Automated EEG and sEEG Electrode Localization and Labeling in Brainstorm
Presented During:
Saturday, June 28, 2025: 11:30 AM - 12:45 PM
Brisbane Convention & Exhibition Centre
Room:
M4 (Mezzanine Level)
Poster No:
1345
Submission Type:
Abstract Submission
Authors:
Takfarinas Medani1, Chinmay Chinara1, Anand Joshi1, Yash Vakilna2, Raymundo Cassani3, Samuel Villalon4, Wayne Mead2, François Tadel5, Johnson Hampson2, Kenneth Taylor6, Christian Bénar4, Dileep Nair6, Sylvain Baillet3, John Mosher2, Richard Leahy1
Institutions:
1University of Southern California, Los Angeles, CA, 2University of Texas Health Science Center at Houston, Houston, TX, 3Montreal Neurological Institute, Montreal, Quebec, 4Institut de Neurosciences des Systèmes, Marseille, France, 5Independent Research Engineer, Grenoble, France, 6Cleveland Clnic, Cleveland, OH
First Author:
Co-Author(s):
Introduction:
Accurate electrode localization is essential for neuroimaging applications, including scalp EEG and stereo-EEG (sEEG). For EEG, precise knowledge of electrode positions facilitates cortical current density mapping and source localization[1]. In sEEG, accurate electrode contact localization enables seizure onset zone (SOZ) identification, which is critical for epilepsy surgery. Traditional methods such as manual digitization or landmark-based techniques are labor-intensive, prone to human error, and require expert involvement. This study introduces automated EEG and sEEG electrode localization workflows within Brainstorm[2], an open-source neuroimaging platform, enhancing accuracy, efficiency, and accessibility for both research and clinical applications.
Methods:
For EEG electrode localization, we employed a structured-light-based 3D scanning approach using the Revopoint 3D scanner[3]. The process began with acquiring a 3D mesh of the participant's scalp and EEG cap (Fig1), which was minimally processed to ensure accuracy[4]. The electrode positions were identified through segmentation and texture mapping of the scalp and cap images. To align the electrode positions with the EEG cap's manufacturer diagram, the 3D mesh was flattened to a 2D template using a spherical-to-Cartesian transform. The flattened 2D map was then automatically aligned to the EEG cap template using the Iterative Closest Point (ICP) algorithm with a bending energy regularizer to ensure smooth deformation.
For sEEG contact localization and labeling, the Brainstorm pipeline automated critical processing steps ((Fig2), including post-CT to pre-MRI rigid co-registration using a correlation ratio cost function. Post-CT scans were skull-stripped to remove extracranial regions and wire clusters, ensuring clean identification of sEEG contacts. Candidate electrode contacts were automatically detected by the GARDEL toolbox[5], and integrated into Brainstorm as a plugin. To provide anatomical context, Brainstorm labeled the contacts using built-in brain atlases. Additionally, manual editing tools were included for refinement, allowing users to fine-tune electrode contact positions when needed.
Both EEG and sEEG workflows were seamlessly integrated into Brainstorm's graphical interface, enabling interactive visualization, editing, and streamlined processing for advanced neuroimaging studies.
For sEEG contact localization and labeling, the Brainstorm pipeline automated critical processing steps ((Fig2), including post-CT to pre-MRI rigid co-registration using a correlation ratio cost function. Post-CT scans were skull-stripped to remove extracranial regions and wire clusters, ensuring clean identification of sEEG contacts. Candidate electrode contacts were automatically detected by the GARDEL toolbox[5], and integrated into Brainstorm as a plugin. To provide anatomical context, Brainstorm labeled the contacts using built-in brain atlases. Additionally, manual editing tools were included for refinement, allowing users to fine-tune electrode contact positions when needed.
Both EEG and sEEG workflows were seamlessly integrated into Brainstorm's graphical interface, enabling interactive visualization, editing, and streamlined processing for advanced neuroimaging studies.
·Figure-1: (a) 3D cap mesh captured by Revopoint scanner (b) 2D flattened mesh (c) sketch of the cap configuration (d) overlay of cap configuration flattened mesh before and (e) after matching (f) auto
·Figure-2: The Brainstorm GUI for automatic SEEG electrode contact localization and labelling
Results:
The EEG workflow successfully localized and labeled electrode positions in 3D space with minimal user intervention, achieving high accuracy when compared to traditional electromagnetic digitizers. For sEEG, the automated pipeline identified electrode contact positions and provided anatomical labels with reduced intra- and inter-rater variability. Both workflows resulted in significant time savings and improved reproducibility across multiple datasets. Brainstorm's intuitive interface facilitates the seamless use of these tools for advanced neuroimaging applications.
Conclusions:
This study presents unified and automated EEG and sEEG workflows for electrode localization and labeling, integrated into the Brainstorm software. The EEG method leverages a low-cost, structured-light 3D scanner, while the sEEG pipeline automates critical steps such as image registration and contact detection. These tools provide a scalable, efficient, and user-friendly solution for neuroimaging researchers and clinicians, enhancing the accuracy and consistency of cortical mapping and seizure localization analyses.
Modeling and Analysis Methods:
EEG/MEG Modeling and Analysis 1
Methods Development
Neuroinformatics and Data Sharing:
Workflows 2
Keywords:
Computational Neuroscience
Data Registration
Electroencephaolography (EEG)
Epilepsy
Experimental Design
MRI
Open Data
Open-Source Code
Open-Source Software
Source Localization
1|2Indicates the priority used for review
Abstract Information
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Provide references using APA citation style.
[2] Tadel, F., et al. "Brainstorm: a user-friendly application for MEG/EEG analysis." Comput Intell Neurosci. 2011 Apr 13; DOI: 10.1155/2011/879716.
[3] Revopoint: https://www.revopoint3d.com/
[4] Homölle, S., et al. “Using a structured-light 3D scanner to improve EEG source modeling with more accurate electrode positions.” J Neurosci Methods. 2019 Oct 1; DOI: 10.1016/j.jneumeth.2019.108378
[5] Villalon, S M., et al. “EpiTools, A software suite for presurgical brain mapping in epilepsy: Intracerebral EEG.” J Neurosci Methods. 2018 Mar 29; DOI: 10.1016/j.jneumeth.2018.03.018