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Pipeline Templates ​

This demo shows how to generate scaffold files for custom preprocessing pipelines and configuration files.

Pipeline Templates ​

generate_pipeline_template creates a ready-to-use Julia file with:

  • Structured preprocessing loop over participants

  • Built-in logging setup and error handling

  • Numbered processing steps with placeholder comments

  • Configuration loading from TOML files

This saves time when starting a new preprocessing project by providing a well-organised starting point.

Configuration Templates ​

generate_config_template creates a TOML configuration file with all available preprocessing parameters:

  • Files: Input/output directory paths

  • Filter: High-pass, low-pass, and ICA-specific filter settings

  • EOG: EOG channel names and correlation thresholds

  • EEG: Artifact and extreme value detection thresholds

  • ICA: Whether to apply ICA and data percentage settings

  • Layout: Neighbour distance criterion

  • Epoch: Timing parameters (start/end times)

Using Templates with Pipelines ​

The generated pipeline and config files are designed to work together:

  1. Generate both template files

  2. Edit the config TOML with your study-specific parameters

  3. Customise the pipeline Julia file with your processing steps

  4. Run the pipeline

Workflow Summary ​

This demo covers:

  • Generating a pipeline template file

  • Customising template options (number of steps, subsections)

  • Generating a configuration template

  • Understanding the generated config sections

Code Examples ​

Show Code
julia
# Demo: Pipeline Templates
# Shows how to generate scaffold files for custom preprocessing pipelines
# and configuration templates.

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")

#######################################################################
# GENERATE A PIPELINE TEMPLATE
#######################################################################

# Generate a basic pipeline template with default settings
# This creates a .jl file with all the boilerplate: logging, config loading,
# error handling, and a structured loop for custom processing steps
EegFun.generate_pipeline_template("my_custom_pipeline.jl")

# Generate with a custom function name and more steps
EegFun.generate_pipeline_template(
    "my_pipeline.jl",
    "my_preprocess";
    options = EegFun.PipelineTemplateOptions(num_steps = 8, subsections_per_step = 3),
)


#######################################################################
# GENERATE A CONFIG TEMPLATE
#######################################################################

# Generate a TOML configuration template with all available settings
EegFun.generate_config_template(filename = "my_config.toml")

# The generated config includes sections for:
# - files (input/output paths)
# - filter (highpass, lowpass, ICA-specific filters)
# - eog (EOG channel configuration and thresholds)
# - eeg (artifact and extreme value thresholds)
# - ica (whether to apply, percentage of data)
# - layout (neighbour criterion)
# - epoch timing (start/end times)

# You can read and inspect a config file
# cfg = EegFun.read_config("my_config.toml")
# EegFun.print_config(cfg)


#######################################################################
# CLEANUP
#######################################################################

# Remove generated template files (for demo purposes)
isfile("my_custom_pipeline.jl") && rm("my_custom_pipeline.jl")
isfile("my_pipeline.jl") && rm("my_pipeline.jl")
isfile("my_config.toml") && rm("my_config.toml")

See Also ​