EEG Connectivity Analysis
Functional Connectivity, Synchronisation and Network Organisation
EEG connectivity analysis estimates how activity at different sensors or sources relates over time, while network analysis summarises the resulting connectivity as a graph. Together they describe functional connectivity, synchronisation and network organisation, which are widely used in research.
Neuromap™ provides connectivity and network analysis using measures selected for the research design, with attention to the methodological pitfalls that make connectivity easy to over-interpret.
Connectivity Measures
- Coherence and imaginary coherence
- Phase-based measures (e.g. phase-locking, phase-lag index)
- Leakage-resistant measures to reduce volume-conduction artefacts
- Amplitude-based coupling where appropriate
Network and Graph Metrics
- Node-level measures (degree, centrality)
- Integration and segregation measures
- Efficiency and modularity
- Edge-level comparisons where montage and data support valid inference
Avoiding Common Pitfalls
Sensor-level connectivity is vulnerable to volume conduction and common-reference effects, which can create spurious connections. Neuromap™ prefers leakage-resistant measures where appropriate, documents montage and reference choices, and is explicit about where inference is and is not supported by the data.
Data Requirements
Valid connectivity and network estimates require adequate channel coverage, sufficient artefact-free data and appropriate preprocessing. Suitability is confirmed during quality control before network metrics are produced.
Sensor-Space Versus Source-Space Connectivity
EEG connectivity analysis can be performed at the sensor level or, with appropriate methods, in source space. Sensor-level connectivity is more exposed to volume conduction and reference effects, which can create spurious links. Neuromap™ selects connectivity measures with these limitations in mind and documents the montage and reference choices behind every connectivity analysis.
- Sensor-level measures with leakage-resistant options
- Careful reference and montage documentation
- Preference for measures robust to volume conduction
- Clear statement of where inference is supported
Turning Connectivity Into Network Metrics
Once connectivity is estimated, graph and network analysis summarises it as integration, segregation, efficiency and centrality measures. Valid network inference from EEG connectivity analysis requires adequate montage and data quality, which Neuromap™ confirms before producing network metrics.
Avoiding Over-Interpretation
Connectivity results are easy to over-interpret. Neuromap™ reports EEG connectivity analysis with explicit limitations, distinguishing statistical association from mechanistic claims, and treats connectivity measures as research variables rather than diagnostic markers.
Applications of EEG Connectivity Analysis
EEG connectivity analysis is used in research across cognition, development, movement disorders and psychiatry, always as a research measure. Because connectivity and network measures describe how activity is coordinated across the brain, they offer a complementary view to spectral measures. Neuromap™ provides EEG connectivity analysis for these research applications with careful attention to methodological validity.
- Cognitive research on network coordination
- Developmental research on maturing networks
- Movement-disorder research on motor networks
- Psychiatry research on network organisation
Reporting EEG Connectivity Analysis for Publication
Publishing EEG connectivity analysis requires transparent reporting of the connectivity measure, montage, reference, preprocessing and network metrics, along with honest limitations around volume conduction. Neuromap™ documents these so that connectivity analysis work can be reported credibly and survive peer review.
What You Receive
Connectivity engagements deliver documented measures and network metrics with explicit statements of where inference is supported.
- Structured, machine-readable data exports keyed by participant and visit
- Clear tables and publication-oriented figures where requested
- A documented record of settings, definitions and any exclusions
- Study-level summaries at agreed milestones
- Optional specialist scientific review at the contracted service level
Every deliverable is prepared in professional British English and is designed to slot directly into the study team’s statistical, regulatory or manuscript workflow.
Getting Started
Measure selection is matched to the research question and data.
The usual first step is a short feasibility review. The study team shares the research outline, the expected data volume, the acquisition details and the deliverables required. Compatibility is then confirmed and a study-specific scope and quotation provided before any commitment.
Guarding Against Spurious Connections
The central methodological risk in this area is mistaking an artefact of measurement for a genuine relationship between brain regions. Activity spreading across the scalp, and the choice of reference, can both create apparent connections that do not reflect true coordination. Choosing measures designed to resist these effects, and documenting the montage and reference behind every result, reduces the chance of building conclusions on spurious links.
Network summaries inherit whatever strengths and weaknesses the underlying estimates carry. Graph measures such as efficiency and centrality are only as trustworthy as the connectivity they are computed from, so adequate channel coverage and data quality are prerequisites, confirmed before metrics are produced. Reporting results with explicit limitations, and distinguishing statistical association from mechanistic claims, keeps the interpretation defensible and appropriately modest about what the network view demonstrates.
Key Terms Explained
The following terms appear across this area of research and may help teams new to advanced electrophysiology.
Coherence
A measure of the consistency of the relationship between two signals by frequency.
Phase-lag index
A measure designed to reduce the influence of volume conduction.
Volume conduction
Spread of activity that can create spurious apparent connections.
Graph metric
A network measure such as efficiency, modularity or centrality.
Source space
An estimated representation of activity at brain sources rather than sensors.
Node
A point in a network, such as a sensor or estimated source.
A Note on Responsible Use
All outputs are provided for research use. They consist of data and neutral observations, and a suitably qualified clinician or investigator retains responsibility for interpretation, clinical meaning and any decisions that follow.
Defensible Network Findings
Connectivity and network measures are powerful precisely because they describe how activity is coordinated across the brain, but that power is easily undermined by artefacts of measurement. Choosing measures that resist volume conduction, documenting montage and reference, confirming adequate data quality before computing network metrics, and reporting explicit limitations together keep the findings defensible. Distinguishing statistical association from mechanistic explanation is equally important. Treated with this care, connectivity offers a genuinely complementary view to spectral analysis while remaining honest about the boundaries of what the network perspective can demonstrate. Where the data support only cautious inference, saying so plainly is far more valuable than presenting a confident network map that the recording cannot truly justify.
Summary
Connectivity analysis work estimates functional connectivity and network organisation, but is vulnerable to volume conduction and reference artefacts. Neuromap™ uses appropriate measures, documents choices and reports connectivity findings with caution.
Scope
Neuromap™ provides EEG and qEEG data analytics for research. Outputs are data and neutral observations only; a suitably qualified clinician or investigator completes all interpretation. Neuromap™ does not provide a clinical diagnosis and is not an accredited clinical diagnostic laboratory.
Frequently Asked Questions
Common questions about EEG connectivity and network analysis.
What is EEG functional connectivity?
A set of measures estimating how activity at different sensors or sources is statistically related over time.
Why use leakage-resistant measures?
To reduce spurious connectivity from volume conduction and shared reference, which can otherwise inflate results.
Is EEG connectivity diagnostic?
No. It provides research measures; interpretation remains with qualified professionals.
Which connectivity measure should we use?
This depends on the question and data; leakage-resistant measures are preferred where volume conduction is a concern, and the choice is documented.
Can you produce network graphs for figures?
Yes. Network metrics and figures can be prepared where montage and data quality support valid inference.
Limitations and Important Notes
- Sensor-level connectivity can be confounded by volume conduction and reference choice.
- Network inference requires adequate montage and data quality.
- Any published figure for analytical-output count, reference datasets, processing capacity or turnaround is conditional on the documented platform version and the requirements of the specific study.