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MEGaNorm

imported

software/meganorm

MEGaNorm is a Python package for normative modeling on MEG and EEG data.

Machine-generated from the listed sources and not yet reviewed by a human.

MEGaNorm project image
GitHub preview card for ML4PNP/MEGaNorm. Served by its origin, not stored here, and not covered by this registry’s licence.
record
Category
Software & Systems
Subcategory
unknown
License
GPL-3.0(osi)
Status
active
Maturity
deployed
Organization
ML4PNP
Country
unknown
Homepage
unknown
Documentation
unknown
Tags
eeg · electroencephalography · magnetoencephalography · meg · normative-modelling · precision-medicine
Regulatory
unknown
similar by tags

Computed from shared tags, weighted so a rare tag counts for more than a common one. These are suggestions, not curated relationships.

  • braindecodeeeg · electroencephalography · magnetoencephalography · meg

    Deep learning software to decode EEG, ECG or MEG signals

  • eyeartifactcorrectioneeg · electroencephalography · magnetoencephalography · meg

    Eye movement and blink-related EEG and MEG artifact correction algorithms

  • mne-arieeg · electroencephalography · magnetoencephalography · meg

    All-resolutions Inference for M/EEG in Python

  • mne-pythoneeg · electroencephalography · magnetoencephalography · meg

    MNE: Magnetoencephalography (MEG) and Electroencephalography (EEG) in Python

  • niseqeeg · electroencephalography · magnetoencephalography · meg

    group sequential tests for neuroimaging

  • mne-bidseeg · electroencephalography · magnetoencephalography · meg

    MNE-BIDS is a Python package that allows you to read and write BIDS-compatible datasets with the help of MNE-Python.

sources
  1. api.github.com/repos/ML4PNP/MEGaNorm
    retrieved 2026-08-25 · via github-api

    Machine-imported from GitHub search. Last push 2026-08-25, 9 stars, license reported as GPL-3.0. Category and schematic were assigned by keyword heuristics and are unreviewed.

Not yet verified by a human. Correct this record →

machine-readable

/v1/entries/9.json→ .entries["meganorm"]

Entries are sharded 64 ways by a stable hash of the id, so a consumer can find any record without an index.