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Condition Operations ​

This demo shows how to combine epoch conditions, compute ERP difference waves, and create condition average waves.

Key Functions ​

FunctionPurposeInput
condition_combineMerge epoch conditions into new groupsEpoch data
condition_differenceSubtract one condition's ERP from anotherERP data
condition_averageAverage multiple conditions into one ERPERP data

When to Use ​

  • condition_combine: When you have many conditions and want to pool some together before averaging (e.g., merge "Go left" and "Go right" into a single "Go" condition)

  • condition_difference: When you need difference waves for analysis (e.g., target minus standard, congruent minus incongruent)

  • condition_average: When you want to average across conditions after computing ERPs (e.g., collapsing across irrelevant factors)

Important Notes ​

  • condition_combine works on epoch data (before averaging); condition_difference and condition_average work on ERP data (after averaging)

  • All three also have batch versions that process all matching JLD2 files in a directory

Workflow Summary ​

Condition Combining ​

  • Merge multiple epoch conditions into new groups (operates on epochs, before averaging)

Condition Differences ​

  • Create difference waves from ERP condition pairs (e.g., [(1, 2), (3, 4)])

Condition Averaging ​

  • Average ERP conditions into combined waves (e.g., [[1, 2], [3, 4]] or [[1, 2, 3, 4]])

Typical Pipeline ​

  • Preprocess → Combine conditions → Average → Difference/Average waves → Grand average

Code Examples ​

Show Code
julia
# Demo: Condition Operations
# Shows how to combine epoch conditions, compute ERP difference waves,
# and create condition average waves.
# Covers condition_combine, condition_difference, and condition_average.

using EegFun
# Note: EegFun.example_path() resolves bundled example data paths.
# When using your own data, simply pass the file path directly, e.g.:
# dat = EegFun.read_raw_data("/path/to/your/data.bdf")

#######################################################################
# CREATE EXAMPLE DATA
#######################################################################

# Load raw data and do minimal preprocessing
dat = EegFun.read_raw_data(EegFun.example_path("data/bdf/example1.bdf"))
layout = EegFun.read_layout(EegFun.example_path("layouts/biosemi/biosemi72.csv"))
EegFun.polar_to_cartesian_xy!(layout)

dat = EegFun.create_eegfun_data(dat, layout)

# minimal preprocessing
EegFun.rereference!(dat, :avg)
EegFun.highpass_filter!(dat, 0.1)

# Extract epochs for four conditions
epoch_cfg = [
    EegFun.EpochCondition(name = "Trigger1", trigger_sequences = [[1]]),
    EegFun.EpochCondition(name = "Trigger2", trigger_sequences = [[2]]),
    EegFun.EpochCondition(name = "Trigger3", trigger_sequences = [[3]]),
    EegFun.EpochCondition(name = "Trigger4", trigger_sequences = [[5]]),
]
epochs = EegFun.extract_epochs(dat, epoch_cfg, (-0.2, 1.0))
EegFun.baseline!(epochs, (-0.2, 0.0))


#######################################################################
# CONDITION COMBINE (EPOCHS)
#######################################################################

# Combine conditions 1+2 into one group and 3+4 into another
epochs_combined = EegFun.condition_combine(epochs, [[1, 2], [3, 4]])


#######################################################################
# CONDITION DIFFERENCE WAVES (ERPS)
#######################################################################

# Create ERPs from the original four conditions
erps = EegFun.average_epochs(epochs)

# Difference wave: condition 1 minus condition 2
diff_waves = EegFun.condition_difference(erps, [(1, 2)])
EegFun.plot_erp(diff_waves, channel_selection = EegFun.channels([:Cz]))

# Multiple difference pairs
diff_waves = EegFun.condition_difference(erps, [(1, 2), (3, 4)])


#######################################################################
# CONDITION AVERAGE WAVES (ERPS)
#######################################################################

# Average conditions 1 and 2 together, and 3 and 4 together
avg_waves = EegFun.condition_average(erps, [[1, 2], [3, 4]])

# Average all four conditions into one
avg_all = EegFun.condition_average(erps, [[1, 2, 3, 4]])

See Also ​