Measures ipsilateral MEPs
Project description
Detection of Ipsilateral Motor Evoked Potentials
DiMEP stands for Detection of ipsilateral Motor Evoked Potentials and was developed by Robert Guggenberger at the Institute of Neuromodulation and Neurotechnology of the University Hospital Tübingen.
Installation
Install the stabel release from PyPI with pip install dimep
and the most recent development tip with pip install git+https://github.com/translationalneurosurgery/tool-dimep.git
.
Usage
Access the algorithms with
from dimep.api import <algorithm>
and subsequently call them, e.g. with
from dimep.api import lewis
lewis(trace=trace, tms_sampleidx= 500, fs = 1000)
# where the trace is the single-channel EMG recording
# tms_sampleidx marks the onset of the TMS pulse
# and fs is the sampling rate.
Documentation
Read the documentation on readthedocs.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
dimep-0.2.tar.gz
(18.5 kB
view details)
Built Distribution
dimep-0.2-py3-none-any.whl
(26.4 kB
view details)
File details
Details for the file dimep-0.2.tar.gz
.
File metadata
- Download URL: dimep-0.2.tar.gz
- Upload date:
- Size: 18.5 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.24.0 setuptools/45.2.0.post20200210 requests-toolbelt/0.8.0 tqdm/4.56.0 CPython/3.7.6
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 29e8e760aad89b42b2b556a5ad6491de5925b2d843bdf4ffbe721728d8e14575 |
|
MD5 | 01d507702dcab676e19c7b4c8d497ce8 |
|
BLAKE2b-256 | 0333914d4d6cb23641c7c4e750fc72827fb8dcd0c9f8c47c45bd74606a3272a7 |
File details
Details for the file dimep-0.2-py3-none-any.whl
.
File metadata
- Download URL: dimep-0.2-py3-none-any.whl
- Upload date:
- Size: 26.4 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.24.0 setuptools/45.2.0.post20200210 requests-toolbelt/0.8.0 tqdm/4.56.0 CPython/3.7.6
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 1dcc0a387556c9e5a2ad67b75b23af37be2d0f06fd2d66d8cac8bd90aa0e7f06 |
|
MD5 | c78670b7669b99c02f06c810fdffeb93 |
|
BLAKE2b-256 | 607d23809700643236953fc40904a97726d784b4e381673f30d432bb161828e6 |