brainmaze-eeg 1.0.0

BrainMaze: Brain Electrophysiology, Behavior and Dynamics Analysis Toolbox

BrainMaze: Brain Electrophysiology, Behavior and Dynamics Analysis Toolbox - EEG

This toolbox provides tools for processing of intracranial EEG recordings. See below and documentation for specific sections. This tool was separated from the BrainMaze toolbox to support a convenient and lightweight sharing of these tools across projects.

This project was originally developed as a part of the BEhavioral STate Analysis Toolbox (BEST) project. However, the development has transferred to the BrainMaze project.

Documentation

Documentation is available here.

Installation

pip install brainmaze-eeg

How to contribute

The project has 2 main protected branches main that contains official software releases and dev that contains the latest feature implementations shared with developers. To implement a new feature a new branch should be created from the dev branch with name pattern of developer_identifier/feature_name.

After the feature is implemented, a pull request can be created to merge the feature branch into the dev branch with. Pull requests need to be reviewed by the code owners.

Releasing

Releases are automated and never bypass branch protection. To cut a release, a code owner runs the Prepare release GitHub Action (Actions tab, workflow_dispatch) and selects the bump (patch / minor / major). This opens a small Release vX.Y.Z pull request that bumps [project].version in pyproject.toml on a release/bump-* branch off main. Once a code owner approves and merges that pull request into main, the Release workflow tags the version, builds the distributions, publishes to PyPI, and drafts the GitHub release automatically. The build reads the version from pyproject.toml, which remains the single source of truth.

Promotion of features from dev to main is independent of releases and must not change [project].version – a Version guard CI check fails any pull request outside the release flow that edits it. The version line is owned solely by the release automation on main; this is what keeps dev -> main merges free of version conflicts under the squash-merge policy (a version edited on both branches would otherwise conflict every release cycle).

The Prepare release action requires the repository/organization setting Allow GitHub Actions to create and approve pull requests to be enabled, so it can open the bump pull request.

New functions need to be implemented with Sphinx compatible docstrings. The documentation is automatically generated from the docstrings using Sphinx.

Building Documentation

To build the documentation locally:

1. Install documentation dependencies:

pip install -e ".[docs]"

Alternatively, you can install from the requirements file:

pip install -r docs_src/requirements.txt

2. Build the documentation:

sphinx-build -b html docs_src/source docs

The generated HTML documentation will be in the docs/ directory. Note that the docs/ directory is excluded from version control via .gitignore and should not be committed to the repository.

License

This software is licensed under BSD-3Clause license. For details see the LICENSE file in the root directory of this project.

Acknowledgment

This code was developed and originally published for the first time by (Mivalt 2022, and Sladky 2022). Additionally, codes related to individual projects available in this repository are stated below. When using this toolbox, we appreciate you citing the papers related to the utilized functionality. Please, see the sections below for references to individual submodules.

F. Mivalt et V. Kremen et al., “Electrical brain stimulation and continuous behavioral state tracking in ambulatory humans,” J. Neural Eng., vol. 19, no. 1, p. 016019, Feb. 2022, doi: 10.1088/1741-2552/ac4bfd.

V. Sladky et al., “Distributed brain co-processor for tracking spikes, seizures and behaviour during electrical brain stimulation,” Brain Commun., vol. 4, no. 3, May 2022, doi: 10.1093/braincomms/fcac115.

Sleep classification and feature extraction

F. Mivalt et V. Kremen et al., “Electrical brain stimulation and continuous behavioral state tracking in ambulatory humans,” J. Neural Eng., vol. 19, no. 1, p. 016019, Feb. 2022, doi: 10.1088/1741-2552/ac4bfd.

F. Mivalt et V. Sladky et al., “Automated sleep classification with chronic neural implants in freely behaving canines,” J. Neural Eng., vol. 20, no. 4, p. 046025, Aug. 2023, doi: 10.1088/1741-2552/aced21.

The work was based on the following references:

Gerla, V., Kremen, V., Macas, M., Dudysova, D., Mladek, A., Sos, P., & Lhotska, L. (2019). Iterative expert-in-the-loop classification of sleep PSG recordings using a hierarchical clustering. Journal of Neuroscience Methods, 317(February), 61?70. https://doi.org/10.1016/j.jneumeth.2019.01.013

Kremen, V., Brinkmann, B. H., Van Gompel, J. J., Stead, S. (Matt) M., St Louis, E. K., & Worrell, G. A. (2018). Automated Unsupervised Behavioral State Classification using Intracranial Electrophysiology. Journal of Neural Engineering. https://doi.org/10.1088/1741-2552/aae5ab

Kremen, V., Duque, J. J., Brinkmann, B. H., Berry, B. M., Kucewicz, M. T., Khadjevand, F., G.A. Worrell, G. A. (2017). Behavioral state classification in epileptic brain using intracranial electrophysiology. Journal of Neural Engineering, 14(2), 026001. https://doi.org/10.1088/1741-2552/aa5688

Evoked Response Potential Analysis

K. J. Miller et al., “Canonical Response Parameterization: Quantifying the structure of responses to single-pulse intracranial electrical brain stimulation,” PLOS Comput. Biol., vol. 19, no. 5, p. e1011105, May 2023, doi: 10.1371/journal.pcbi.1011105.

EEG Slow Wave Detection and Analysis

Carvalho DZ, Kremen V, Mivalt F, St Louis EK, McCarter SJ, Bukartyk J, Przybelski SA, Kamykowski MG, Spychalla AJ, Machulda MM, Boeve BF, Petersen RC, Jack CR Jr, Lowe VJ, Graff-Radford J, Worrell GA, Somers VK, Varga AW, Vemuri P. Non-rapid eye movement sleep slow-wave activity features are associated with amyloid accumulation in older adults with obstructive sleep apnoea. Brain Commun. 2024 Oct 7;6(5):fcae354. doi: 10.1093/braincomms/fcae354. PMID: 39429245; PMCID: PMC11487750.

Readme to the EEG Slow Detection project available in this repository in this repository: projects/slow_wave_detection/readme.

Funding

Individual sections of this code were developed under different projects including:

• NIH Brain Initiative UH2&3 NS095495 - Neurophysiologically-Based Brain State Tracking & Modulation in Focal Epilepsy,

• NIH U01-NS128612 - An Ecosystem of Techmology and Protocols for Adaptive Neuromodulation Research in Humans,

• DARPA - HR0011-20-2-0028 Manipulating and Optimizing Brain Rhythms for Enhancement of Sleep (Morpheus).

• FEKT-K-22-7649 realized within the project Quality Internal Grants of the Brno University of Technology (KInG BUT), Reg. No. CZ.02.2.69/0.0/0.0/19_073/0016948, which is financed from the OP RDE.