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.

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Examples

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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 →

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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 →

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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 →

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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 →

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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 →

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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 →

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Citation

@misc{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},
  url    = {https://kingjr.github.io/files/neuralset.pdf},
  note   = {Preprint; URL will be updated when the paper lands on arXiv}
}