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eeg-preprocess

Preprocess raw EEG recordings into ICA-ready data with filtering and artifact removal.

Updated Aug 27, 2026
Search Tags:#batch processing#reproducibility#eeg preprocessing#mne python#bad channel detection#re-referencing#line noise removal
One-click install
npx skills add https://github.com/dengzhe-hou/auto-eeg-analysis --skill eeg-preprocess
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: eeg-preprocess
Source: https://github.com/dengzhe-hou/auto-eeg-analysis/tree/main/skills/eeg-preprocess
Command: npx skills add https://github.com/dengzhe-hou/auto-eeg-analysis --skill eeg-preprocess

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Converts messy raw EEG recordings into a clean, standardized, preprocessed dataset by filtering, line-noise removal, bad-channel detection, interpolation, and re-referencing so downstream ERP/TFR/ICA steps become reliable and reproducible.

Core Features & Use Cases

  • Canonical EEG preprocessing pipeline: filter → bad-channel detection (RANSAC/PREP-style options) → interpolation → re-reference → optional resampling, with order preserved to reduce methodological bias.
  • Backend-validated execution: verifies the computation environment via ENVIRONMENT.json and uses MNE-Python for deterministic preprocessing.
  • Study-ready outputs: writes per-subject preprocessed FIF files plus structured summaries for auditing and methods reporting.
  • Use Case: Starting a new EEG study from raw recordings, especially when you need paper-grade preprocessing logs that later skills can verify.

Quick Start

Run eeg-preprocess in your project folder by pointing it at the study directory containing DATASET_BRIEF.md, ENVIRONMENT.json, and raw/ files.

Frequently Asked Questions about eeg-preprocess

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
What is the canonical order for EEG preprocessing before running ICA?▼

The canonical EEG preprocessing order applies filtering, line-noise removal, bad-channel detection, interpolation, and re-referencing before optional resampling to produce ICA-ready data.

How do I batch preprocess raw EEG recordings in MNE-Python?▼

Batch EEG preprocessing in MNE-Python requires pointing the pipeline at a study directory containing raw files, DATASET_BRIEF.md, and ENVIRONMENT.json to validate the backend and process multiple subjects.

Does EEG preprocessing support .bdf, .edf, and .set raw data formats?▼

EEG preprocessing supports raw formats including .bdf, .edf, .set, .fif, and .vhdr, reading them from the raw/ directory to standardize diverse recordings into clean FIF outputs.

How do I detect and interpolate bad channels in raw EEG data?▼

Bad channel detection in raw EEG data uses RANSAC or PREP-style options to identify noisy channels, which are then interpolated and re-referenced during the preprocessing pipeline.

Do I need an environment configuration file to start EEG preprocessing?▼

Yes, an ENVIRONMENT.json file is mandatory to validate the computation environment and ensure MNE-Python executes deterministic, reproducible preprocessing steps across all subjects.

Why does my EEG preprocessing pipeline need a dataset brief file?▼

A DATASET_BRIEF.md file is required as a mandatory project input to define study parameters, ensuring the preprocessing pipeline generates accurate per-subject summaries for downstream auditing.

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