Overview

The function of this script is to visualize every subject's MMN and to display an MMN of the grand average. This code is broken down into four parts.

• Part 1: Loading ERP data and data cleaning
• Part 2: Calculating the MMN for Cz channel
• Part 3: Displaying the MMN for each subject
• Part 4: Displaying the grand mean MMN

Part 1: Loading in deviant and standard ERP data

library(tidyverse)
library(ggplot2)

# Set working directory
setwd("Z:/Leo/KBB_EEG_Pilot/6_ERPLAB/Exported ERP")

# Obtain all deviant and standard files separately
Deviant.Tone.files <- list.files(pattern = "Deviant 1200Hz Tone")
Standard.Tone.files <- list.files(pattern = "Standard 1000Hz Tone")

# Save the names/IDs of each file
Names <- sub("_.*", "", Deviant.Tone.files)

# Read in the files separately
deviant.list <- list()
standard.list <- list()

for(ii in 1:length(Deviant.Tone.files)) {
  
  # load in files
  current.deviant <- read.csv(Deviant.Tone.files[ii], sep = "\t")
  current.standard <- read.csv(Standard.Tone.files[ii], sep = "\t")

  # Rename Time to Channel
  current.deviant <- rename(current.deviant, Channel = time)
  current.standard <- rename(current.standard, Channel = time)
  
  # Remove variable X
  current.deviant <- select(current.deviant, - X)
  current.standard <- select(current.standard, - X)
  
  # Save into the list
  deviant.list[ii](/LeoLedesma237/LeoWebsite/wiki/ii) <- current.deviant
  standard.list[ii](/LeoLedesma237/LeoWebsite/wiki/ii) <- current.standard
}

Part 2: Extracting Fz information + creating MMN

# Extract Fz data

data <- list()

for(ii in 1:length(deviant.list)) {
  
  # Extract the datasets from the list one by one
  current.deviant <- deviant.list[ii](/LeoLedesma237/LeoWebsite/wiki/ii)
  current.standard <- standard.list[ii](/LeoLedesma237/LeoWebsite/wiki/ii)
  
  current.deviant <- filter(current.deviant, Channel == "Fz") 
  current.standard <- filter(current.standard, Channel  == "Fz")
  
  # Remove the Channel variable
  current.deviant <- select(current.deviant, - Channel)
  current.standard <- select(current.standard, - Channel)
  
  # Obtain the colMeans
  current.deviant.CM <- colMeans(current.deviant)
  current.standard.CM <- colMeans(current.standard)
  
  # Create a time variable from the names of these variables
  Time = seq(from = -100, to = 348, by = 2)
  
  # Calculate the MMN (according to google and ChatGPT it is deviant - standard)
  MMN <-  current.deviant.CM - current.standard.CM
  
  # Join everything into a dataframe
  joined.data <- data.frame(Time = Time,
                            Deviant = current.deviant.CM,
                            Standard = current.standard.CM,
                            MMN = MMN) 
  
  # Make the data longer
  data[ii](/LeoLedesma237/LeoWebsite/wiki/ii) <- pivot_longer(joined.data, cols = -Time, names_to = "ERP", values_to = "Voltage")
  
}

Part 3: Visualizing the MMN at Cz for every subject individually

# Plot the MMN at Cz individually 
for(ii in 1:length(data)) {

  # Save each plot into a list
  plot.list <- list()
  
  plot.list[ii](/LeoLedesma237/LeoWebsite/wiki/ii) <- data[ii](/LeoLedesma237/LeoWebsite/wiki/ii) %>%
    ggplot(aes(Time, Voltage, color = ERP)) +
    geom_hline(yintercept=0, color = "black") +
    geom_vline(xintercept=0, color = "black") +
    geom_point(size = 1) +
    geom_line(size = 1) +
    labs(title = paste(Names[ii],"MMN Tones at Cz"),
         x = "Time (ms)",
         y = "MicroVolts") +
    theme_classic()
    
  # Print the graphs from the list created
  print(plot.list[ii](/LeoLedesma237/LeoWebsite/wiki/ii))

}

Part 4: Visualizing the Grand MMN from all subjects

# Creating an averaged Tone MMN at Cz

Voltage.list <- lapply(data, function(x) x$Voltage)
mean.Voltage <- rowMeans(do.call(cbind, Voltage.list))

# Use the same information from the first two variables in data 1 to create an averaged dataset
averaged.data <- data.frame(Time = data[1](/LeoLedesma237/LeoWebsite/wiki/1)$Time,
                            ERP = data[1](/LeoLedesma237/LeoWebsite/wiki/1)$ERP,
                            mean.Voltage = mean.Voltage)

# Graph the averaged data
averaged.data %>%
  ggplot(aes(Time, mean.Voltage, color = ERP)) +
  geom_hline(yintercept=0, color = "black") +
  geom_vline(xintercept=0, color = "black") +
  geom_point(size = 1) +
  geom_line(size = 1) +
  labs(title = paste("Averaged MMN Tones at Cz (n = ",length(data),")", sep =""),
       x = "Time (ms)",
       y = "MicroVolts") +
  theme_classic()