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Signal Preprocessing Guide

Source: vignettes/preprocessing-guide.Rmd
preprocessing-guide.Rmd

Introduction

PhysioPreprocess provides a comprehensive set of tools for preprocessing physiological signals stored in PhysioExperiment objects. This vignette covers the core preprocessing operations: filtering, resampling, re-referencing, and detrending.

Setup

Creating Example Data

We begin by constructing a PhysioExperiment object with simulated EEG-like data. The object holds a time-by-channels matrix as its primary assay along with channel metadata and a sampling rate.

set.seed(42)
n_time <- 2560
n_channels <- 10
sr <- 256

# Simulate 10 seconds of data at 256 Hz
raw_data <- matrix(rnorm(n_time * n_channels), nrow = n_time, ncol = n_channels)

pe <- PhysioExperiment(
  assays = list(raw = raw_data),
  colData = S4Vectors::DataFrame(
    label = c("Fp1", "Fp2", "F3", "F4", "C3", "C4", "P3", "P4", "O1", "O2"),
    type = rep("EEG", n_channels)
  ),
  samplingRate = sr
)
pe

Filtering

PhysioPreprocess offers several filtering functions for removing unwanted frequency components from recorded signals.

Moving Average Filter

The simplest filter is a moving average, which smooths the signal by averaging over a sliding window. This is useful for quick noise reduction but offers limited frequency selectivity.

pe_ma <- filterSignals(pe, window = 5)

Butterworth Filter

The Butterworth filter is an infinite impulse response (IIR) filter widely used in physiological signal processing. It provides a maximally flat magnitude response in the passband and supports lowpass, highpass, bandpass, and bandstop configurations. Zero-phase forward-backward filtering is applied automatically via signal::filtfilt().

# Bandpass filter between 1 and 40 Hz (common for EEG)
pe_bp <- butterworthFilter(pe, low = 1, high = 40, type = "pass")

# Highpass filter at 0.5 Hz to remove slow drift
pe_hp <- butterworthFilter(pe, low = 0.5, type = "high",
                           output_assay = "highpass")

# Lowpass filter at 30 Hz
pe_lp <- butterworthFilter(pe, high = 30, type = "low",
                           output_assay = "lowpass")

FIR Filter

Finite impulse response (FIR) filters offer linear phase and guaranteed stability. They require a higher order than Butterworth filters for the same transition bandwidth but are more predictable in their frequency response.

pe_fir <- firFilter(pe, low = 1, high = 40, order = 100, type = "pass")

Notch Filter

Power line interference (50 Hz in Europe/Asia, 60 Hz in the Americas) is a common artifact in electrophysiological recordings. The notch filter removes the fundamental frequency and optionally its harmonics.

# Remove 50 Hz power line noise
pe_notch <- notchFilter(pe, freq = 50)

# Remove 60 Hz and its second harmonic
pe_notch60 <- notchFilter(pe, freq = 60, harmonics = 2)

Resampling

Changing the sampling rate is sometimes necessary when combining datasets recorded at different rates or when reducing data size for storage.

Arbitrary Resampling

The resample() function converts data to any target sampling rate. Three interpolation methods are available: linear interpolation (fast, adequate for most cases), spline interpolation (smoother), and FFT-based resampling (preserves frequency content).

# Downsample from 256 Hz to 128 Hz
pe_128 <- resample(pe, target_rate = 128, method = "linear")

# Upsample with spline interpolation
pe_512 <- resample(pe, target_rate = 512, method = "spline")

Integer-Factor Decimation

When the downsampling factor is an integer, decimate() applies an anti-aliasing lowpass filter before subsampling to prevent spectral aliasing.

# Decimate by a factor of 2 (256 Hz -> 128 Hz)
pe_dec <- decimate(pe, factor = 2)

Integer-Factor Interpolation

For integer-factor upsampling, interpolate() inserts new samples using the specified interpolation method.

# Upsample by a factor of 2 (256 Hz -> 512 Hz)
pe_up <- interpolate(pe, factor = 2, method = "spline")

Re-Referencing

Re-referencing changes the reference electrode for EEG data. The choice of reference affects spatial interpretation and is an important preprocessing decision.

Average Reference

The average reference subtracts the mean of all channels at each time point. This is the most common choice for high-density EEG because it approximates a reference-free measure.

pe_avg <- rereference(pe, ref_type = "average")
getCurrentReference(pe_avg)

Single Channel Reference

A single electrode can serve as the reference. Common choices include Cz, the nose, or a mastoid electrode.

pe_c3 <- rereference(pe, ref_type = "channel", ref_channels = "C3")

Linked Reference

Averaging two electrodes (e.g., linked mastoids) provides a symmetric reference.

# Assuming M1 and M2 channels exist
# pe_linked <- rereference(pe, ref_type = "channels",
#                          ref_channels = c("M1", "M2"))

Checking the Reference

After re-referencing, the reference information is stored in the object metadata and can be queried.

Detrending

Slow drifts and DC offsets are common in electrophysiological recordings. Detrending removes these trends without altering faster signal components.

Linear and Mean Detrending

The detrendSignals() function supports mean removal, linear detrending, and polynomial detrending.

# Remove the mean (DC offset)
pe_mean <- detrendSignals(pe, method = "mean")

# Remove a linear trend
pe_linear <- detrendSignals(pe, method = "linear")

# Remove a quadratic trend
pe_poly <- detrendSignals(pe, method = "polynomial", order = 2)

Alternative Detrending

The detrendSignal() function in filters-advanced.R provides a simpler interface for linear and constant detrending.

pe_dt <- detrendSignal(pe, type = "linear")

Baseline Removal

When a known baseline period exists (e.g., a pre-stimulus interval), you can subtract the baseline mean from the entire signal using removeBaseline().

pe_bl <- removeBaseline(pe, baseline_start = 0, baseline_end = 0.2)

Preprocessing Pipelines

For reproducible analysis, PhysioPreprocess supports pipelines that chain multiple preprocessing steps together. Pipelines are defined as a sequence of named steps, each specifying a function and its arguments.

Creating a Pipeline 

pipeline <- createPipeline(
  detrend = list(fn = "detrendSignals", method = "linear"),
  filter  = list(fn = "butterworthFilter", low = 1, high = 40, type = "pass"),
  notch   = list(fn = "notchFilter", freq = 50)
)
print(pipeline)

Applying a Pipeline 

pe_processed <- applyPipeline(pe, pipeline, verbose = TRUE)

Summary

This vignette demonstrated the core preprocessing operations available in PhysioPreprocess:

  • Filtering: Moving average, Butterworth, FIR, and notch filters
  • Resampling: Arbitrary-rate resampling, decimation, and interpolation
  • Re-referencing: Average, single-channel, and multi-channel references
  • Detrending: Mean removal, linear/polynomial detrending, and baseline subtraction
  • Pipelines: Reproducible, chainable preprocessing workflows

For artifact removal using Independent Component Analysis, see the companion vignette vignette("ica-artifact-removal", package = "PhysioPreprocess").

References

  • Oppenheim AV, Willsky AS, Nawab SH (1997). Signals and Systems. 2nd ed. Prentice Hall.
  • Nunez PL, Srinivasan R (2006). Electric Fields of the Brain. 2nd ed. Oxford University Press.
  • Crochiere RE, Rabiner LR (1983). Multirate Digital Signal Processing. Prentice Hall.