NeuralSet

NeuralSet turns raw neural recordings and stimuli into PyTorch-ready datasets.

Quick install

pip install neuralset

Heavier dependencies (e.g. transformers for text/image/etc feature extraction) can be pre-installed with:

pip install 'neuralset[all]'

see Installation for the full breakdown.

---

Examples

---

Tutorials

Each tutorial walks through one building block of the NeuralSet pipeline.

Events

The universal data format — every recording, stimulus, and annotation is an event.

Snippet Tutorial

from neuralset.events import Event
evt = Event(type="Word", start=1.0,
            duration=0.3, timeline="sub-01")
print(evt)              # pydantic model
print(evt.model_dump()) # dict

Open full tutorial →

↓

Studies

Download datasets and load them as events DataFrames.

Snippet Tutorial

study = ns.Study(name="Fake2025Meg",
                 path=ns.CACHE_FOLDER)
events = study.run()
print(f"{len(events)} events, "
      f"{events['subject'].nunique()} subjects")

Open full tutorial →

↓

Transforms

Filter, split, and enrich events before extraction.

Snippet Tutorial

import neuralset as ns
transform = ns.events.transforms.AddSentenceToWords()
events = transform(events)
print(events[events.type == "Sentence"].head())

Open full tutorial →

↓

Extractors

Turn events into tensors — brain signals, text embeddings, images, labels.

Snippet Tutorial

meg = ns.extractors.MegExtractor(frequency=100.0)
sample = meg(events, start=0.0, duration=1.0)
print(f"MEG shape: {sample.shape}")

Open full tutorial →

↓

Segmenter & Dataset

Create time-locked segments and iterate with a PyTorch DataLoader.

Snippet Tutorial

segmenter = ns.dataloader.Segmenter(
    start=-0.1, duration=0.5,
    trigger_query='type=="Word"',
    extractors=dict(meg=meg),
    drop_incomplete=True)
dataset = segmenter.apply(events)
loader = DataLoader(dataset, batch_size=8,
                    collate_fn=dataset.collate_fn)

Open full tutorial →

↓

Chains

Compose Study + Transforms into reproducible, cacheable pipelines.

Snippet Tutorial

chain = ns.Chain(steps=[
    ns.Study(name="Fake2025Meg",
             path=ns.CACHE_FOLDER),
    transforms.AddSentenceToWords(),
])
events = chain.run()

Open full tutorial →

Installation → Start Tutorials →

---

Citation

If you use NeuralSet in your research, please cite the NeuralSet paper:

@article{king2026neuralset,
  title   = {NeuralSet: A High-Performing Python Package for Neuro-AI},
  author  = {King, Jean-R{\'e}mi and Bel, Corentin and Evanson, Linnea
             and Gadonneix, Julien and Houhamdi, Sophia and L{\'e}vy, Jarod
             and Raugel, Josephine and Santos Revilla, Andrea
             and Zhang, Mingfang and Bonnaire, Julie and Caucheteux, Charlotte
             and D{\'e}fossez, Alexandre and Desbordes, Th{\'e}o
             and Diego-Sim{\'o}n, Pablo and Khanna, Shubh and Millet, Juliette
             and Orhan, Pierre and Panchavati, Saarang and Ratouchniak, Antoine
             and Thual, Alexis and Brooks, Teon L. and Begany, Katelyn
             and Benchetrit, Yohann and Careil, Marl{\`e}ne and Banville, Hubert
             and d'Ascoli, St{\'e}phane and Dahan, Simon and Rapin, J{\'e}r{\'e}my},
  year    = {2026},
  journal = {arXiv preprint arXiv:2605.03169},
  url     = {https://arxiv.org/abs/2605.03169},
}