Skip to main content

Online adaptive acquisition of motor-evoked potential recruitment curves

Project description

onMEP

onMEP is a Python library for online adaptive acquisition of motor-evoked potential (MEP) recruitment curves.

Cite Cite

Citation

Please cite Tyagi et al., 2025 if you find this code useful in your research. The BibTeX entry for the paper is:

@article{tyagi_hierarchical_2025,
    title = {Hierarchical {Bayesian} estimation of motor-evoked potential recruitment curves yields accurate and robust estimates},
    author = {Tyagi, Vishweshwar and Murray, Lynda M. and Asan, Ahmet S. and Mandigo, Christopher and Virk, Michael S. and Harel, Noam Y. and Carmel, Jason B. and McIntosh, James R.},
    journal = {Brain Stimulation},
    year = {2025},
    doi = {10.1016/j.brs.2025.09.008},
}

License

onMEP is free software made available under the MIT License. For details see the LICENSE file.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

onmep-0.1.0.tar.gz (17.7 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

onmep-0.1.0-py3-none-any.whl (18.1 kB view details)

Uploaded Python 3

File details

Details for the file onmep-0.1.0.tar.gz.

File metadata

  • Download URL: onmep-0.1.0.tar.gz
  • Upload date:
  • Size: 17.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.10.12

File hashes

Hashes for onmep-0.1.0.tar.gz
Algorithm Hash digest
SHA256 46b206779faf24f9bf408f6d2f6f752f5539fb032a0adc35a3543224461ff790
MD5 a561f0e3cd7d1d4e7f510897d70c3546
BLAKE2b-256 e2956ed5c19f78f61dcde8b7428920baef35dae91a8941ab3912b82ab7253294

See more details on using hashes here.

File details

Details for the file onmep-0.1.0-py3-none-any.whl.

File metadata

  • Download URL: onmep-0.1.0-py3-none-any.whl
  • Upload date:
  • Size: 18.1 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.10.12

File hashes

Hashes for onmep-0.1.0-py3-none-any.whl
Algorithm Hash digest
SHA256 9e72901151b2d248dea14ebf9536db1b834cc8502b6fb4d4348ccb9e678b6bf5
MD5 1c2bbab6b279553993f1bea93f180139
BLAKE2b-256 d1ca542d9385ecf5e15761087482595d9b5b31d43f4977fa13224c5423f13694

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page