https://github.com/gweindel/hmp
Repository for the hmp python package
Science Score: 39.0%
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○Scientific vocabulary similarity
Low similarity (12.1%) to scientific vocabulary
Keywords
Repository
Repository for the hmp python package
Basic Info
Statistics
- Stars: 46
- Watchers: 5
- Forks: 11
- Open Issues: 5
- Releases: 15
Topics
Metadata Files
README.md
HMP
HMP is an open-source Python package to analyze neural time-series (e.g. EEG) to estimate Hidden Multivariate Patterns. HMP is described in Weindel, van Maanen & Borst (2024, paper ) and is a generalized and simplified version of the HsMM-MVPA method developed by Anderson, Zhang, Borst, & Walsh (2016).
As a summary of the method, an HMP model parses the reaction time into a number of successive events determined based on patterns in a neural time-serie (e.g. EEG, MEG). Hence any reaction time (or any other relevant behavioral duration) can then be described by a number of cognitive events and the duration between them estimated using HMP. The important aspect of HMP is that it is a whole-brain analysis (or whole scalp analysis) that estimates the peak of trial-recurrent multivariate events on a single-trial basis. These by-trial estimates allow you then to further dig into any aspect you are interested in a signal: - Describing an experiment or a clinical sample in terms of events detected in the EEG signal - Describing experimental effects based on the time onset of a particular event - Estimating the effect of trial-wise manipulations on the identified event presence and time occurrence (e.g. the by-trial variation of stimulus strength or the effect of time-on-task) - Time-lock EEG signal to the onset of a given event and perform classical ERPs or time-frequency analysis based on the onset of a new event - And many more (e.g. evidence accumulation models, classification based on the number of events in the signal,...)
Documentation
The documentation for the latest version is available on readthedocs: https://hmp.readthedocs.io/en/latest/welcome.html
To get started
To get started with the code you can run the different tutorials in docs/source/notebooks after having installed HMP (see documentation)
- General aspects on HMP (tutorial 1)
- The different estimation methods (tutorial 2)
- Applying HMP to real data (tutorial 3)
- Load your own EEG data
Citation:
To cite the HMP method you can use the following paper:
Weindel, G., van Maanen, L., & Borst, J. P. (2024). Trial-by-trial detection of cognitive events in neural time-series. Imaging Neuroscience, 2, 1-28.
Owner
- Login: GWeindel
- Kind: user
- Location: Utrecht
- Company: University of Groningen & University of Utrecht
- Website: https://gweindel.github.io
- Twitter: GWeindel
- Repositories: 3
- Profile: https://github.com/GWeindel
French/German cognitive scientist, torturing cognitive models using electrophysiology in the Netherlands. #Stats #Bayes #linux enthusiast
GitHub Events
Total
- Fork event: 3
- Create event: 49
- Commit comment event: 1
- Issues event: 20
- Release event: 5
- Watch event: 12
- Delete event: 41
- Member event: 3
- Issue comment event: 49
- Push event: 200
- Pull request review comment event: 92
- Pull request event: 110
- Pull request review event: 90
Last Year
- Fork event: 3
- Create event: 49
- Commit comment event: 1
- Issues event: 20
- Release event: 5
- Watch event: 12
- Delete event: 41
- Member event: 3
- Issue comment event: 49
- Push event: 200
- Pull request review comment event: 92
- Pull request event: 110
- Pull request review event: 90
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 78
- Total pull requests: 188
- Average time to close issues: 3 months
- Average time to close pull requests: 2 days
- Total issue authors: 8
- Total pull request authors: 8
- Average comments per issue: 1.06
- Average comments per pull request: 0.54
- Merged pull requests: 145
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 13
- Pull requests: 113
- Average time to close issues: 3 months
- Average time to close pull requests: 4 days
- Issue authors: 4
- Pull request authors: 6
- Average comments per issue: 0.38
- Average comments per pull request: 0.58
- Merged pull requests: 79
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- GWeindel (62)
- jelmerborst (2)
- JoKra1 (2)
- dmkhitaryan (2)
- SKMuller (2)
- a1phaeeg (1)
- rickdott (1)
- qubixes (1)
Pull Request Authors
- GWeindel (101)
- jelmerborst (60)
- qubixes (33)
- nmararo (9)
- rickdott (5)
- maartenschermer (3)
- jelletreep (2)
- dmkhitaryan (2)
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Packages
- Total packages: 1
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Total downloads:
- pypi 168 last-month
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 8
- Total maintainers: 1
pypi.org: hmp
Package for fitting Hidden Multivariate pattern model to time-series
- Homepage: https://github.com/GWeindel/hmp
- Documentation: https://hmp.readthedocs.io/
- License: BSD 3-Clause License Copyright (c) 2022, Gabriel Weindel, Leendert van Maanen, Jelmer Borst All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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Latest release: 0.5.0
published about 1 year ago