awesome-mmps

Corpus of resources for multimodal machine learning with physiological signals (mmps).

https://github.com/willxxy/awesome-mmps

Science Score: 67.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
    Found CITATION.cff file
  • codemeta.json file
    Found codemeta.json file
  • .zenodo.json file
    Found .zenodo.json file
  • DOI references
    Found 60 DOI reference(s) in README
  • Academic publication links
    Links to: arxiv.org, medrxiv.org, preprints.org, researchgate.net, pubmed.ncbi, ncbi.nlm.nih.gov, sciencedirect.com, springer.com, wiley.com, nature.com, science.org, plos.org, frontiersin.org, mdpi.com, ieee.org, acm.org, iop.org, zenodo.org
  • Committers with academic emails
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (7.2%) to scientific vocabulary

Keywords

biosignals deep-learning machine-learning multimodal multimodal-data multimodal-deep-learning multimodal-learning physiological-signals signal-processing wearable wearable-devices
Last synced: 6 months ago · JSON representation ·

Repository

Corpus of resources for multimodal machine learning with physiological signals (mmps).

Basic Info
  • Host: GitHub
  • Owner: willxxy
  • License: mit
  • Default Branch: main
  • Homepage:
  • Size: 735 KB
Statistics
  • Stars: 105
  • Watchers: 9
  • Forks: 5
  • Open Issues: 0
  • Releases: 2
Topics
biosignals deep-learning machine-learning multimodal multimodal-data multimodal-deep-learning multimodal-learning physiological-signals signal-processing wearable wearable-devices
Created over 2 years ago · Last pushed 6 months ago
Metadata Files
Readme License Citation

README.md

awesome-mmps

DOI

Corpus of resources for multimodal machine learning with physiological signals.

Any additions, corrections, or concerns please submit an issue. For additions to the list, please provide the relevant information. Thank you! :)


Table of Contents

Publications-and-Preprints

EEG :brain: + X

ECG :anatomical_heart: + X

EDA :sweat_drops: + X

Eye Movement :eye: + X

EOG :eye: + X

EMG :muscle: + X

Other :placard: + X

Datasets

Laboratories

Citation

If you found this repository helpful in your research, please cite the following:

@software{han_2025_15151285, author = {Han, William}, title = {A corpus of resources for Multimodal Learning with Physiological Signals }, month = apr, year = 2025, publisher = {Zenodo}, version = {second}, doi = {10.5281/zenodo.15151285}, url = {https://doi.org/10.5281/zenodo.15151285}, swhid = {swh:1:dir:d44f23c5c3ed58e8f53d29c22dab067c30f0769b ;origin=https://doi.org/10.5281/zenodo.10903466;vi sit=swh:1:snp:571aa8cdb6e22b70e7833f00c447e7841903 938b;anchor=swh:1:rel:9fc43ffed2239a01f7dc6a4a96c3 ccac93e3dea1;path=willxxy-awesome-mmps-68d74c0 }, }

Owner

  • Login: willxxy
  • Kind: user
  • Company: Honda Research Institute

PhD @ CMU Safe AI Lab and Research @ Honda Research Institute

Citation (CITATION.cff)

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
  - family-names: Han
    given-names: William
    orcid: https://orcid.org/0009-0003-4262-4399
title: "A corpus of resources for Multimodal Learning with Physiological Signals"
version: 2.0.4
identifiers:
  - type: doi
    value: 10.5281/zenodo.10903467
date-released: 2024-04-01
url: "https://github.com/willxxy/awesome-mmps"

GitHub Events

Total
  • Create event: 1
  • Release event: 1
  • Issues event: 3
  • Watch event: 72
  • Issue comment event: 2
  • Push event: 105
  • Fork event: 2
Last Year
  • Create event: 1
  • Release event: 1
  • Issues event: 3
  • Watch event: 72
  • Issue comment event: 2
  • Push event: 105
  • Fork event: 2

Committers

Last synced: 8 months ago

All Time
  • Total Commits: 315
  • Total Committers: 1
  • Avg Commits per committer: 315.0
  • Development Distribution Score (DDS): 0.0
Past Year
  • Commits: 163
  • Committers: 1
  • Avg Commits per committer: 163.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
willxxy 9****y 315

Issues and Pull Requests

Last synced: 8 months ago

All Time
  • Total issues: 2
  • Total pull requests: 0
  • Average time to close issues: about 7 hours
  • Average time to close pull requests: N/A
  • Total issue authors: 2
  • Total pull request authors: 0
  • Average comments per issue: 1.0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 2
  • Pull requests: 0
  • Average time to close issues: about 7 hours
  • Average time to close pull requests: N/A
  • Issue authors: 2
  • Pull request authors: 0
  • Average comments per issue: 1.0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Top Authors
Issue Authors
  • lanxiang1017 (1)
  • sdimi (1)
Pull Request Authors
Top Labels
Issue Labels
Pull Request Labels