signalJourney Examples¶

Welcome to the signalJourney examples! These examples demonstrate how to document signal processing pipelines using the signalJourney specification. Each example includes a detailed breakdown of the JSON structure and showcases different features of the format.

Example Categories¶

📚 Schema Examples¶

These examples are found in the schema/examples/ directory and demonstrate core signalJourney features with implementations in both MNE-Python and EEGLAB:

🔬 Real-World Examples¶

Production pipelines demonstrating advanced signalJourney features:

Implementation Comparison¶

Software-Specific Features¶

MNE-Python Examples: - Object-oriented API (epochs.filter(), raw.set_eeg_reference()) - HDF5 and FIF file formats - Advanced source modeling (BEM, forward solutions) - Integration with scipy/numpy ecosystem

EEGLAB Examples: - Pop-up function interface (pop_loadset, pop_eegfiltnew) - .set/.fdt file formats - ICLabel automated component classification - MATLAB ecosystem integration

Learning Path Recommendations¶

🎯 For Beginners¶

1. Start with Basic Preprocessing (MNE-Python or EEGLAB)
2. Understand signalJourney structure and common patterns
3. Learn about quality metrics and parameter documentation

🚀 For Intermediate Users¶

1. Explore ICA Decomposition examples for multi-output workflows
2. Study Time-Frequency Analysis for advanced parameter handling
3. Examine Real-World Examples for production pipeline patterns

🔬 For Advanced Users¶

1. Analyze Source Localization for complex dependency chains
2. Study Connectivity Analysis for cross-channel processing
3. Use NEMAR Pipeline as template for your own workflows

Key signalJourney Features Demonstrated¶

Interactive Features¶

Each example page includes: - 📊 Mermaid flowcharts showing pipeline structure - 💻 JSON code examples with syntax highlighting
- 📋 Parameter explanations for each processing step - 🔍 Quality control integration examples - 🔗 Cross-references to related examples and schemas

Mermaid Diagram Legend¶

The flowcharts use a consistent color scheme to distinguish different types of elements:

flowchart TD
    A[Processing Step<br/>Function call] --> B[Another Step<br/>Function call]

    %% Input and output files  
    C["📁 input_file.fif<br/>Input data"] --> A
    B --> D["💾 output_file.fif<br/>Processed data"]

    %% Inline data (small parameters/values saved in JSON)
    A --> E["📊 Inline Data<br/>Small parameters/values"]

    %% Quality metrics
    B --> F["📈 Quality Metrics<br/>Processing statistics"]

    %% Styling
    classDef processStep fill:#e1f5fe,stroke:#01579b,stroke-width:2px
    classDef inputFile fill:#fff3e0,stroke:#e65100,stroke-width:2px
    classDef outputFile fill:#e8f5e8,stroke:#1b5e20,stroke-width:2px
    classDef inlineData fill:#f3e5f5,stroke:#4a148c,stroke-width:2px
    classDef qualityMetric fill:#f9f9f9,stroke:#666,stroke-width:1px

    class A,B processStep
    class C inputFile
    class D outputFile
    class E inlineData
    class F qualityMetric

Legend: - 🔵 Light Blue: Processing steps (function calls) - 🟠 Orange: Input files - 🟢 Green: Output files (saved results) - 🟣 Purple: Inline data (small parameters/values stored in JSON) - ⚪ Gray: Quality metrics (processing statistics)

Getting Started¶

1. Choose your software: Pick MNE-Python or EEGLAB based on your workflow
2. Start simple: Begin with Basic Preprocessing to understand the format
3. Build complexity: Progress through examples as your needs grow
4. Adapt patterns: Use examples as templates for your own pipelines

Each example is designed to be both educational and practical, providing patterns you can adapt for documenting your own signal processing workflows.

JSON Schema Reference¶

All examples validate against the signalJourney JSON schema. For detailed field specifications, see: - Schema Overview - Field Definitions - Validation Guide

Contributing Examples¶

Have a pipeline you'd like to document as an example? See our contributing guide for how to add new examples to the collection.