chlabel + (bini*chlabel | ERPset), data=erpSubjectAveraged_middle9)
summary(fit9middle)

anova(fit9middle, correction = "GG")

# Anova for middle 9 electrodes + 3 more on the right


erpSubjectAveraged_12$bini <- as.factor(erpSubjectAveraged_12$bini)
erpSubjectAveraged_12$chlabel <- as.factor(erpSubjectAveraged_12$chlabel)
erpSubjectAveraged_12$ERPset <- as.factor(erpSubjectAveraged_12$ERPset)


fit12 <- aov_4(value ~ bini * chlabel + (bini*chlabel | ERPset), data=erpSubjectAveraged_12)
summary(fit12)

anova(fit12, correction = "GG")


#Same main effect for TOT when analyzing with 12 electrodes. With 12 though, TOT and electrode interact significantly

A few quick plots

# Plot of average amp for 9 electrodes
myPlot9 <- ggplot(erpSubjectAveraged_middle9, aes(x = tot, y = value)) +
  stat_summary(fun.data = mean_cl_boot) + 
  facet_wrap("chlabel")
myPlot9

# Plot of average amp for 12 electrodes
myPlot12 <- ggplot(erpSubjectAveraged_12, aes(x = tot, y = value)) + 
  stat_summary(fun.data = mean_cl_boot) + 
  facet_wrap("chlabel")
myPlot12


# Plot averaged effect for individual subjects (average of 9 electrodes)
myPlot9_bySUB <- ggplot(erpSubjectAveraged_middle9, aes(x = tot, y = value, col = tot)) + 
  stat_summary(fun.data = mean_cl_boot) + 
  facet_wrap("ERPset")

myPlot9_bySUB + 
  labs(title = "effect of tot for each subject, averaged across 9 electrodes", y = 'amplitude')

ANOVAS just on subsequently recalled items

# Anova for middle 9 electrodes

erpSubjectAveraged_middle9$bini <- as.factor(erpSubjectAveraged_middle9$bini)
erpSubjectAveraged_middle9$chlabel <- as.factor(erpSubjectAveraged_middle9$chlabel)
erpSubjectAveraged_middle9$ERPset <- as.factor(erpSubjectAveraged_middle9$ERPset)


fit9middle <- erpSubjectAveraged_middle9aov_4 %>%
  (value ~ bini * chlabel + (bini*chlabel | ERPset))
summary(fit9middle)

anova(fit9middle, correction = "GG")