Eeg Preprocessing Pipeline Guide

EEG Preprocessing Pipeline Guide

Purpose

EEG preprocessing transforms raw electrophysiological recordings into clean data suitable for analysis. Unlike generic signal processing, every preprocessing decision in EEG involves domain-specific trade-offs: filtering at the wrong cutoff distorts ERP component morphology, choosing the wrong reference scheme biases topographic maps, and automated artifact rejection with incorrect parameters either leaves artifacts in the data or removes real neural signal.

A competent programmer without EEG training would not know that a 1 Hz high-pass filter is needed before ICA but distorts slow ERP components, that average reference requires a minimum of 64 channels, or that the order of preprocessing steps matters critically. This skill encodes the domain judgment required to build a correct EEG preprocessing pipeline.

When to Use This Skill

• Setting up an EEG preprocessing pipeline for ERP, time-frequency, or connectivity analysis
• Choosing filter parameters for specific analysis goals
• Deciding between ICA and ASR for artifact removal
• Selecting an appropriate re-referencing scheme
• Performing quality control on preprocessed EEG data
• Reviewing or troubleshooting an existing EEG preprocessing pipeline

Research Planning Protocol

Before executing the domain-specific steps below, you MUST:

1. State the research question — What specific question is this analysis/paradigm addressing?
2. Justify the method choice — Why is this approach appropriate? What alternatives were considered?
3. Declare expected outcomes — What results would support vs. refute the hypothesis?
4. Note assumptions and limitations — What does this method assume? Where could it mislead?
5. Present the plan to the user and WAIT for confirmation before proceeding.

For detailed methodology guidance, see the research-literacy skill.

⚠️ Verification Notice

This skill was generated by AI from academic literature. All parameters, thresholds, and citations require independent verification before use in research. If you find errors, please open an issue.

Standard Preprocessing Pipeline Order

The recommended order of preprocessing steps, based on established best practices (Luck, 2014; Onton & Makeig, 2006; Bigdely-Shamlo et al., 2015):

1. Import and inspect raw data
2. Remove (mark) bad channels
3. High-pass filter
4. Line noise removal
5. Re-reference
6. ICA decomposition and artifact removal
7. Interpolate bad channels
8. Epoch and baseline correct
9. Epoch rejection by amplitude threshold

Critical ordering constraints:

• ICA must come after high-pass filtering (Step 3) because low-frequency drift degrades ICA decomposition (Winkler et al., 2015)
• Bad channel removal (Step 2) must precede ICA because bad channels degrade component estimation
• Bad channel interpolation (Step 7) must come after ICA to avoid ICA learning the interpolated data
• Re-referencing (Step 5) should precede ICA so that all components are in a common reference frame

Step 1: Import and Inspect Raw Data

• Convert to a standard format (EDF, BDF, or tool-native) if needed
• Visually scroll through the entire recording to identify gross artifacts (disconnected electrodes, large muscle bursts, saturated channels)
• Note segments with excessive noise for later rejection
• Check sampling rate: 250-512 Hz typical for ERP; 1000+ Hz for high-frequency oscillatory analysis (Cohen, 2014)

Step 2: Remove Bad Channels

Bad channels contribute noise to re-referencing, ICA, and spatial interpolation. Identify them before other steps.

Identification Criteria

Practical Limits

• Remove no more than 10% of channels (e.g., 6 of 64). If more channels are bad, consider re-collecting data (Keil et al., 2014)
• Mark bad channels for later interpolation (Step 7); do not interpolate yet

Step 3: High-Pass Filtering

High-pass filtering removes slow drifts from skin potentials, electrode drift, and movement artifacts.

Critical domain knowledge: For ERP studies, use 0.1 Hz for the final analysis data but 1 Hz for the ICA decomposition step. The recommended workflow is:

1. Filter a copy of the data at 1 Hz for ICA
2. Run ICA on the 1 Hz-filtered copy
3. Apply the ICA weights (unmixing matrix) to the original 0.1 Hz-filtered data
4. This preserves slow ERP components while giving ICA clean decomposition (Winkler et al., 2015)

Why not 1 Hz for ERPs? A 1 Hz high-pass filter distorts ERP waveforms by introducing artificial pre-stimulus baseline shifts and reducing the amplitude of sustained components like the sustained negativity or the P3b (Tanner et al., 2015; Acunzo et al., 2012).

Filter Specifications

Domain warning: IIR (Butterworth) filters introduce phase distortion that shifts ERP peak latencies. Always use FIR zero-phase filters for ERP analysis unless there is a specific reason for causal filtering (Widmann et al., 2015).

Step 4: Line Noise Removal

Remove power line noise at 50 Hz (Europe, Asia) or 60 Hz (Americas) and harmonics.

Recommendation: Use CleanLine or ZapLine over notch filters. Notch filters create spectral distortion ("ringing") and remove real neural oscillatory power in the gamma band near 50/60 Hz (Muthukumaraswamy, 2013).

Step 5: Re-Referencing

EEG signals are always measured as potential differences relative to a reference. The choice of reference affects all downstream analyses.

Decision logic:

How many clean channels do you have?
 |
 +-- >= 64 with good head coverage
 | --> Average reference (Dien, 1998)
 |
 +-- 32-63 channels
 | --> Linked mastoids or average reference
 | (average reference becomes unreliable with sparse coverage)
 |
 +-- < 32 channels
 --> Linked mastoids (Luck, 2014)

Domain warning: Average reference assumes dense, uniform electrode coverage of the head. With sparse arrays (< 64 channels) or missing channels, the average reference is biased and can distort topographies (Dien, 1998).

Step 6: ICA Decomposition and Artifact Removal

Independent Component Analysis (ICA) separates the EEG signal into statistically independent spatial components, allowing identification and removal of artifact sources (Onton & Makeig, 2006).

ICA Algorithm Selection

Recommendation: Use Extended Infomax (default in EEGLAB) or PICARD (default in MNE-Python) for most analyses. AMICA is preferred for high-quality research when computation time is not a constraint.

Data Requirements for ICA

• Minimum data points: At least 20 * n_channels^2 data points for stable decomposition (Onton & Makeig, 2006). For 64 channels: 20 * 64^2 = 81,920 samples (~5.3 minutes at 256 Hz)
• High-pass filter at 1 Hz before ICA (Winkler et al., 2015)
• Remove bad channels before ICA (bad channels produce bad components)

Artifact Component Identification

Automated Classification: ICLabel (Pion-Tonachini et al., 2019)

ICLabel classifies ICA components into 7 categories with probability estimates:

Recommended threshold: Remove components classified as non-brain with probability > 0.80 (conservative) or > 0.50 (liberal) (Pion-Tonachini et al., 2019).

Manual Identification Criteria

Domain insight: Typically remove 1-3 components for eye artifacts and 0-2 for other artifact types. Removing more than 5-6 components total risks removing neural signal. If many components appear artifactual, the data quality may be too poor for reliable analysis (Onton & Makeig, 2006).

Alternative: ASR (Artifact Subspace Reconstruction)

ASR is a real-time-capable method that identifies and reconstructs artifact-contaminated data segments (Mullen et al., 2015).

When to use ASR vs. ICA:

Is data heavily contaminated with non-stationary artifacts?
 |
 +-- YES --> ASR first (for gross artifact removal), then ICA for residual eye artifacts
 |
 +-- NO --> ICA alone is usually sufficient

Domain insight: ASR and ICA can be combined. Apply ASR first to remove large transient artifacts (burst criterion = 20 SD), then run ICA on the ASR-cleaned data for residual artifact removal (Chang et al., 2020).

Step 7: Interpolate Bad Channels

After ICA, interpolate the bad channels identified in Step 2.

• Method: Spherical spline interpolation (Perrin et al., 1989)
• Maximum interpolation: No more than 10% of channels (Keil et al., 2014)
• Order: Interpolate after ICA so that ICA does not learn interpolated (non-independent) data
• Verify: Check that interpolated channel time courses are consistent with neighbors

Step 8: Epoch and Baseline Correct

• Epoch time window: Typically -200 to 800 ms for ERP; adjust based on component of interest (Luck, 2014)
• Baseline window: -200 to 0 ms pre-stimulus (standard for ERP; Luck, 2014)
• Baseline correction: Subtract the mean of the baseline window from each time point in the epoch

Domain warning: For time-frequency analysis, use a longer baseline period (-500 to -200 ms) and avoid the immediate pre-stimulus period to prevent contamination by anticipatory activity. Alternatively, use single-trial baseline normalization (Cohen, 2014).