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mne-eeg

Process EEG/MEG recordings with MNE-Python for ERP, time-frequency, and band-power analyses.

33|6|Updated Mar 17, 2026
Search Tags:#neuroscience#time-frequency#eeg#power spectral density#mne-python#ica artifact removal#erp analysis
One-click install
npx skills add https://github.com/xjtulyc/awesome-rosetta-skills --skill mne-eeg
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: mne-eeg
Source: https://github.com/xjtulyc/awesome-rosetta-skills/tree/main/skills/05-neuroscience/mne-eeg
Command: npx skills add https://github.com/xjtulyc/awesome-rosetta-skills --skill mne-eeg

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It solves the challenge of turning raw EEG/MEG recordings into interpretable neural metrics by providing a complete, repeatable analysis workflow (preprocessing, artifact removal, ERP, time-frequency, and resting-state PSD) instead of leaving you to stitch steps together manually.

Core Features & Use Cases

  • Full M/EEG preprocessing pipeline: load common EEG formats, apply montage handling, notch + bandpass filtering, re-referencing, bad-channel interpolation, optional resampling.
  • ICA-based artifact removal: fit ICA with MNE conventions, detect and exclude EOG/ECG components, and return cleaned raw data.
  • Event-related and spectral analyses: epoch around annotated or stim-derived events, compute condition-specific ERPs, estimate Morlet TFR, and compute band-averaged PSD (delta/theta/alpha/beta/gamma) for resting-state comparisons.
  • Use Case: Analyze an auditory oddball experiment by preprocessing raw EEG, removing eye/heart artifacts with ICA, extracting ERPs for left/right conditions, and quantifying peak N100 and P300 amplitudes at a target scalp channel.

Quick Start

Use the mne-eeg skill to compute ERP and band power from your EEG file by instructing it to load your recording, run preprocessing plus ICA cleaning, epoch events into conditions, then generate ERPs, a Morlet time-frequency map, and delta/theta/alpha/beta/gamma PSD outputs.

Frequently Asked Questions about mne-eeg

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

FAQPage Schema
How do I run a complete EEG analysis pipeline from raw recordings to ERP extraction?▼

To run EEG analysis, load raw recordings, apply filtering and re-referencing, remove artifacts with ICA, extract event-related epochs, and compute condition-specific ERPs. This pipeline handles preprocessing and ERP extraction end-to-end for publication-style outputs.

Can I use MNE-Python to remove ECG and EOG artifacts from raw EEG data?▼

Yes, you can remove EOG and ECG artifacts by fitting ICA to raw EEG data, detecting ocular and cardiac components, and excluding them to return cleaned data. This ensures artifact-free signals for downstream neural metric extraction.

Does this EEG preprocessing workflow support BrainVision, EEGLab, and BDF file formats?▼

This workflow supports BrainVision, EEGLab, BDF, EDF, and FIF formats for loading raw recordings. MNE-Python handles the data parsing, enabling consistent preprocessing, filtering, and epoch extraction across these diverse electrophysiology file types.

How do I compute resting-state band power and power spectral density for EEG signals?▼

Compute resting-state band power by applying Welch-based PSD extraction to cleaned EEG signals, estimating delta, theta, alpha, beta, and gamma bands. This quantifies spectral metrics for eyes-open and eyes-closed resting-state comparisons.

What is the best way to estimate time-frequency representations for an auditory oddball EEG paradigm?▼

Estimate time-frequency representations by epoching auditory oddball events and applying Morlet TFR to the cleaned data. This isolates oscillatory dynamics across conditions, yielding publication-style time-frequency maps for electrophysiology analysis.

Why do I need to interpolate bad channels during MEG and EEG signal processing?▼

Interpolating bad channels during signal processing replaces corrupted sensor data with estimated values from neighboring channels. This maintains consistent spatial montage coverage before ICA fitting and event-related epoch extraction.

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