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mne-eeg-tool

Load, preprocess, and analyze EEG data with MNE-Python pipelines.

78|3|Updated Mar 25, 2026
Search Tags:#connectivity#erp#signal-processing#artifact-removal#eeg#microstates#mne
One-click install
npx skills add https://github.com/CUHK-AIM-Group/NeuroClaw --skill mne-eeg-tool
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: mne-eeg-tool
Source: https://github.com/CUHK-AIM-Group/NeuroClaw/tree/main/skills/mne-eeg-tool
Command: npx skills add https://github.com/CUHK-AIM-Group/NeuroClaw --skill mne-eeg-tool

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires mne, pyentrp, pandas, numpy, matplotlib, mne-connectivity, mne-microstates, and includes scripts (resource) components.

What problem does it solve?

EEG data processing in NeuroClaw typically involves long, error-prone pipelines spanning data loading, preprocessing, artifact handling, and feature extraction. This skill provides a ready-to-run, end-to-end MNE-Python implementation to streamline EEG workflows and ensure reproducible results.

Core Features & Use Cases

  • Load EEG data from .set, .edf/.bdf, and .fif formats with validation
  • Apply robust preprocessing: filtering, resampling, artifact removal via ICA and AutoReject
  • Compute connectivity, ERP features, spectral bands, and advanced analyses like alpha asymmetry and microstate analysis
  • One-click full pipeline: use eeg_pipeline.py to execute end-to-end processing on resting-state or event-related data

Quick Start

Run the eeg_pipeline.py wrapper with --input path_to_raw_file and optional --resting flag to execute the full EEG processing pipeline.

Frequently Asked Questions about mne-eeg-tool

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

FAQPage Schema
How do I run a full EEG processing pipeline from raw data to feature extraction?▼

EEG artifact removal can be performed using ICA and AutoReject within the MNE-Python preprocessing pipeline, automatically identifying and correcting ocular and muscular artifacts to ensure clean data for downstream analysis.

What EEG data formats does this MNE-Python pipeline support for loading?▼

EEG event-related potential features are extracted by processing event-related datasets through the MNE-Python pipeline, calculating ERP characteristics and generating standardized outputs for subsequent analysis.

Can I process resting-state EEG data and compute connectivity and microstates?▼

EEG event-related potential features are extracted by processing event-related datasets through the MNE-Python pipeline, calculating ERP characteristics and generating standardized outputs for subsequent analysis.

How do I remove artifacts from EEG data using MNE-Python?▼

Yes, this pipeline computes resting-state EEG features including spectral band power, alpha asymmetry, functional connectivity, and microstate analysis, providing standardized outputs for neuroscience research.

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