mne-python-guide
MNE-Python Analysis Guide
Purpose
This skill encodes expert methodological knowledge for analyzing neurophysiological data (EEG, MEG, sEEG, ECoG, NIRS, eye-tracking) using MNE-Python (Gramfort et al., 2013; Gramfort et al., 2014). It covers the complete analysis pipeline with recommended parameters, code examples, and common pitfall warnings.
When to Use This Skill
Activate when the user:
- Asks about EEG/MEG/sEEG/ECoG/NIRS data analysis in Python
- Mentions MNE, MNE-Python, epochs, evoked, Raw, source estimate, ICA, ERP, ERF
- Wants to load neurophysiological data files (.fif, .edf, .bdf, .set, .vhdr, .mff, .cnt, .snirf)
- Needs preprocessing, time-frequency, source localization, decoding, or statistical testing guidance
- Wants to create MNE objects from numpy arrays or simulate data
Research Planning Protocol
- State the research question — What is the user investigating?
- Justify the method choice — Confirm MNE-Python fits their data type and goal.
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