Example: Basic EEG Preprocessing Pipeline (EEGLAB)¶

This page explains the basic_preprocessing_pipeline_eeglab.signalJourney.json example file, which documents a standard EEG preprocessing workflow using EEGLAB.

Pipeline Overview¶

The EEGLAB basic preprocessing pipeline demonstrates fundamental EEG preprocessing steps using EEGLAB functions:

• Load raw data from EEGLAB .set format
• Apply high-pass filtering (1 Hz) using pop_eegfiltnew
• Apply low-pass filtering (40 Hz) using pop_eegfiltnew
• Apply notch filtering (60 Hz) using pop_eegfiltnew
• Set average reference using pop_reref
• Interpolate bad channels using pop_interp

Pipeline Flowchart¶

flowchart TD
    A[Load Raw Data
pop_loadset] --> B[High-pass Filter
pop_eegfiltnew
1 Hz]
    B --> C[Low-pass Filter
pop_eegfiltnew
40 Hz]
    C --> D[Notch Filter
pop_eegfiltnew
58-62 Hz]
    D --> E[Average Reference
pop_reref]
    E --> F[Interpolate Bad Channels
pop_interp]

    %% Input file
    G["📁 sub-01_task-rest_raw.set
Raw EEGLAB dataset"] --> A

    %% Final output
    F --> H["💾 sub-01_task-rest_desc-preproc_eeg.set
Preprocessed dataset"]

    %% Quality metrics
    E --> Q1["📈 Reference type: average
Channels: 62"]
    F --> Q2["📈 Interpolated: [53, 21]
Method: spherical"]

    %% 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 qualityMetric fill:#f9f9f9,stroke:#666,stroke-width:1px

    class A,B,C,D,E,F processStep
    class G inputFile
    class H outputFile
    class Q1,Q2 qualityMetric

Key EEGLAB Features Demonstrated¶

EEGLAB Function Calls¶

• pop_loadset: Load EEGLAB dataset files (.set/.fdt)
• pop_eegfiltnew: Modern FIR filtering with linear phase
• pop_reref: Re-referencing to average or specific channels
• pop_interp: Spherical spline interpolation for bad channels
• pop_saveset: Save processed datasets

EEGLAB-Specific Parameters¶

• Filter specifications: Uses EEGLAB's default FIR filter parameters
• Channel selection: EEGLAB channel indexing and selection
• Dataset structure: EEGLAB EEG structure format preservation

Example JSON Structure¶

The signalJourney file documents each processing step with:

{
  "stepId": "2",
  "name": "Apply High-pass Filter",
  "description": "Apply a FIR high-pass filter at 1 Hz.",
  "software": {
    "name": "EEGLAB",
    "version": "2023.1",
    "functionCall": "pop_eegfiltnew(EEG, 'locutoff', 1, 'plotfreqz', 0)"
  },
  "parameters": {
    "locutoff": 1.0,
    "hicutoff": null,
    "filtorder": [],
    "revfilt": 0,
    "usefft": 1,
    "plotfreqz": 0
  }
}

Quality Control Integration¶

Each step includes quality metrics specific to EEGLAB processing: - Channel counts and indices - Filter specifications and verification - Reference channel information - Interpolation success metrics

EEGLAB vs MNE-Python Comparison¶

EEGLAB-Specific Workflow¶

Step-by-Step Filtering Approach¶

Unlike MNE-Python's combined band-pass filter, EEGLAB demonstrates: 1. Separate high-pass filtering for drift removal 2. Separate low-pass filtering for noise reduction
3. Independent notch filtering for line noise

EEG Structure Preservation¶

EEGLAB maintains the EEG structure throughout processing: - Channel information preserved across all steps - Event markers maintained during filtering - Dataset history automatically tracked

Usage Notes¶

This example demonstrates: - EEGLAB workflow patterns with pop_ functions - Parameter documentation for reproducible processing - File format handling for EEGLAB datasets - Quality metrics relevant to EEGLAB processing - Sequential filtering approach common in EEGLAB pipelines

The pipeline is designed to be representative of standard EEGLAB preprocessing workflows while maintaining full parameter transparency for reproducibility.