Example: Time-Frequency Analysis (EEGLAB)¶

This page explains the time_frequency_analysis_pipeline_eeglab.signalJourney.json example file, which documents time-frequency decomposition using EEGLAB's timef function.

Pipeline Overview¶

This EEGLAB pipeline demonstrates time-frequency analysis using EEGLAB's wavelet decomposition capabilities: - Load cleaned data from ICA decomposition pipeline - Extract event-related epochs for time-frequency analysis - Compute time-frequency decomposition using timef function - Apply baseline correction and save results - Generate visualization plots for ERSP and ITC

Pipeline Flowchart¶

flowchart TD
    A[Load Cleaned Data
pop_loadset] --> B[Extract Epochs
pop_epoch]
    B --> C[Compute ERSP
timef]
    C --> D[Compute ITC
newtimef]
    D --> E[Save Results
pop_saveset]

    %% Input file
    F["📁 sub-01_task-rest_desc-cleaned_eeg.set
From: ICA Decomposition Pipeline"] --> A

    %% Inline data
    C --> V1["📊 Frequencies
[3:0.5:30] Hz"]
    C --> V2["📊 Cycles
[3 0.5] wavelet"]
    C --> V3["📊 Baseline
[-200 0] ms"]

    %% Final outputs
    E --> G["💾 sub-01_task-rest_desc-ersp_eeg.set
ERSP results"]
    E --> H["💾 sub-01_task-rest_desc-itc_eeg.set
ITC results"]
    D --> I["💾 sub-01_task-rest_desc-tfr_plot.fig
EEGLAB figure"]

    %% Quality metrics
    C --> Q1["📈 Frequency bins: 55
Time points: 200"]
    D --> Q2["📈 Baseline mode: Relative
Significance: p<0.01"]

    %% 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 processStep
    class F inputFile
    class G,H,I outputFile
    class V1,V2,V3 inlineData
    class Q1,Q2 qualityMetric

Key EEGLAB Features Demonstrated¶

EEGLAB Time-Frequency Functions¶

• pop_epoch: Extract event-related epochs from continuous data
• timef: Time-frequency decomposition with Morlet wavelets
• newtimef: Enhanced time-frequency analysis with bootstrap statistics
• pop_saveset: Save results in EEGLAB dataset format

EEGLAB-Specific Parameters¶

• Wavelet specification: EEGLAB's [cycles freqs] format
• Baseline correction: Built into timef function
• Statistical testing: Bootstrap significance testing
• Visualization: Automatic ERSP and ITC plotting

Example JSON Structure¶

The EEGLAB time-frequency computation demonstrates integrated analysis:

{
  "stepId": "3",
  "name": "Compute ERSP",
  "description": "Calculate event-related spectral perturbation using EEGLAB timef.",
  "software": {
    "name": "EEGLAB", 
    "version": "2023.1",
    "functionCall": "timef(EEG.data, frames, tlimits, srate, cycles, 'baseline', [-200 0], 'plotitc', 'off')"
  },
  "parameters": {
    "frames": 1000,
    "tlimits": [-500, 1000],
    "srate": 500,
    "cycles": [3, 0.5],
    "freqs": [3, 30],
    "nfreqs": 55,
    "baseline": [-200, 0],
    "baselinetype": "relative",
    "plotitc": "off",
    "plotersp": "on"
  }
}

Integrated Output Documentation¶

EEGLAB's timef produces multiple outputs simultaneously:

"outputTargets": [
  {
    "targetType": "in-memory",
    "format": "double",
    "description": "ERSP matrix (freqs x times x trials).",
    "variableName": "ersp"
  },
  {
    "targetType": "in-memory", 
    "format": "double",
    "description": "ITC matrix (freqs x times).",
    "variableName": "itc"
  },
  {
    "targetType": "in-memory",
    "format": "double",
    "description": "Power base matrix for baseline correction.",
    "variableName": "powbase"
  }
]

EEGLAB Time-Frequency Features¶

Wavelet Parameter System¶

• Cycles specification: [min_cycles max_cycles] format
• Automatic scaling: Cycles increase linearly with frequency
• Frequency resolution: Number of frequency bins automatically calculated
• Time resolution: Determined by cycle parameters and sampling rate

Baseline Correction Options¶

• Relative: (power - baseline) / baseline
• Absolute: power - baseline
• Relchange: (power - baseline) / baseline * 100
• Log: 10 * log10(power / baseline)

Bootstrap Statistics¶

• Significance testing: Built-in permutation testing
• Multiple comparison correction: False discovery rate (FDR)
• Confidence intervals: Bootstrap confidence bounds
• Alpha level: Customizable significance threshold

EEGLAB vs MNE-Python Comparison¶

EEGLAB-Specific Workflow¶

Integrated Analysis Approach¶

EEGLAB's timef provides: 1. Combined ERSP/ITC computation in single function call 2. Automatic baseline correction with multiple methods 3. Built-in statistical testing via bootstrap procedures 4. Immediate visualization with publication-ready plots

EEG Dataset Integration¶

• Event information automatically extracted from EEG.event
• Channel locations used for topographic plotting
• Dataset history updated with analysis parameters
• STUDY compatibility for group-level analysis

Usage Notes¶

This example demonstrates: - EEGLAB's integrated approach to time-frequency analysis - Automatic parameter optimization for wavelet decomposition - Built-in statistical testing for significance assessment - Multi-output handling with ERSP, ITC, and baseline data - EEGLAB visualization with publication-ready plots

The pipeline showcases EEGLAB's comprehensive time-frequency analysis capabilities with emphasis on ease-of-use and integrated statistical testing while maintaining full parameter documentation for reproducibility.