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esinet

imported

software/esinet

EEG inverse solution with artificial neural networks. This package works with MNE-Python data structures for easy integration into your MNE-based M/EEG code

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

esinet project image
GitHub preview card for LukeTheHecker/esinet. Served by its origin, not stored here, and not covered by this registry’s licence.
record
Category
Software & Systems
Subcategory
unknown
License
MIT(osi)
Status
dormant
Maturity
deployed
Organization
unknown
Country
unknown
Homepage
unknown
Repository
github.com/LukeTheHecker/esinet
Documentation
unknown
Tags
anns · convdip · eeg · esinet · inverse · inverse-solutions · meg · mne-python
Regulatory
unknown
built by · 1

Top contributors by commit count, from the project’s public repository. Avatars are served by their origin, not stored here. To be removed from this list, open an issue.

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.

  • mne-rsaeeg · meg · mne-python

    Representational Similarity Analysis on MEG and EEG data

  • niseqeeg · meg · mne-python

    group sequential tests for neuroimaging

  • preprocessing_pipelineseeg · meg · mne-python

    Preprocessing Pipelines for EEG (MNE-python), fMRI (nipype), MEG (MNE-python/autoreject) data

  • Cartooleeg · inverse-solutions

    EEG & MRI processing, Micro-States analysis & Sources localization

  • intro-to-eegeeg · mne-python

    Introduction to EEG analysis course using MNE-Python

  • neural-signals-101eeg · mne-python

    Beginner tutorial: ML & signal processing on neural signals (EEG) in Python — raw recordings to honest evaluation, in runnable notebooks. MIT.

sources
  1. api.github.com/repos/LukeTheHecker/esinet
    retrieved 2026-08-05 · via github-api

    Machine-imported from GitHub search. Last push 2024-04-25, 53 stars, license reported as MIT. Category and schematic were assigned by keyword heuristics and are unreviewed.

Not yet verified by a human. Correct this record →

machine-readable

/v1/entries/11.json→ .entries["esinet"]

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