Spatial localization of EEG electrodes using 3D scanning

Gaia Amaranta Taberna, Marco Marino, Marco Ganzetti, Dante Mantini

Research output: Contribution to journalArticlepeer-review


Objective. A reliable reconstruction of neural activity using high-density electroencephalography (EEG) requires an accurate spatial localization of EEG electrodes aligned to the structural magnetic resonance (MR) image of an individual's head. Current technologies for electrode positioning, such as electromagnetic digitization, are yet characterized by non-negligible localization and co-registration errors. In this study, we propose an automated method for spatial localization of EEG electrodes using 3D scanning, a non-invasive and easy-to-use technology with potential applications in clinical settings. Approach. Our method consists of three main steps: (1) the 3D scan is ambient light-corrected and spatially aligned to the head surface extracted from the anatomical MR image; (2) electrode positions are identified by segmenting the 3D scan based on predefined colour and topological properties; (3) electrode labelling is performed by aligning an EEG montage template to the electrode positions. The performance of the method was assessed on data collected in eight participants wearing high-density EEG caps with 128 sensors, from three different manufacturers. We estimated the co-registration error using the distance between the MR-based head shape and the closest 3D scan points. Also, we quantified the positioning error using the distance between the detected electrode positions and the corresponding locations manually selected on the 3D scan data. Main results. For all participants and EEG caps, we obtained a median error of co-registration below 3.0 mm and of spatial localization below 1.4 mm. The method based on 3D scanning data was significantly more precise compared to the electromagnetic digitization technique, and the total time required for obtaining electrode positions was reduced by about half. Significance. We have introduced a method to automatically detect EEG electrodes based on 3D scanning information. We believe that our work can contribute to a more effective, reliable and widespread use of high-density EEG as brain imaging tool.

Original languageEnglish
Article number026020
JournalJournal of Neural Engineering
Issue number2
Publication statusPublished - Jan 1 2019


  • 3D scan
  • brain
  • electrode positions
  • electroencephalography
  • head model
  • image processing
  • point cloud

ASJC Scopus subject areas

  • Biomedical Engineering
  • Cellular and Molecular Neuroscience


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