eeg-meg-brain-network-analysis

name	eeg-meg-brain-network-analysis
description	Skill for analyzing brain networks using noninvasive electrophysiological measurements (EEG/MEG) based on arXiv:2607.17602v1 'Exploring Brain Networks Using Noninvasive Electrophysiological Measurements: Methods and Applications'

Overview

This skill provides a structured approach to analyzing brain networks using EEG and MEG data, covering preprocessing, source reconstruction, connectivity analysis, and network-based methods as outlined in the referenced paper.

Activation

Trigger this skill when you need to:

• Analyze EEG/MEG data for brain network connectivity
• Apply source localization techniques
• Compute functional or effective connectivity measures
• Perform network-based analysis on electrophysiological data

Steps

1. Data Preprocessing

• Import raw EEG/MEG data using appropriate toolboxes (e.g., EEGLAB, MNE-Python, FieldTrip)
• Apply filtering: bandpass (typically 0.1-100 Hz) and notch filters (50/60 Hz) to remove line noise
• Detrend and remove bad channels using visual inspection or automated methods
• Re-reference to average reference or reference electrode standard technique (REST)
• Segment data into epochs of interest (e.g., stimulus-locked or resting-state)
• Baseline correction if applicable
• Artifact removal: Use ICA or PCA to remove ocular, cardiac, and muscle artifacts

2. Forward Modeling and Source Reconstruction

• Obtain individual MRI or use template MRI (e.g., MNI) for head model construction
• Conductivity assignment: Assign conductivities to scalp, skull, CSF, brain tissues
• Compute lead field matrix using boundary element method (BEM) or finite element method (FEM)
• Apply inverse solution:
  • Minimum norm estimate (MNE)
  • LORETA (Low Resolution Electromagnetic Tomography)
  • Beamforming (LCMV - Linearly Constrained Minimum Variance)
  • MUSIC (Multiple Signal Classification)
• Source space definition: Choose cortical surface mesh or volumetric grid
• Apply orientation constraints (fixed or free orientation)
• Regularization: Tikhonov regularization with appropriate lambda

3. Connectivity Analysis

Choose appropriate connectivity measure based on research question:

Functional Connectivity (Zero-lag or linear mixing)

• Coherence: Frequency-specific correlation
• Phase Locking Value (PLV): Phase synchronization
• Phase Lag Index (PLI): Asymmetric component of phase synchronization
• Imaginary part of coherency: Eliminates zero-lag artifacts
• Amplitude Envelope Correlation (AEC): Correlates amplitude envelopes in frequency bands

Effective Connectivity (Directional, causal inference)

• Granger Causality (GC): Predictive influence in time/frequency domain
• Directed Transfer Function (DTF): Frequency-domain GC
• Partial Directed Coherence (PDC): Normalized GC
• Dynamic Causal Modeling (DCM): Biophysical model of neuronal populations
• Transfer Entropy (TE): Model-free, information-theoretic measure

4. Network Construction and Analysis

• Define nodes: Brain regions of interest (ROIs) from atlas (e.g., AAL, Desikan-Killiany) or data-driven parcels
• Define edges: Weighted by connectivity measure values above threshold
• Apply thresholding:
  • Proportional threshold (keep top X% strongest connections)
  • Absolute threshold (statistical significance via surrogate testing)
  • Adaptive thresholding (e.g., False Discovery Rate correction)
• Compute network metrics:
  • Global: Characteristic path length, clustering coefficient, small-worldness, global efficiency
  • Nodal: Degree, betweenness centrality, eigenvector centrality, local efficiency
  • Motif analysis: Frequency of subgraph patterns
  • Rich-club organization: Rich-club coefficient
• Statistical comparison: Use permutation testing or false discovery rate for group comparisons

5. Advanced Analyses

• Time-varying connectivity: Sliding window or time-frequency resolved methods
• Cross-frequency coupling: Phase-amplitude modulation (PAC) analysis
• Multiscale entropy: Complexity of neural signals
• Machine learning integration: Use network features for classification/regression
• Simulation validation: Compare with generative models (e.g., Kuramoto oscillators)

Validation and Quality Control

• Signal-to-noise ratio (SNR) assessment
• Split-half reliability or test-retest reliability
• Surrogate data testing for significance of connectivity measures
• Comparison with anatomical connectivity (e.g., from DTI)
• Visual inspection of source estimates and connectivity matrices
• Control for volume conduction and signal leakage using appropriate metrics (e.g., PLI, imaginary coherency)

Software and Tools

• EEGLAB (MATLAB) - https://sccn.ucsd.edu/eeglab/
• MEG-Python (MNE-Python) - https://mne.tools/
• FieldTrip (MATLAB) - https://www.fieldtriptoolbox.org/
• Brainstorm (MATLAB) - https://neuroimage.usc.edu/brainstorm/
• NetMat (MATLAB) - https://github.com/CollectiveDynamicsLab/netmat
• Brain Connectivity Toolbox (BCT) - https://sites.google.com/site/bctnet/
• TVB (The Virtual Brain) - https://www.thevirtualbrain.org/
• TENSORFLOW / PYTORCH for deep learning approaches

Pitfalls and Best Practices

• Volume conduction: Zero-lag correlations can be spurious; use imaginary part of coherency or PLI
• Reference dependence: EEG references affect connectivity; consider reference-free techniques (e.g., Laplacian, REST)
• Volume conductor model accuracy: Individual head models improve source localization accuracy
• Multiple comparisons: Correct for multiple comparisons across frequencies, connections, and time points
• Stationarity assumption: Ensure data is sufficiently stationary for chosen methods; consider time-varying approaches
• Interpretability: Distinguish between statistical significance and biological relevance
• Reproducibility: Share preprocessing pipelines, parameters, and code (e.g., via OSF, GitHub)

References

• Primary: arXiv:2607.17602v1 "Exploring Brain Networks Using Noninvasive Electrophysiological Measurements: Methods and Applications"
• Supplementary: Standard EEG/MEG textbooks and toolbox documentation
• Related: Brain Connectivity Toolbox (Rubinov & Sporns, 2010), FieldTrip tutorial series

Example Workflow (MNE-Python)

import mne
import numpy as np
from mne.connectivity import spectral_connectivity

# Load raw data
raw = mne.io.read_raw_fif('raw_data.fif', preload=True)

# Preprocessing
raw.filter(1., 40., fir_design='firwin')
raw.notch_filter(np.arange(50, 201, 50))
ica = mne.preprocessing.ICA(n_components=20, random_state=97)
ica.fit(raw)
raw_clean = ica.apply(raw.copy())

# Epoching
events = mne.find_events(raw_clean)
epochs = mne.Epochs(raw_clean, events, event_id={'stim': 1}, tmin=-0.2, tmax=0.5,
                    baseline=(-0.2, 0), preload=True)

# Forward model
conductivity = (0.3, 0.006, 0.3)  # skull conductivity reduced
model = mne.make_bem_model(epochs.info, subject='sample', 
                           subjects_dir='/path/to/freesurfer',
                           conductivity=conductivity)
bem = mne.make_bem_solution(model)
src = mne.setup_source_space(epochs.info, subject='sample',
                             subjects_dir='/path/to/freesurfer',
                             spacing='oct6')
fwd = mne.make_forward_solution(epochs.info, trans='sample-trans.fif',
                                src=src, bem=bem, eeg=True, meg=False,
                                mindist=5.0)

# Inverse solution
cov = mne.compute_covariance(epochs, tmax=, method=[, ])
inv = mne.minimum_norm.make_inverse_operator(epochs.info, fwd, cov,
                                             loose=, depth=)
stc = mne.minimum_norm.apply_inverse_epochs(epochs, inv, lambda2=/, 
                                            method=, return_generator=)


label = mne.read_labels_from_annot(, parc=, 
                                   subjects_dir=)[]  
stc_label = [stc_in.label(label)  stc_in stc]
ts = np.array([sc.extract()[]  sc  stc_label])  


sfreq = epochs.info[]
fmin, fmax = , 
con, freq, times, n_epochs, n_tapers = spectral_connectivity(
    stc_label, mode=, sfreq=sfreq, fmin=fmin, fmax=fmax,
    faverage=, mt_adaptive=, n_jobs=)


con_avg = np.mean(con, axis=(, -))  


 bct
threshold = np.percentile(con_avg, )  
adj = (con_avg > threshold).astype()
binarized = binarize(adj, copy=)  
degrees = bc.degrees_und(binarized)

Customization

• Adjust frequency bands of interest (delta, theta, alpha, beta, gamma)
• Choose inverse method based on spatial resolution and SNR requirements
• Select connectivity measures based on hypothesis (undirected vs directed)
• Adapt pipeline for specific experimental designs (resting-state, task-based, sleep)

Validation

After implementing this skill, verify:

• Preprocessing steps reduce artifacts without removing neural signals
• Source localization yields physiologically plausible dipoles
• Connectivity matrices show expected patterns (e.g., higher connectivity within known networks)
• Network metrics align with prior literature for similar conditions
• Results survive surrogate testing and multiple comparison corrections

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Skill generated from arXiv:2607.17602v1 on 2026-07-21 via automated cron job.