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Cedalion Tutorial: A Python-based framework for comprehensive analysis of multimodal fNIRS & DOT from the lab to the
E Middell1,2, L Carlton3, S Moradi1,2
1Intelligent Biomedical Sensing (IBS) Lab, Technische Universität Berlin, 10587 Berlin, Germany.
Arxiv
|January 16, 2026
Summary
Cedalion is a new Python framework unifying functional near-infrared spectroscopy (fNIRS) and diffuse optical tomography (DOT) analysis. It enhances reproducibility and integrates machine learning for advanced neuroimaging workflows.
Area of Science:
- Neuroimaging
- Computational Neuroscience
- Biomedical Engineering
Background:
- Functional near-infrared spectroscopy (fNIRS) and diffuse optical tomography (DOT) are advancing towards wearable, multimodal, and AI-supported neuroimaging.
- Current analytical tools for fNIRS and DOT are fragmented, hindering reproducibility, interoperability, and integration with machine learning (ML) workflows.
Purpose of the Study:
- To introduce Cedalion, a Python-based open-source framework for unified analysis of multimodal fNIRS and DOT data.
- To provide a reproducible, extensible, and community-driven environment for advanced neuroimaging analysis.
- To bridge established optical neuroimaging pipelines with ML frameworks for seamless multimodal data fusion.
Main Methods:
- Cedalion integrates model-based and data-driven analyses, including forward modeling, optode co-registration, signal processing, GLM analysis, DOT image reconstruction, and ML methods.
- The framework adheres to SNIRF and BIDS standards, supports cloud-executable Jupyter notebooks, and utilizes containerized workflows for scalable, reproducible analysis.
- It incorporates validated algorithms for signal quality assessment, motion correction, GLM modeling, DOT reconstruction, and includes modules for simulation, data augmentation, and multimodal physiology analysis.
Main Results:
- Cedalion provides a standardized architecture within the Python ecosystem, unifying diverse analytical methods.
- The framework facilitates seamless integration with ML libraries (scikit-learn, PyTorch) and other neuroimaging modalities (EEG, MEG, physiological data).
- A tutorial paper with seven executable notebooks demonstrates Cedalion's core features for laboratory-based and real-world neuroimaging.
Conclusions:
- Cedalion offers an open, transparent, and community-extensible foundation for fNIRS and DOT data analysis.
- It supports reproducible, scalable, cloud- and ML-ready neuroimaging workflows.
- The framework empowers researchers by connecting established optical neuroimaging techniques with modern computational approaches.
