hermes-brain-connectivity
CommunityUnified brain connectivity analysis toolbox
Education & Research#neuroscience#hermes#eeg#brain-connectivity#granger-causality#functional-connectivity#meg
Authorhiyenwong
Version1.0.0
Installs0
System Documentation
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.
Dependency Matrix
Required Modules
None requiredComponents
Standard package💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: hermes-brain-connectivity Download link: https://github.com/hiyenwong/ai_collection/archive/main.zip#hermes-brain-connectivity Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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