Example: ICA Decomposition Pipeline (MNE-Python)¶

This page explains the ica_decomposition_pipeline_mne.signalJourney.json example file, which documents an Independent Component Analysis (ICA) workflow for artifact removal using MNE-Python.

Pipeline Overview¶

This pipeline demonstrates how to apply ICA to remove ocular artifacts (eye movements and blinks) from preprocessed EEG data. It builds upon the Basic Preprocessing Pipeline by:

• Loading preprocessed data from the previous pipeline
• Fitting ICA decomposition using FastICA algorithm
• Identifying artifact components correlated with EOG channels
• Removing artifact components from the data
• Saving cleaned data for further analysis

Pipeline Flowchart¶

flowchart TD
    A[Load Preprocessed Data
mne.io.read_raw_fif] --> B[Fit ICA
ica.fit]
    B --> C[Find EOG Components
ica.find_bads_eog]
    C --> D[Review Components
ica.plot_components]
    D --> E[Apply ICA
ica.apply]
    E --> F[Save Cleaned Data
raw.save]

    %% Input file
    G["📁 sub-01_task-rest_desc-preproc_eeg.fif
From: Basic Preprocessing Pipeline"] --> A

    %% Inline data
    C --> V3["📊 Bad Components
[0, 15, 23] identified"]

    %% Final outputs
    F --> H["💾 sub-01_task-rest_desc-cleaned_eeg.fif
ICA-cleaned data"]
    D --> I["💾 sub-01_task-rest_desc-components_plot.png
Component visualization"]

    %% Quality metrics
    C --> Q1["📈 Components removed: 3/64
Explained variance: 15.7%"]
    E --> Q2["📈 EOG correlation: 0.82
SNR improvement: 4.2 dB"]

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

    class A,B,C,D,E,F processStep
    class G inputFile
    class H,I outputFile
    class V3 inlineData
    class Q1,Q2 qualityMetric

Key MNE-Python Features Demonstrated¶

ICA Functions and Methods¶

• mne.preprocessing.ICA: Create ICA object with FastICA algorithm
• ica.fit: Fit ICA decomposition to preprocessed data
• ica.find_bads_eog: Automatically identify EOG-correlated components
• ica.plot_components: Visualize component topographies and properties
• ica.apply: Remove identified artifact components from data

Advanced ICA Parameters¶

• Algorithm selection: FastICA vs. Infomax vs. Picard algorithms
• Component number: Automatic or manual specification
• Convergence criteria: Tolerance and maximum iterations
• Random state: Reproducible decomposition results

Example JSON Structure¶

The ICA fitting step demonstrates complex parameter documentation:

{
  "stepId": "2",
  "name": "Fit ICA Decomposition",
  "description": "Apply FastICA to decompose data into independent components.",
  "software": {
    "name": "MNE-Python",
    "version": "1.6.1",
    "functionCall": "ica.fit(raw, picks='eeg', decim=2, reject=dict(eeg=100e-6))"
  },
  "parameters": {
    "n_components": 64,
    "algorithm": "fastica",
    "fun": "logcosh",
    "max_iter": 200,
    "tol": 1e-4,
    "w_init": null,
    "whiten": true,
    "random_state": 42
  }
}

Artifact Identification Documentation¶

The component identification step includes correlation thresholds:

{
  "stepId": "3",
  "name": "Find EOG Components",
  "description": "Identify components correlated with EOG channels.",
  "qualityMetrics": {
    "eogChannels": ["EOG001", "EOG002"],
    "correlationThreshold": 0.7,
    "componentsFound": 3,
    "maxCorrelation": 0.82,
    "explainedVariance": 15.7
  }
}

ICA Analysis Features¶

Component Identification Methods¶

• EOG correlation: Correlation with electrooculogram channels
• Variance explained: Contribution to total signal variance
• Topographic patterns: Spatial distributions matching known artifacts
• Time course properties: Temporal characteristics of components

Quality Assessment Metrics¶

• Correlation values: Quantify artifact-component relationships
• Explained variance: Component contribution to signal
• Signal-to-noise ratio: Improvement after component removal
• Component stability: Reproducibility across runs

MNE-Python vs EEGLAB Comparison¶

ICA Workflow Patterns¶

Component Selection Strategy¶

1. Automatic detection: Use correlation thresholds with reference channels
2. Visual inspection: Review topographies and time courses
3. Combined approach: Automatic detection + manual verification
4. Conservative removal: Remove only clearly artifactual components

Quality Control Steps¶

• Component stability: Ensure reproducible decomposition
• Artifact effectiveness: Verify artifact reduction in cleaned data
• Signal preservation: Confirm minimal neural signal loss
• Validation metrics: Compare before/after signal characteristics

Usage Notes¶

This example demonstrates: - ICA workflow patterns for artifact removal - Parameter documentation for reproducible decomposition - Quality metrics for component evaluation - Visualization integration for manual review - Pipeline continuation feeding into further analysis

The pipeline showcases MNE-Python's flexible ICA capabilities while emphasizing the importance of quality control and parameter documentation for reproducible artifact removal.