Digital Signal Processing for Big EEG Datasets
Project description
Digital Signal Processing for Big EEGs
Key Features | Installation | Dependencies | Documentation | Attribution | Contributions | Issues | License | Acknowledgements
- Source Code: https://github.com/mscaudill/opensieze
- White Paper: LINK
Key Features
Recent innovations in EEG recording technologies make it possible to perform high channel count recordings at high sampling frequencies spanning many days. This results in big EEG data sets that are often not addressable to virtual memory. Worse yet, current digital signal processing (DSP) packages that rely on Matlab© or Scipy's DSP routines require the data to be a contiguous in-memory array. Openseize is a fully iterative DSP Python package that can scale to the largest of EEG data sets. It accomplishes this by storing DSP operations, such as filtering, as on-the-fly iterables that "produce" DSP results one fragment of the data at a time. Additionally, Openseize is built using time-tested software design principles that support extensions while maintaining a simple interface. Finally, Openseize's documentation features in-depth discussions of iterative DSP processing and its implementation.
- Construct sequences of DSP steps that operate completely 'out-of-core' to process data too large to fit into memory.
- DSP pipelines are constructed using a familiar Scipy-like API, so you can start quickly without sweating the details.
- Supports processing of data from multiple file types including the popular European Data Format (EDF).
- Supports 'masking' to filter data sections by artifacts, behavioral states or any externally measured signals or annotations.
- Effeciently process large data using the amount of memory you choose to use.
- DSP tools currently include a large number of FIR & IIR Filters, polyphase decomposition resamplers, and spectral estimation tools for both stationary and non-stationary data.
- Built using a developer-friendly object-oriented approach to support extensibility.
Installation
For each installation guide below, we strongly recommend creating a
virtual environment. This environment will isolate external dependencies
that may conflict with packages you already have installed on your system.
Python comes installed with a virtual environment manager called venv
.
Additionally, there are environment managers like conda
that can check
for package conflicts when the environment is created or updated. For more
information please see:
Python Virtual Environment
- Create your virtual environment, Here we name it
my_venv
.
$ python -m venv my_venv
- Activate your 'my_venv' environment
$ source my_venv/bin/activate
- Install openseize into your virtual environment
(my_venv)$ python -m pip install openseize
Conda
The conda
environment manager is more advanced than venv
. When a conda
environment is created, conda
simultaneously looks at all the packages
to be installed to reduce package conflicts. Having said that, conda
and
pip
, the tool used to install Openseize from pypi, do not always work
well together. The developers of conda
recommend installing all possible
packages from conda repositories before installing non-conda packages using
pip
. To ensure this order of installs, Openseize's source code includes an
environment configuration file (yml) that will build an openseize conda
environment. Once built you can then use pip
to install the openseize
package into this environment. Here are the steps:
-
Download the openseize environment configuration yaml
-
Create a conda openseize environment.
$ conda env create --file environment.yml
- Activate the
openseize
environment.
$ conda activate openseize
- Install openseize from pypi into your openseize environment.
(openseize)$ pip install openseize
From Source
If you would like to develop Openseize further, you'll need the source code and all development dependencies. Here are the steps:
- Create a virtual environment with latest pip version.
$ python3 -m venv env
$ source env/bin/activate
$ pip install --upgrade pip
- Get the source code
$ git clone https://github.com/mscaudill/openseize.git
- CD into the directory containing the pyproject.toml and create an
editable install with
pip
$ pip install -e .[dev]
Dependencies
Openseize requires Python ≥ 3.8 and has the following dependencies:
package | pypi | conda |
---|---|---|
requests | https://pypi.org/project/requests/ | ✓ |
wget | https://pypi.org/project/wget/ | ✓ |
numpy | https://pypi.org/project/numpy/ | ✓ |
scipy | https://pypi.org/project/scipy/ | ✓ |
matplotlib | https://pypi.org/project/matplotlib/ | ✓ |
ipython | https://pypi.org/project/ipython/ | ✓ |
notebook | https://pypi.org/project/jupyter/ | ✓ |
pytest | https://pypi.org/project/pytest/ | ✓ |
psutil | https://pypi.org/project/psutil/ | ✓ |
Documentation
Openseize documentation site has a quickstart guide, extensive tutorials and reference pages for all publicly available modules, classes and functions.
Attribution
Citation to be added
And if you really like Openseize, you can star the repository ⭐!
Contributions
Contributions are what makes open-source fun and we would love for you to contribute. Please check out our contribution guide to get started.
Issues
Openseize provides custom issue templates for filing bugs, requesting feature enhancements, suggesting documentation changes, or just asking questions. Ready to discuss? File an issue here.
License
Openseize is licensed under the terms of the 3-Clause BSD License.
Acknowledgements
This work was generously supported through the Ting Tsung and Wei Fong Chao Foundation.
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