mne-python-guide

name	mne-python-guide
description	Domain-validated pipeline guidance for EEG/MEG data analysis using MNE-Python: data loading, preprocessing (filtering, ICA, re-referencing), epoching, ERP/ERF computation, time-frequency decomposition, source localization, decoding/MVPA, statistical testing, simulation, and visualization. Use this skill whenever the user works with EEG/MEG/sEEG/ECoG/NIRS/eye-tracking data in Python, mentions MNE, or needs neurophysiological analysis guidance.
domain	neuroimaging-methods
version	2.0.0
authors	["Claude (AI-assisted)"]
papers	["Gramfort et al., 2013","Gramfort et al., 2014","Luck, 2014","Jas et al., 2018","Dale et al., 2000","Pascual-Marqui, 2002","Tallon-Baudry et al., 1997","Maris & Oostenveld, 2007","Blankertz et al., 2008","King & Dehaene, 2014"]
dependencies	{"required":["research-literacy"]}
review_status	ai-generated

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

1. State the research question — What is the user investigating?
2. Justify the method choice — Confirm MNE-Python fits their data type and goal.
3. Declare expected outcomes — What output format? (ERP plots, TFR maps, source maps, decoding accuracy)
4. Note assumptions and limitations — Data quality, sample size, MRI availability, montage info.
5. Present the plan and WAIT for confirmation before writing code.

Verification Notice

This skill was generated by AI from MNE-Python source code and academic literature. All parameters, thresholds, and citations require independent verification. If you find errors, please open an issue at https://github.com/NeuroAIHub/awesome_cognitive_and_neuroscience_skills/issues.

Reference Files (Progressive Disclosure)

This skill uses layered references. Read the relevant file when the user's question goes deeper than the overview below:

Topic	Reference File	When to Read
Data I/O (30+ formats)	references/io_formats.md	User asks about loading specific file formats, creating objects from arrays, or exporting
Preprocessing	references/preprocessing.md	User needs ICA details, Maxwell filtering, artifact annotation, bad channel detection, CSD, fNIRS/iEEG-specific preprocessing
Time-Frequency