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Realignment ​

This demo shows how to realign stimulus-locked epochs to a different time point, such as response time, for response-locked ERP analysis.

When to Use Realignment ​

Stimulus-locked epochs are aligned to stimulus onset (t=0). Realignment shifts t=0 to a different event — typically the participant's response — so that you can study activity time-locked to that event instead.

Common use cases:

  • Response-locked ERPs — study motor preparation relative to button press

  • Saccade-locked ERPs — study activity relative to eye movement onset

  • Any event-locked analysis — realign to any column in the epoch data

How it Works ​

  1. Each epoch's time vector is shifted so that the realignment value becomes t=0

  2. All epochs are cropped to the common time interval that is valid across all trials

  3. A uniform time vector is regenerated to ensure consistency

Key Functions ​

FunctionPurpose
realign!(epochs, :rt)Realign in place (mutating)
realign(epochs, :rt)Return a realigned copy
realign(file_pattern, :rt)Batch realign across participants

Workflow Summary ​

Single-Participant Realignment ​

  • Realign epochs to response time column

Batch Realignment ​

  • Process all participant files in a directory

Typical Pipeline ​

  • Extract stimulus-locked epochs → realign to RT → average → LRP → jackknife

Code Examples ​

Show Code
julia
# Demo: Response-Locked Realignment
# Shows how to realign stimulus-locked epochs to a different time point
# (e.g., response time) for response-locked ERP analysis.

# 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")

using EegFun
using GLMakie

const DEMO_OUTPUT = "./tutorials/output/"
mkpath(DEMO_OUTPUT)

#######################################################################
# LOAD EPOCHED DATA
#######################################################################

# Load stimulus-locked epochs for a single participant
epochs = EegFun.read_data(EegFun.example_path("data/julia/epochs/example1_epochs.jld2"))

#######################################################################
# REALIGN EPOCHS (IN-MEMORY)
#######################################################################

# Realign epochs to response triggers (e.g., 201, 202). 
# This shifts the time axis of each epoch so that the response trigger becomes t=0,
# and drops epochs that do not contain any of these triggers.
EegFun.realign!(epochs, [201, 202])

# The time axis is now response-locked! Let's compute the ERP.
# We will use condition 1.
# Note that we use grand_average since we are taking the mean over epochs
response_locked_erp = EegFun.grand_average([epochs[1]])

# Plot the response-locked ERP
EegFun.plot_erp(response_locked_erp)

#######################################################################
# TRIGGER INTERVALS (REACTION TIMES)
#######################################################################

# We can also calculate the time interval between the stimulus and the response 
# (i.e. the reaction time) and append it as a column to the epoch data.
epochs_rt = EegFun.read_data(EegFun.example_path("data/julia/epochs/example1_epochs.jld2"))

# Calculate the interval between stimulus (e.g., 101) and response (e.g., 201)
EegFun.calculate_trigger_interval!(epochs_rt, [101], [201], column_name = :reaction_time)


#######################################################################
# BATCH REALIGNMENT (FROM DISK)
#######################################################################

# Realign all epoch files matching the pattern to response triggers
EegFun.realign(
    "epochs",
    [201, 202],
    input_dir = EegFun.example_path("data/julia/epochs/"),
    output_dir = DEMO_OUTPUT
)

See Also ​