mne-python

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.