neuralset 0.1.0

A Python toolkit for building neuroimaging data pipelines.

neuralset

A Python toolkit for building neuroimaging data pipelines — from raw data to AI-ready batched tensors.

• Event-driven processing with typed, validated DataFrames
• Multi-modal extractors for MEG, EEG, fMRI, EMG, iEEG, text, image, audio, video
• Caching and remote compute via exca

Install

pip install neuralset

Quick example

import neuralset as ns

# Load a study and build events
study = ns.Study(name="Mne2013Sample", path="./data")

study.download() # ~ 1.6GB

events = study.run()

# Configure extractors
meg = ns.extractors.MegExtractor(frequency=100.0, filter=(0.5, 30))

# Segment around triggers and build a dataset
segmenter = ns.Segmenter(
    extractors=dict(meg=meg),
    trigger_query='type == "Word"',
    start=-0.1, duration=0.5,
)
dataset = segmenter.apply(events)
batch = dataset.load_all()

Citation

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License

This project is licensed under the MIT License. See LICENSE for details.