Cedalion tutorial: a Python-based framework for comprehensive analysis of multimodal fNIRS and DOT from the lab to the everyday world.

Overview

1. Technische Universität Berlin, Intelligent Biomedical Sensing (IBS) Lab, Berlin, Germany
2. BIFOLD – Berlin Institute for the Foundations of Learning and Data, Berlin, Germany
3. Boston University, Neurophotonics Center, Biomedical Engineering, Boston, Massachusetts, United States

Abstract

Functional near-infrared spectroscopy (fNIRS) and diffuse optical 1 tomography (DOT) are rapidly evolving toward wearable, multimodal, data-driven, and artificial-intelligence-supported neuroimaging in the everyday world. However, current analytical tools are fragmented across platforms, limiting reproducibility, interoperability, and integration with modern machine learning (ML) workflows. Cedalion is a Python-based open-source framework designed to unify advanced model-based and data-driven analysis of multimodal fNIRS and DOT data within a reproducible, extensible, and community-driven environment. Cedalion integrates forward modeling, photogrammetric optode coregistration, signal processing, general linear model (GLM) analysis, DOT image reconstruction, and ML-based data-driven methods within a single standardized architecture based on the Python ecosystem. It adheres to SNIRF and BIDS standards, supports cloud-executable Jupyter notebooks, and provides containerized workflows for scalable, fully reproducible analysis pipelines that can be provided alongside original research publications. Cedalion connects established optical-neuroimaging pipelines with ML frameworks such as scikit-learn and PyTorch, enabling seamless multimodal fusion with electroencephalography (EEG), magnetoencephalography (MEG), and physiological data. It implements validated algorithms for signal quality assessment, motion correction, GLM modeling, and DOT reconstruction, complemented by modules for simulation, data augmentation, and multimodal physiology analysis. Automated documentation links each method to its source publication, and continuous-integration testing ensures robustness. This tutorial paper provides seven fully executable notebooks that demonstrate core features. Cedalion offers an open, transparent, and community-extensible foundation that supports reproducible, scalable, and cloud- and ML-ready fNIRS/ DOT workflows for laboratory-based and real-world neuroimaging.

Reproduced under the paper's license (CC BY), from the paper cited above.

Code

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Data

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Code and Data Availability

Version 26.5 of the Cedalion toolbox presented in this article, alongside documentation, example data, and Jupyter notebooks, is publicly available on www.cedalion.tools. The seven tutorials for this paper are available in rendered form in the Supplemental Material (https://doi.org/10.1117/1.NPh.13.S3.S32602.s01) and provided in a versioned executable container on https://codeocean.com/capsule/5432660/tree.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 13 authors, 7 keywords, 2 funders, 81 references.

Cite

This paper

Middell, E., Carlton, L. B., Moradi, S., Codina, T., Fischer, T., Cutler, J., Kelley, S. M., Behrendt, J., Dissanayake, T., Harmening, N., Yücel, M. A., Boas, D. A., & von Lühmann, A. (2026). Cedalion tutorial: a Python-based framework for comprehensive analysis of multimodal fNIRS and DOT from the lab to the everyday world. Neurophotonics, 13(Suppl 3), S32602. https://doi.org/10.1117/1.nph.13.s3.s32602

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