hermes-brain-connectivity

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.

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.

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.

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.

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.

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.