Examples#

This section contains executable example scripts demonstrating various eegprep workflows. All examples are automatically executed by sphinx-gallery during documentation build, generating a gallery with output plots and code.

Example Gallery:

Overview#

The examples below demonstrate key eegprep functionality:

  1. Basic EEG Preprocessing Workflow - A complete preprocessing pipeline including artifact cleaning and channel interpolation with visualization of results.

  2. BIDS Dataset Preprocessing - Working with BIDS-formatted EEG datasets, demonstrating data loading and batch preprocessing workflows.

  3. Artifact Removal Comparison - Comparing different artifact removal methods (clean_artifacts, ASR) with parameter effects and statistical analysis.

  4. ICA Decomposition and ICLabel Classification - Independent Component Analysis with automatic component classification for artifact identification.

  5. Channel Interpolation - Identifying bad channels and performing interpolation with quality assessment and visualization.

Example Categories#

Basic Examples#

These examples demonstrate fundamental eegprep operations:

  • plot_basic_preprocessing.py - Create synthetic EEG data, apply preprocessing steps, and visualize results. Covers artifact cleaning and channel interpolation.

Advanced Examples#

These examples show more sophisticated workflows:

  • plot_bids_pipeline.py - Work with BIDS-formatted datasets, understand the BIDS structure, and apply preprocessing pipelines to multiple subjects.

  • plot_artifact_removal.py - Compare different artifact removal methods, understand parameter effects, and analyze statistical properties of cleaned data.

  • plot_ica_and_iclabel.py - Perform ICA decomposition, classify components using ICLabel, and identify artifacts for rejection.

Specialized Examples#

These examples focus on specific preprocessing tasks:

  • plot_channel_interpolation.py - Identify bad channels using statistical criteria, perform interpolation, and assess interpolation quality.

Running the Examples#

All examples are designed to be self-contained and executable:

  1. Synthetic Data - Examples use synthetic data to avoid external dependencies

  2. No Setup Required - All necessary imports and data generation are included

  3. Visualization - Examples generate plots showing preprocessing effects

  4. Documentation - Each example includes detailed comments explaining each step

Example Structure#

Each example follows this structure:

  1. Title and Description - Clear explanation of what the example demonstrates

  2. Imports - Required libraries and eegprep modules

  3. Data Creation - Generate synthetic EEG data with realistic characteristics

  4. Processing - Apply eegprep functions with explanations

  5. Visualization - Create plots showing results

  6. Analysis - Print summary statistics and recommendations

Key Features#

  • Executable Code - All examples are runnable Python scripts

  • Matplotlib Plots - Visualizations generated during execution

  • Print Output - Summary statistics and results printed to console

  • Sphinx-Gallery Format - Proper docstring format for auto-generation

  • Comments - Detailed comments explaining each processing step

  • Realistic Data - Synthetic data with realistic EEG characteristics

Learning Path#

We recommend exploring the examples in this order:

  1. Start with plot_basic_preprocessing.py to understand the basic workflow

  2. Move to plot_artifact_removal.py to learn about different cleaning methods

  3. Explore plot_channel_interpolation.py for channel quality assessment

  4. Study plot_ica_and_iclabel.py for advanced component analysis

  5. Finally, check plot_bids_pipeline.py for working with real datasets

Tips for Using Examples#

  • Modify Parameters - Try changing preprocessing parameters to see effects

  • Inspect Plots - Carefully examine generated plots to understand results

  • Read Comments - Comments explain the reasoning behind each step

  • Check Output - Print statements show important statistics and results

  • Adapt Code - Use examples as templates for your own preprocessing pipelines

For more information, see the User Guide and API Reference documentation.