exploring-brain-networks-eeg-meg

name	exploring-brain-networks-eeg-meg
description	Skill for exploring brain networks using noninvasive electrophysiological measurements (EEG/MEG) based on arXiv:2607.17602v1. Covers forward/inverse problems, source reconstruction, connectivity measures, and analysis pipelines.
license	Complete terms in LICENSE.txt
metadata	{"arxiv_id":"2607.17602v1","published":"2026-07-20","authors":["Unknown"]}
tags	["eeg","meg","brain network","connectivity","source localization"]

Exploring Brain Networks Using Noninvasive Electrophysiological Measurements

Based on arXiv:2607.17602v1 - "Exploring Brain Networks Using Noninvasive Electrophysiological Measurements: Methods and Applications"

Overview

This skill provides a practical guide for analyzing brain networks using EEG and MEG data. It covers the methodological foundations from forward/inverse modeling to connectivity analysis and practical workflows using open-source tools like Brainstorm.

Key Concepts

1. Forward and Inverse Problems

• Forward problem: Predicting sensor signals from known neural sources
• Inverse problem: Estimating neural sources from sensor measurements (ill-posed)
• Requires accurate head modeling and source reconstruction techniques

2. Source Reconstruction Techniques

• Minimum norm estimates (MNE)
• Beamforming (LCMV)
• Multiple sparse priors (MSP)
• Dipole fitting

3. Mitigating Volume Conduction and Signal Leakage

• Orthogonalization approaches
• Signal space projection (SSP)
• Surface Laplacian
• Imaginary part of coherency
• Phase lag index

4. Functional and Effective Connectivity Measures

• Functional (symmetric, undirected):
  • Coherence
  • Phase synchronization (PLV, PLI)
  • Amplitude envelope correlation
• Effective (directed, causal):
  • Granger causality
  • Dynamic causal modeling (DCM)
  • Transfer entropy

5. Analysis Pipelines

• Preprocessing (filtering, artifact removal)
• Source localization
• Connectivity estimation
• Statistical validation
• Visualization (brain networks, graphs)

Practical Workflow (Brainstorm-centric)

1. Data Import: Load raw EEG/MEG files (EDF, BDF, FIF, etc.)
2. Preprocessing:
   • Bandpass filtering (typically 1-40 Hz)
   • Artifact removal (ICA, SSP, regression)
   • Bad channel detection/interpolation
3. Head Modeling:
   • Create volume conduction model (single sphere, realistically shaped)
   • Align MRI with sensor positions
4. Source Localization:
   • Compute leadfield matrix
   • Apply inverse method (MNE, beamforming)
   • Extract source time series
5. Connectivity Analysis:
   • Choose appropriate measure based on hypothesis
   • Compute connectivity matrices (frequency-specific if needed)
   • Apply statistical thresholding (permutation testing, FDR)
6. Network Analysis:
   • Graph theoretical metrics (degree, betweenness, clustering, path length)
   • Community detection
   • Rich-club analysis
7. Visualization:
   • Source activations on cortical surface
   • Connectivity matrices (circular, matrix plots)
   • Brain networks (glass brain, force-directed layouts)

Emerging Approaches

• Time-varying connectivity: Sliding windows, state-space models, hidden Markov models
• Cross-frequency interactions: Phase-amplitude coupling, cross-frequency coherence
• Multivariate decoding: MVPA on source space, decoding networks

Tools and Resources

• Brainstorm: https://neuroimage.usc.edu/brainstorm/
• EEGLAB: https://sccn.ucsd.edu/eeglab/
• MEG-Python (MNE-Python): https://mne.tools/
• FieldTrip: https://www.fieldtriptoolbox.org/
• TVB: The Virtual Brain (https://www.thevirtualbrain.org/)
• Graph analysis: Brain Connectivity Toolbox (https://sites.google.com/site/bctnet/)

Validation and Best Practices

• Validate forward model with simulated dipoles
• Test inverse solutions with known source configurations
• Use surrogate data testing for connectivity measures
• Correct for multiple comparisons
• Report parameters and preprocessing steps for reproducibility

Activation Keywords

eeg meg brain network connectivity source localization brainstorm mne python neuroscience electrophysiology

References

1. arXiv:2607.17602v1 - Exploring Brain Networks Using Noninvasive Electrophysiological Measurements: Methods and Applications
2. Brainstorm tutorials and documentation
3. MNE-Python documentation
4. FieldTrip tutorials