eeg-preprocess

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.