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Appropriate Null Models for Testing the Effect of the Head Model on MEG Functional Connectivity Fingerprinting.

Matthias Schelfhout¹, Thomas Hinault², Sara Lago^3,4

¹Department of Data Analysis, Ghent University, Ghent, Belgium.

Brain Topography

|May 26, 2026

Summary

Neural fingerprinting uses brain activity patterns to identify individuals. This study reveals that head models significantly impact subject identification accuracy in electroencephalography and magnetoencephalography, challenging previous assumptions.

Keywords:

Brain connectivity Differentiability Head model MEG Fingerprinting

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Area of Science:

Neuroscience
Biophysics
Computational Biology

Background:

Functional connectivity (FC)-based neural fingerprinting aims to identify individuals using brain activity patterns.
Electroencephalography (EEG) and magnetoencephalography (MEG) rely on projecting sensor data to cortical sources via inverse models.
Previous assessments of head model influence on neural fingerprinting used incomplete null models.

Purpose of the Study:

To rigorously test the null hypothesis of no head model effect on neural fingerprinting.
To decouple the influence of recorded sensor data and head models in EEG/MEG-based identification.

Main Methods:

Designed experiments to isolate the impact of head models versus neural data on fingerprinting.
Compared identification accuracy when only head models varied across subjects (using identical source data).
Compared identification accuracy when only neural data varied across subjects (using identical head models).

Main Results:

Perfect identification (EER=0) was achieved across all frequency bands when only the head model varied.
Identifiability and differentiability were substantially reduced when only neural data varied.
These findings demonstrate a significant head model effect on neural fingerprinting.

Conclusions:

The null hypothesis of no head model effect on neural fingerprinting is not supported.
Head models play a crucial role in the accuracy of subject identification using FC-based methods.
Further research is needed to understand the relative contributions of neural and anatomical factors.