PhysioQA
Title
PhysioQA
Leaders
@RickReddy - Rithwik Guntaka
Collaborators
@rgbayrak - Roza Bayrak
Brainhack Global 2023 Event
BrainHack Vanderbilt
Project Description
We are working on creating a model that can classify physiological data (respiratory + cardiac) that is associated with fMRI data, so that the end user can determine whether the data is usable, if it needs to be modified to be usable, or if it is simply not usable.
When it comes to using peripheral physiological data in your fMRI data analysis, the quality of the recordings is super important, but let’s face it, checking the quality of this data can be a real headache. It usually involves a lot of manual work and you need to know what is real data, what is an artifact. That’s why we want to create a nifty deep-learning tool to automate quality assessment! This tool doesn’t just check the quality of your data; it also points out any issues and gives you tips on how to fix them. It’s like having a friendly expert on your team, making sure your research data is as good as it can be!
Link to project repository/sources
https://github.com/brainhack-vandy/projects/blob/main/physioQA.md
Goals for Brainhack Global
- Brainstorm different techniques to increase the classification accuracy of the model
- Modify the MATLAB GUI to be more usable for classifying data
- Potentially, getting data labeled by an expert who is familiar with physiological data
Good first issues
Classification tool (beginner machine learning friendly)
- experimenting with different neural network architectures using keras
- experimenting with feature engineering
- experiment with different hyperparameters for the model
Manual annotation tool
- adding more button functionality to the GUI tool, to allow for more detailed labeling of data
- modify the GUI to select and label certain sections of data
Communication channels
#physioqa channel on https://discord.gg/GyeeVbYC
Skills
Having any one of these skills would enable an individual to contribute. However, if they have none of these there are onboarding documents that would help them experiment, learn, and contribute regardless.
- Familiarity with Python and Jupyter notebooks
- Familiarity with MATLAB
- Familiarity with physiological data in order to asses its quality
Onboarding documentation
No response
What will participants learn?
Participants will:
- Learn the significance and influence of physiological data in fMRI analysis
- Become familiar with and explore different aspects of ML, and how it can be used for timeseries data
- Learning how to create/modify a GUI to analyze timeseries data using MATLAB toolbox
Data to use
Public HCP dataset that has physiological data paired with fMRI data.
https://www.humanconnectome.org/study/hcp-young-adult
Number of collaborators
3
Credit to collaborators
Collaborators will be credited on the GitHub site and credited in any paper that results from this project
Image
Type
method_development, pipeline_development, visualization
Development status
1_basic structure
Topic
data_visualisation, deep_learning, machine_learning, physiology
Tools
Jupyter
Programming language
Matlab, Python
Modalities
fMRI, other
Git skills
0_no_git_skills, 1_commit_push, 2_branches_PRs
Anything else?
other under modalities: physiological data (cardiac + respiration)
Things to do after the project is submitted and ready to review.
- Add a comment below the main post of your issue saying:
Hi @brainhackorg/project-monitors my project is ready! - Twitter-sized summary of your project pitch.