Calculate ISC Pupil/Eye movements

gistfile1.txt

calculate_isc_per_subject <- function(data_matrix, window_size = 10) {
  # data_matrix: A matrix where each column is a participant and each row is a time point.
  # window_size: The size of the sliding window for calculating correlations.

  n_timepoints <- nrow(data_matrix)
  n_participants <- ncol(data_matrix)
  
  # Check that the number of participants is greater than 1
  if (n_participants <= 1) {
    stop("There must be more than one participant to calculate inter-subject correlation.")
  }
  
  # Initialize a vector to store ISC values for each subject
  isc_values <- numeric(n_participants)
  
  # Loop over each participant to calculate their ISC
  for (subject in 1:n_participants) {
    z_values <- numeric()  # Store z-transformed correlation coefficients
    
    # Loop over the time points with a sliding window
    for (i in 1:(n_timepoints - window_size + 1)) {
      # Subset the data for the current window
      window_data <- data_matrix[i:(i + window_size - 1), ]
      
      # Calculate the average time series of all other subjects
      other_subjects_mean <- rowMeans(window_data[, -subject])
      
      # Correlate the excluded subject's data with the average of the other subjects
      subject_corr <- cor(window_data[, subject], other_subjects_mean)
      
      # Apply Fisher's r-to-z transformation
      z_values <- c(z_values, atanh(subject_corr))
    }
    
    # Average the z-values and inverse Fisher transformation back to r
    avg_z_value <- mean(z_values)
    isc_values[subject] <- tanh(avg_z_value)
  }
  
  return(isc_values)
}

# Example data: a matrix with 5 participants and 100 time points
#set.seed(123)
#data_matrix <- matrix(rnorm(500), nrow = 100, ncol = 5)

# Calculate ISC for each subject with a sliding window of size 10
#isc_result_per_subject <- calculate_isc_per_subject(data_matrix, window_size = 10)

# View ISC values for each subject
#print(isc_result_per_subject)