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mne-python

Plan EEG and MEG preprocessing and analysis workflows in Python with MNE.

1|Updated May 16, 2026
Search Tags:#preprocessing#ica#time-frequency#eeg#source localization#meg#epochs
One-click install
npx skills add https://github.com/MarvinCui/NeuroForge --skill mne-python
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: mne-python
Source: https://github.com/MarvinCui/NeuroForge/tree/main/NeuroForge/skills/mne-python
Command: npx skills add https://github.com/MarvinCui/NeuroForge --skill mne-python

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) and assets (resource) components.

What problem does it solve?

Helps you reason about EEG/MEG preprocessing and analysis planning in Python using MNE, so you can choose appropriate steps and avoid unsafe or mismatched assumptions before running anything heavy.

Core Features & Use Cases

  • EEG/MEG preprocessing planning: filtering strategy, channel handling (bad channels), ICA planning, epoching, and event-to-epoch mapping.
  • Analysis planning across time and frequency: evoked responses and time-frequency (e.g., TFR) planning.
  • Source localization planning: forward/inverse workflow considerations including source space and spatial model assumptions.
  • Use case: You have an EEG/MEG recording and event questions (e.g., “Which events map to which conditions and what epochs/timing should I use?”), and you want a safe plan plus which references to consult first.

Quick Start

Provide your EEG/MEG data type (raw vs epochs), the file format you have, and what analysis question you want to answer, then ask the AI to plan an MNE-Python workflow and list the most relevant references and cautions for your scenario.

Frequently Asked Questions about mne-python

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

FAQPage Schema
How do I plan a safe EEG preprocessing workflow in Python?▼

To plan safe EEG preprocessing in Python, define your recording format, event mapping, and epoching boundaries first. This ensures appropriate filtering, bad channel handling, and ICA preparation before unattended heavy processing.

What is event-to-epoch mapping in MEG analysis?▼

Event-to-epoch mapping in MEG analysis links specific trigger events to experimental conditions and time windows. Proper mapping structures your epochs safely, ensuring accurate evoked response and time-frequency analysis setup.

Can I use Python to prepare a source localization workflow for MEG data?▼

Yes, you can prepare a source localization workflow for MEG data by planning forward and inverse models. This involves defining source space and spatial model assumptions before running heavy processing commands.

Do I need raw or epoched EEG data to start time-frequency analysis planning?▼

You can start time-frequency analysis planning with either raw or epoched EEG data. Specifying your current data type helps structure the safe TFR planning and identify appropriate inspection references first.

What are the limitations of automated ICA planning for EEG recordings?▼

Automated ICA planning limitations include mismatched assumptions if domain specifics are ignored. It prioritizes inspection, documentation, and safe command suggestions rather than unattended heavy processing for EEG recordings.

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