hermes-brain-connectivity

Quantify functional and effective brain connectivity from EEG, MEG, and fMRI signals.

2|Updated Feb 12, 2026

Search Tags:#neuroscience #hermes #eeg #brain-connectivity #granger-causality #functional-connectivity #meg

Ready to run

npx skills add https://github.com/hiyenwong/ai_collection --skill hermes-brain-connectivity

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: hermes-brain-connectivity
Source: https://github.com/hiyenwong/ai_collection/tree/main/collection/skills/hermes-brain-connectivity
Command: npx skills add https://github.com/hiyenwong/ai_collection --skill hermes-brain-connectivity

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Integrates multiple functional and effective connectivity analyses into a single toolbox, simplifying the workflow for EEG/MEG and neural signal studies.

Core Features & Use Cases

FC methods: cross-correlation, coherence, phase-locking value, mutual information.
EC methods: Granger causality, transfer entropy, directed transfer function (DTF), and PDC.
Python implementation ready for EEG/MEG/fMRI data preprocessing, connectivity computation, and network analysis.
Use Case: Researchers can load multi-channel data, compute a connectivity matrix, and explore network properties for brain network studies.

Quick Start

Load multi-channel EEG/MEG data, preprocess it, and compute the functional and effective connectivity matrix using the toolbox.

Frequently Asked Questions about hermes-brain-connectivity

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

FAQPage Schema

How do I compute functional brain connectivity from EEG data?▼

To compute functional brain connectivity from EEG data, load multi-channel signals, preprocess them, and use methods like cross-correlation, coherence, phase-locking value, or mutual information to generate a connectivity matrix.

Can I perform Granger causality analysis on MEG signals in Python?▼

Yes, you can perform Granger causality analysis on MEG signals in Python. The toolbox computes effective connectivity using Granger causality, transfer entropy, directed transfer function, and partial directed coherence.

What is the difference between functional and effective connectivity in neural signal analysis?▼

Functional connectivity measures statistical dependencies like coherence and phase-locking value between neural signals, whereas effective connectivity uses directional methods like Granger causality to infer causal influences across brain regions.

Does this brain connectivity toolbox support fMRI data preprocessing workflows?▼

Yes, the brain connectivity toolbox supports fMRI data preprocessing workflows. It applies cross-method analyses to EEG, MEG, and fMRI data, enabling preprocessing, connectivity computation, and network analysis across modalities.

How do I analyze network properties after calculating a brain connectivity matrix?▼

After calculating a brain connectivity matrix, you can analyze network properties by applying network analysis techniques directly within the toolbox to explore brain network characteristics from the computed functional or effective connectivity results.

Do I need external libraries to calculate transfer entropy and directed transfer function?▼

You need external Python libraries like numpy, scipy, and statsmodels to calculate transfer entropy and directed transfer function, as the toolbox implements these effective connectivity methods using these specific dependencies.