What types of data can I analyze with this MNE skill?

You can analyze various human neurophysiological data formats, including EEG (Electroencephalography), MEG (Magnetoencephalography), sEEG (Stereotactic EEG), and ECoG (Electrocorticography).

Does this skill support 3D brain visualization?

Yes, it provides patterns for source localization which allows you to map signals back to brain anatomy and visualize activity in source space.

Why should I filter data before epoching in MNE?

Filtering continuous raw data first prevents edge artifacts that can occur if you filter short epochs individually, ensuring better signal integrity for averaged responses.

Can I use this for real-time brain signal processing?

While MNE-Python supports some real-time capabilities, this skill focuses on the standard analysis pipeline including preprocessing, epoching, and source localization for recorded datasets.

MNE Neurophysiology Analysis

Name: MNE Neurophysiology Analysis
Author: tondevrel

bytondevrel

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データサイエンスとML

Analyzes and visualizes human neurophysiological data including EEG, MEG, and intracranial recordings using Python-based best practices.

MNE is a comprehensive toolkit designed to facilitate the exploration and analysis of complex brain signals within Claude Code. It provides a structured framework for the entire neurophysiological data pipeline, from loading raw data and filtering noise to performing advanced source localization and connectivity analysis. This skill is indispensable for researchers and data scientists working on clinical studies, cognitive neuroscience experiments, or preparing high-dimensional neuro-data for machine learning models, ensuring adherence to scientific standards for signal integrity and artifact rejection.

主な機能

01Automated event detection and epoching for trial-based analysis

02Frequency analysis and spectral connectivity computation

03Source space reconstruction for mapping signals to brain anatomy

041 GitHub stars

05Comprehensive preprocessing for EEG, MEG, sEEG, and ECoG data

06Sophisticated signal filtering and artifact removal pipelines

ユースケース

01Performing source localization to identify the origin of neural activity

02Processing and cleaning clinical EEG recordings for diagnostic research

03Preprocessing neurophysiological signals for machine learning classification

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add tondevrel/scientific-agent-skills mne

For use in Claude.ai and ChatGPT

Download Skill