EMC2 (version 3.2.0)

high_pass_filter: Apply High-Pass Filtering to fMRI Data

Description

This function applies high-pass filtering to fMRI data to remove low-frequency noise and drift. It supports two filtering methods: cosine basis functions and polynomial regressors.

Usage

high_pass_filter(X, high_pass_model = "cosine", frame_times = NULL, ...)

Value

A data frame or matrix with the same structure as X, but with high-frequency components removed from the data columns.

Arguments

X

A data frame or matrix containing the data to be filtered. If it contains columns 'subjects' and 'run', the function will apply filtering separately for each subject-run combination.

high_pass_model

A character string specifying the high-pass filtering method. Options are 'cosine' (default) or 'poly' for polynomial regressors.

frame_times

A numeric vector of time points for each frame. If NULL, the function will attempt to extract this from a 'time' column in X.

...

Additional arguments passed to the function.

Examples

# Create a simple example data frame with drift
set.seed(123)
n_frames <- 100
time <- seq(0, 99)

# Create a signal with low-frequency drift
drift <- 0.1 * time
signal <- sin(2 * pi * 0.1 * time) + drift
noise <- rnorm(n_frames, 0, 0.5)
data <- signal + noise

# Create a data frame
df <- data.frame(
  time = time,
  signal = data
)

# Apply high-pass filtering using cosine basis functions
filtered_df <- high_pass_filter(df, high_pass_model = "cosine")