sEEG-Suite: An Interactive Pipeline for Semi-Automated Contact Localization and Anatomical Labeling with Brainstorm
Chinmay ChinaraiD, Raymundo CassaniiD, Takfarinas MedaniiD, Anand A. JoshiiD, Samuel Médina VillaloniD, Yash Shashank VakilnaiD, Johnson P. HampsoniD, Kenneth TayloriD, François TadeliD, Dileep R NairiD, Christian BénariD, Sylvain BailletiD, John C. MosheriD, Richard M. LeahyiD
Abstract
Stereoelectroencephalography (sEEG) is a critical tool for mapping epileptic networks in patients with drug-resistant epilepsy. Accurate localization and labeling of sEEG contacts are essential for identifying the seizure onset zone (SOZ) and ensuring optimal resective surgery. Traditional methods for localizing and labeling sEEG contacts rely on manual processing, which is prone to human error and variability. To address these challenges, we developed and integrated a semi-automatic sEEG contact localization and labeling pipeline within Brainstorm1, an open-source software platform for multimodal brain imaging analysis2-8, widely adopted in the neuroscience community with over 50,000 registered users and an active online user forum. The software has been supported by the National Institute of Health (NIH) for over two decades. The pipeline presented in this paper performs three key steps: (1) import and apply rigid co-registration of post-implantation Computed Tomography (CT) or post-CT with pre-implantation Magnetic Resonance Imaging (pre-MRI), (2) post-CT image segmentation and semi-automatic detection of sEEG contacts using GARDEL9, which has been integrated as a Brainstorm plugin, and (3) automatic anatomical labeling of contacts using standard and commonly used brain anatomy templates and atlases. Integrating this pipeline into Brainstorm brings the best of both worlds: GARDELs automation and Brainstorms multimodal data compatibility, and rich library of visualization and advanced analysis tools at both sensor and source level6. This sEEG-Suite tool facilitates reproducible research, supports clinical workflows, and accelerates sEEG-based investigations of invasive brain recordings.
02 /Specification & Classification
Click any tag to browse matching papers- Citations
- 2
- FWCI
- 8.36
- Field percentile
- top 5%
- Organism
- Assay
- Research Focus
- Approach
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Fully Automated and Noise-Robust SEEG Electrode Localization under Practical Clinical Conditions
C. L. Philip Chen, Long Zhang, Pei Feng Tong·bioRxiv·Feb 13, 2026
- Building an Ecosystem of Seizure Localization Methods: Neural Fragility as the First Step
Jiefei Wang, Anne‐Cécile Lesage, … Patrick J. Karas·eNeuroPeer-reviewed·Feb 27, 2026
- Functional Mapping of Movement and Speech Using Task-Based Electrophysiological Changes in Stereoelectroencephalography
Michael A. Jensen, Anthony L. Fine, … Kai Joshua Miller·Journal of neurosurgeryPeer-reviewed·Aug 30, 2024
- Source-sink connectivity: A novel interictal EEG marker for seizure localization
K. M. Gunnarsdottir, A. Li, … S. V. Sarma·bioRxiv·Oct 15, 2021
- Evaluating Invasive EEG Implantations in Medically Refractory Epilepsy with Functional Scalp EEG Recordings and Structural Imaging Data
Anil K. Palepu, Adam Li, … Sridevi V. Sarma·bioRxiv·Feb 19, 2019
- Multi-channel deep learning with intracranial neurostimulation can localize seizure onset zones in humans
Graham Walter Johnson, Leon Y. Cai, … Dario J. Englot·bioRxiv·Feb 28, 2022