Omni-iEEG Planner: an open-source Python platform integrating multimodal neuroimaging and intracranial EEG data for epilepsy surgery research
Abstract
Multimodal intracranial EEG (iEEG) research for epilepsy surgery combines anatomical and functional imaging, implanted electrode locations, interictal biomarkers, and functional mapping--outputs produced by different applications and reconciled by hand before they can be viewed on the same anatomy. Omni-iEEG Planner is an open-source Python platform that brings these inputs into one patient coordinate frame for research data analysis. The workflow starts from the preoperative T1-weighted MRI, from which FreeSurfer provides the cortical surface and anatomical labels; CT, PET and diffusion MRI are then co-registered to that reference, electrode contacts are detected automatically on the postimplantation CT and reviewed by the analyst, and tractography is reconstructed within the application. Channel-indexed results computed outside the platform, such as high-frequency oscillation counts from pyHFO or time-resolved gamma responses, are projected onto the contacts and the cortical surface with their source units preserved, and interictal and functional evidence remain separate layers. We describe the design and demonstrate representative workflows on de-identified patient data. The platform is not proposed as a method for every laboratory; it is a worked example of how one research group combined multimodal integration with an AI-assisted iEEG analysis tool, pyHFO, in a single open environment that others can adapt to their own pipelines. The design will accommodate further evidence types--model-derived contact scores, virtual implantation and hypothetical resection studies, group-level analyses--as they are implemented. The software is released under the MIT license for research and educational use, not clinical decision-making.
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BibTeX
@misc{tanoue2026omniieeg,
title = {{Omni-iEEG Planner: an open-source Python platform integrating multimodal neuroimaging and intracranial EEG data for epilepsy surgery research}},
author = {Yuta Tanoue and Masaki Izumi and Yipeng Zhang and Chenda Duan and Tonmoy Monsoor and Christopher A. Chan and Joe X. Qiao and Noriko Salamon and Aria Fallah and Vwani Roychowdhury and Hiroki Nariai},
howpublished = {arXiv},
year = {2026},
month = nov,
doi = {10.48550/arxiv.2611.00018},
eprint = {2611.00018},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2611.00018},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - Omni-iEEG Planner: an open-source Python platform integrating multimodal neuroimaging and intracranial EEG data for epilepsy surgery research
AU - Yuta Tanoue
AU - Masaki Izumi
AU - Yipeng Zhang
AU - Chenda Duan
AU - Tonmoy Monsoor
AU - Christopher A. Chan
AU - Joe X. Qiao
AU - Noriko Salamon
AU - Aria Fallah
AU - Vwani Roychowdhury
AU - Hiroki Nariai
PY - 2026
DA - 2026/11/01
PB - arXiv
DO - 10.48550/arxiv.2611.00018
UR - https://arxiv.org/abs/2611.00018
AB - Multimodal intracranial EEG (iEEG) research for epilepsy surgery combines anatomical and functional imaging, implanted electrode locations, interictal biomarkers, and functional mapping--outputs produced by different applications and reconciled by hand before they can be viewed on the same anatomy. Omni-iEEG Planner is an open-source Python platform that brings these inputs into one patient coordinate frame for research data analysis. The workflow starts from the preoperative T1-weighted MRI, from which FreeSurfer provides the cortical surface and anatomical labels; CT, PET and diffusion MRI are then co-registered to that reference, electrode contacts are detected automatically on the postimplantation CT and reviewed by the analyst, and tractography is reconstructed within the application. Channel-indexed results computed outside the platform, such as high-frequency oscillation counts from pyHFO or time-resolved gamma responses, are projected onto the contacts and the cortical surface with their source units preserved, and interictal and functional evidence remain separate layers. We describe the design and demonstrate representative workflows on de-identified patient data. The platform is not proposed as a method for every laboratory; it is a worked example of how one research group combined multimodal integration with an AI-assisted iEEG analysis tool, pyHFO, in a single open environment that others can adapt to their own pipelines. The design will accommodate further evidence types--model-derived contact scores, virtual implantation and hypothetical resection studies, group-level analyses--as they are implemented. The software is released under the MIT license for research and educational use, not clinical decision-making.
ER -