Problem: Across EEG preprocessing software tools, there appears to be variation in source data used for example analysis. Switching contexts when explaining the utility of a pipeline tool obscures the comparative effectiveness of one tool in a set of competing tools.
Alternatives:
Design: Inspired by the database/framework defects4j, I propose an open-source and standardized set of diverse & unprocessed EEG data that comes with a python framework necessary to generate data, plug-in pipelines, and test clean data.
It will come with a container for running, testing, and developing. Some creative feat perhaps could be inserted in this section that allows for easy containerization of varying EEG tools across languages. Ex: A script that parses import libraries and appends those libraries and their dependencies to the container. It would also contain the necessary scripts to allow for parallel processing on HPC clusters.
This framework would directly inherit the testing metrics composed from the PEPPER-pipeline.
Informally, the design would like this:
Each tool is manually pulled into a cluster (preferably) by the user. The common test suite is run on each output of each pipeline (all of which are run in parallel by the cluster). A multidimensional dataset explaining each metric is then created and saved upon completion:
Lastly, EEG4p stands for EEG-4-Processing.
Funding:
Authors: If a paper were to be written, and considering the pre-requisite EEG knowledge required to compose such a dataset, I propose that someone knowledgeable of EEG data take on a first author role, while I can focus on the work of implementing the testing framework, makefiles, and data hosting.
But on the software side of things, anyone that makes a valid contribution is a contributor.
Milestones:
Assume this project's planning is begun October 1st. Perhaps the following schedule could work:
- Lab presentation. [November 1st, 2021]
- 1st pre-release announced, stable for contributors, and tests for the framework are made. [November 22nd, 2021]
- 1st release announced, stable for researchers [December 1st, 2021]
- Perhaps a paper is written on this approach? [January 2022?]
Problem: Across EEG preprocessing software tools, there appears to be variation in source data used for example analysis. Switching contexts when explaining the utility of a pipeline tool obscures the comparative effectiveness of one tool in a set of competing tools.
Alternatives:
Design: Inspired by the database/framework defects4j, I propose an open-source and standardized set of diverse & unprocessed EEG data that comes with a python framework necessary to generate data, plug-in pipelines, and test clean data.
It will come with a container for running, testing, and developing. Some creative feat perhaps could be inserted in this section that allows for easy containerization of varying EEG tools across languages. Ex: A script that parses import libraries and appends those libraries and their dependencies to the container. It would also contain the necessary scripts to allow for parallel processing on HPC clusters.
This framework would directly inherit the testing metrics composed from the PEPPER-pipeline.
Informally, the design would like this:
Each tool is manually pulled into a cluster (preferably) by the user. The common test suite is run on each output of each pipeline (all of which are run in parallel by the cluster). A multidimensional dataset explaining each metric is then created and saved upon completion:
Lastly, EEG4p stands for EEG-4-Processing.
Funding:
Authors: If a paper were to be written, and considering the pre-requisite EEG knowledge required to compose such a dataset, I propose that someone knowledgeable of EEG data take on a first author role, while I can focus on the work of implementing the testing framework, makefiles, and data hosting.
But on the software side of things, anyone that makes a valid contribution is a contributor.
Milestones:
Assume this project's planning is begun October 1st. Perhaps the following schedule could work: