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Data-driven head model individualization from digitized electrode positions or photogrammetry improves M/EEG source

Nils Harmening1,2,3, Alexander von Lühmann1,2, Benjamin Blankertz3

  • 1BIFOLD - Berlin Institute for the Foundations of Learning and Data, Berlin, Germany.

Imaging Neuroscience (Cambridge, Mass.)
|January 12, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a new algorithm to create personalized head models without MRI/CT scans, improving brain source localization accuracy for electroencephalography (EEG) and magnetoencephalography (MEG) studies.

Keywords:
BEMEEGMEGhead modelinginverse problemphotogrammetrysource localization

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Medical Imaging

Background:

  • Accurate source localization in electroencephalography (EEG) and magnetoencephalography (MEG) relies on precise head models.
  • Standard head models (e.g., Colin27, ICBM-152) lack individual anatomical accuracy, potentially limiting source localization performance.
  • Acquiring individual structural MRI/CT scans is often impractical or unavailable for many M/EEG studies.

Purpose of the Study:

  • To develop and validate a data-driven algorithm for approximating individual head anatomies using only scalp information.
  • To enhance the accuracy of brain source localization in M/EEG by employing individualized head models.
  • To provide a practical solution for improving source localization when structural MRI/CT data is inaccessible.

Main Methods:

  • A low-dimensional representation of a head model database was utilized to derive individual head shape parameters.
  • Scalp information, obtained from photogrammetry or electrode positions (even from smartphone scans), was used to personalize head models.
  • An experimental study with 16 subjects and an EEG simulation study with 22 heads were conducted for validation.

Main Results:

  • The proposed algorithm generated more accurate head model anatomies compared to existing methods, even with smartphone-derived scalp data.
  • Individualized head models significantly improved source localization accuracy in EEG simulations compared to standard and other individualization approaches.
  • The method demonstrated superior performance in approximating individual head shapes and enhancing localization precision.

Conclusions:

  • The proposed data-driven algorithm offers a viable method for creating individualized head models without requiring structural MRI/CT scans.
  • This approach can substantially improve source localization accuracy in M/EEG studies with minimal additional effort.
  • The findings suggest a practical pathway to enhance the utility of M/EEG in various research and clinical applications.