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Rereference ​

This demo demonstrates applying different re-referencing schemes to EEG data.

What is Re-referencing? ​

Re-referencing changes the voltage baseline by subtracting a reference signal from all electrodes. Since EEG measures potential differences, the choice of reference affects:

  • Signal amplitudes and topographies

  • Component interpretations

  • Statistical comparisons

Why Re-reference? ​

Recording reference: The reference used during data acquisition (e.g., Cz, linked mastoids, or manufacturer default)

Analysis reference: The reference appropriate for your research question and field conventions

Re-referencing allows you to change from the recording reference to a more appropriate analysis reference.

Common Reference Schemes ​

Average Reference (:avg):

  • Subtracts mean of all electrodes from each channel

  • Assumes zero average scalp potential

  • Most common for high-density EEG (64+ channels)

  • Reference-free topographies

Mastoid Reference:

  • References to average of left/right mastoid electrodes (e.g., :M1, :M2)

  • Common in ERP research

  • Reduces contamination from reference site activity

Single Electrode Reference:

  • References to a specific electrode (e.g., :Cz, :Fz)

  • Useful for specific research questions

  • Activity at reference site becomes invisible (single reference electrode = 0)

Best Practices ​

  • Apply the same reference across all conditions and participants

  • Re-reference before interpolating bad channels (bad channels affect average reference)

  • Document your reference choice in publications

  • Use average reference for high-density recordings

Re-referencing is Reversible ​

You can change references multiple times. EegFun tracks the current reference and updates accordingly.

Workflow Summary ​

This demo shows re-referencing workflows:

Continuous Data ​

  • Load and preprocess data

  • Apply different reference schemes (:Fp1, :AF7)

  • Visualize in databrowser to see effects

Epoched Data ​

  • Create epochs from continuous data

  • Apply references to segmented trials

  • Data structure automatically updated

ERP Data ​

  • Average epochs into ERPs

  • Apply references to averaged data

  • Reference changes preserved across data types

Code Examples ​

Show Code
julia

# Demo: Rereferencing {#Demo:-Rereferencing}

# Shows different rereferencing methods (average, linked mastoids, specific channel reference). {#Shows-different-rereferencing-methods-average,-linked-mastoids,-specific-channel-reference.}

using EegFun

# Note: EegFun.example_path() resolves bundled example data paths. {#Note:-EegFun.example_path-resolves-bundled-example-data-paths.}

# When using your own data, simply pass the file path directly, e.g.: {#When-using-your-own-data,-simply-pass-the-file-path-directly,-e.g.:}

# dat = EegFun.read_raw_data("/path/to/your/data.bdf") {#dat-EegFun.readrawdata"/path/to/your/data.bdf"}

# read raw data {#read-raw-data}

dat = EegFun.read_raw_data(EegFun.example_path("data/bdf/example1.bdf"));

# read and prepare layout file {#read-and-prepare-layout-file}

layout = EegFun.read_layout(EegFun.example_path("layouts/biosemi/biosemi72.csv"));

# create EegFun data structure (EegFun.ContinuousData) {#create-EegFun-data-structure-EegFun.ContinuousData}

dat = EegFun.create_eegfun_data(dat, layout);

# Some minimal preprocessing (average reference and highpass filter) {#Some-minimal-preprocessing-average-reference-and-highpass-filter}

EegFun.highpass_filter!(dat, 1)

# Rereference to :Fp1 {#Rereference-to-:Fp1}

EegFun.rereference!(dat, :Fp1) dat.data

# Rereference to :AF7 {#Rereference-to-:AF7}

EegFun.rereference!(dat, :AF7) dat.data

# We can also see the influence of reference using the databrowser {#We-can-also-see-the-influence-of-reference-using-the-databrowser}

EegFun.plot_databrowser(dat)

################################

# Epoching {#Epoching}

################################

# Create some epoched data {#Create-some-epoched-data}

epoch_cfg = [     EegFun.EpochCondition(name = "ExampleEpoch1", trigger_sequences = [[1]]),     EegFun.EpochCondition(name = "ExampleEpoch2", trigger_sequences = [[2]]), ] epochs = EegFun.extract_epochs(dat, epoch_cfg, (-0.2, 1.0))  # -200 to 1000 ms

EegFun.rereference!(epochs, :Fp1) EegFun.all_data(epochs[1]) # first condition EegFun.all_data(epochs[2]) # second condition

################################

# ERPs {#ERPs}

################################

# ERPs {#ERPs-2}

erps = EegFun.average_epochs(epochs)

EegFun.rereference!(erps, :Fp1) EegFun.all_data(erps[1]) EegFun.all_data(erps[2])

EegFun.rereference!(erps, :F1) EegFun.all_data(erps[1]) EegFun.all_data(erps[2])```

See Also ​