Neuroimaging

Meet Cedalion: an open-source Python toolkit for wearable fNIRS/DOT

Kari Jaaskelainen

12 Jan 2026 — 1 min read

What if lab-grade brain imaging could go wearable—and be analyzed with the same, reproducible playbook? Meet Cedalion, an open-source Python framework for making sense of light-based brain data: fNIRS and DOT (they use harmless light to track brain activity).

Cedalion unifies the whole pipeline in one place: forward modeling and optode co-registration, signal cleaning and quality checks, GLM analysis, DOT image reconstruction, and modern ML. It speaks community standards (SNIRF, BIDS), runs in cloud-friendly Jupyter notebooks, and ships containerized workflows you can share alongside your paper.

Plug into scikit-learn and PyTorch for data-driven insights and multimodal fusion with EEG/MEG/physiology.
Use validated tools for motion correction, QC, simulation, and data augmentation.
Seven fully executable tutorial notebooks get you from raw data to results fast.

Built for reproducibility, scalability, and collaboration, so you can move from lab benches to real-world neurotech with confidence.

Read the tutorial and try the notebooks: https://arxiv.org/abs/2601.05923v1

Paper: https://arxiv.org/abs/2601.05923v1

Register: https://www.AiFeta.com

Neuroimaging fNIRS DOT OpenSource Python MachineLearning Reproducibility BIDS SNIRF EEG MEG PyTorch scikitLearn Jupyter

Meet GRACE: a moral governor for safer, more transparent AI

AI agents are getting powerful—so how do we make sure they do the right thing, not just the effective thing? Meet GRACE, a reason-based moral governor that keeps AI behavior aligned with human norms by separating moral reasoning from goal-driven decision-making. * Moral Module: uses deontic logic and explicit reasons

Why some fine-tuned LLMs miss phishing—and how to fix it

Not all fine-tuned LLMs spot phishing equally. A new study tests Llama 3.1 8B, Gemma 2 9B, and Mistral on high-stakes phishing detection—and uses SHAP and mechanistic interpretability to reveal why models do (or don’t) generalize. * Architecture × data diversity matters: Gemma 2 9B hits state-of-the-art performance (F1

AI that explains itself—by following the science

AI that explains itself—by following the science Black-box AI can be powerful, but it often can’t tell us why it made a decision. Concept Bottleneck Models (CBMs) try to fix that by predicting human-understandable concepts first, then the final answer. The catch: standard CBMs ignore domain-specific cause-and-effect and

Meet GenomAgent: A Team of AI Specialists for Smarter Genomics Q&A

TL;DR: Finding reliable answers in genomics is hard. GenomAgent turns one big AI into a coordinating team of specialists—and beats the current leader by 12% on a key benchmark. Genomic facts live across many databases. Standard chatbots struggle because they can’t flexibly query those sources. GeneGPT added

Read more

Meet GRACE: a moral governor for safer, more transparent AI

Why some fine-tuned LLMs miss phishing—and how to fix it

AI that explains itself—by following the science

Meet GenomAgent: A Team of AI Specialists for Smarter Genomics Q&A