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
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