Skip to main content
Pre-release

This release is a pre-release and may not be stable for production use.

Auto EEG Analysis (AEA)

AEA provides reusable EEG analysis skills and study recipes for large language model coding agents. MNE-Python is the primary computation backend. Selected EEGLAB and FieldTrip routes are described in the bundled documentation.

This package installs the resource library and a project initialization command. It includes 22 skills, 10 study recipes, analysis helpers, templates and setup documentation. It does not install or configure a model client or an account.

Install and create a project

Use Python 3.10 or newer in an environment of your choice.

python -m pip install auto-eeg-analysis==0.3.3rc1
aea-eeg init ./my-eeg-study --agent codex
cd my-eeg-study

Use --agent claude for Claude Code or --agent both for both clients. The command copies the library into a new or empty directory and creates project-local skill links using AEA's existing installer. Existing nonempty directories are refused. It does not change global client configuration. python -m auto_eeg_analysis accepts the same arguments as aea-eeg.

The default installation has no scientific runtime dependencies. Reuse your existing MNE environment, use the included environment.yml to create one, or install the analysis dependencies from the existing environment specification.

python -m pip install 'auto-eeg-analysis[analysis]==0.3.3rc1'

Read AGENT_GUIDE.md, select procedures through RESOURCE_INDEX.md, and approve the study-specific analysis plan. Start the separately installed coding client from the initialized project directory. aea-eeg path shows the installed resource directory.

Version and evidence

This packaging candidate uses published AEA source commit cdc0c30105cc4547aed8e5ece41739a8462cb47a. The package version identifies this distribution. Retained scientific validation records and plugin versions keep their original identifiers. Packaging checks do not extend the numerical validation coverage in docs/CERTIFICATION_LEVELS.md.

The package contains no participant datasets, model credentials or archived model sessions. The Git repository remains the development source and the place for contributions and support.

License and citation

Code and skills use the repository's MIT license. Study recipes use CC BY 4.0 as stated in recipes/README.md. Preserve the recipe citations and original license notices. CITATION.cff retains the source repository's citation metadata.

Project · Setup guide · Validation coverage

Metadata

Release files for auto-eeg-analysis 0.3.3rc1

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for auto-eeg-analysis 0.3.3rc1
File Size Uploaded Provenance Actions
auto_eeg_analysis-0.3.3rc1.tar.gz 1.6 MB With provenance Details for auto_eeg_analysis-0.3.3rc1.tar.gz

Built distribution (wheel)

Table of built distributions (wheels) for auto-eeg-analysis 0.3.3rc1
File Interpreter ABI Platform Actions
auto_eeg_analysis-0.3.3rc1-py3-none-any.whl Python 3 none any Details for auto_eeg_analysis-0.3.3rc1-py3-none-any.whl

Total release size: 3.4 MB

Release files / auto_eeg_analysis-0.3.3rc1.tar.gz

Download URL auto_eeg_analysis-0.3.3rc1.tar.gz
Size 1.6 MB
Tags Source
SHA-256 checksum
How to use checksums
723376997ad4a81983571e75e78be55dd027f922a041893c62d8e23a5fde026f
BLAKE2b-256 checksum
How to use checksums
a4206a91d9bb7a458732de4fe5a53b839a458c551dbfbeaead2b569f356afe8a
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.16

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Nov 7, 2026.

Transparency log

Release files / auto_eeg_analysis-0.3.3rc1-py3-none-any.whl

Download URL auto_eeg_analysis-0.3.3rc1-py3-none-any.whl
Size 1.8 MB
Tags Python 3
SHA-256 checksum
How to use checksums
d52558c391c914bfbb08158a68059dcfa3794e28326b5cc7971025a7f91a2325
BLAKE2b-256 checksum
How to use checksums
e1bbe56efbccfd7e6f4074b9204a16918d7fef36a01685060b9c495eb4c9f46e
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.16

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Nov 7, 2026.

Transparency log

Security for auto-eeg-analysis 0.3.3rc1

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

Full provenance

Both files in this release have been uploaded with provenance metadata.

Select from 2 files

Attestation for auto_eeg_analysis-0.3.3rc1.tar.gz

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Nov 7, 2026.

Transparency log

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

To learn more about protecting yourself from malicious actors, visit our package security documentation.

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page