Analysis functions
Package: musclesyneRgies 1.2.5.9009
musclesyneRgies: Extract Muscle Synergies from Electromyography
Provides a framework to factorise electromyography (EMG) data. Tools are provided for raw data pre-processing, non negative matrix factorisation, classification of factorised data and plotting of obtained outcomes. In particular, reading from ASCII files is supported, along with wide-used filtering approaches to process EMG data. All steps include one or more sensible defaults that aim at simplifying the workflow. Yet, all functions are largely tunable at need. Example data sets are included.
Authors:
musclesyneRgies_1.2.5.9009.tar.gz
musclesyneRgies_1.2.5.9009.zip(r-4.7)musclesyneRgies_1.2.5.9009.zip(r-4.6)musclesyneRgies_1.2.5.9009.zip(r-4.5)
musclesyneRgies_1.2.5.9009.tgz(r-4.6-any)musclesyneRgies_1.2.5.9009.tgz(r-4.5-any)
musclesyneRgies_1.2.5.9009.tar.gz(r-4.7-any)musclesyneRgies_1.2.5.9009.tar.gz(r-4.6-any)
musclesyneRgies_1.2.5.9009.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
musclesyneRgies/json (API)
| # Install 'musclesyneRgies' in R: |
| install.packages('musclesyneRgies', repos = c('https://alesantuz.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/alesantuz/musclesynergies/issues
Datasets:
- act_pattern - Single activation pattern example
- act_patterns - All activation patterns of one synergy example
- FILT_EMG - Filtered EMG example
- RAW_DATA - Raw EMG example
- SYNS - Muscle synergies example
emgmuscle-synergiesnmfphysiolrstudio
Last updated from:0fbf53b01d. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 152 | ||
| source / vignettes | OK | 210 | ||
| linux-release-x86_64 | OK | 160 | ||
| macos-release-arm64 | OK | 192 | ||
| macos-oldrel-arm64 | OK | 169 | ||
| windows-devel | OK | 108 | ||
| windows-release | OK | 108 | ||
| windows-oldrel | OK | 118 | ||
| wasm-release | OK | 349 |
Exports:classify_kmeansCoAcossimfiltEMGFWHMHFDHurstnormEMGplot_classified_synsplot_classified_syns_UMAPplot_meanEMGplot_rawEMGplot_syn_trialsrawdatasMLEsubsetEMGsynsNMF
Dependencies:askpassclicpp11farverFNNggplot2gluegridExtragtablehereisobandjsonlitelabelinglatticelifecyclemagrittrMASSMatrixopensslplyrpngproxyR6rappdirsRColorBrewerRcppRcppEigenRcppTOMLreshape2reticulaterlangrprojrootRSpectraS7scalessignalstringistringrsysumapvctrsviridisLitewithr
Muscle synergy analysis with musclesyneRgies
Last update: 2023-01-12
Started: 2022-02-06
Pro tips for fully exploiting the potential of musclesyneRgies
Pro tips | Parallel computation | Fractal analysis
Last update: 2022-09-13
Started: 2022-02-06
Typical workflow for musclesyneRgies
What this package does: | What this package does not do: | Typical workflow | How to prepare your data set | Workflow example
Last update: 2022-09-13
Started: 2022-02-03
Plotting options in musclesyneRgies
Plots help the workflow
Last update: 2022-02-10
Started: 2022-02-06
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Single activation pattern example (30 cycles) | act_pattern |
| All activation patterns of one synergy example (30 cycles) | act_patterns |
| Muscle synergy classification with k-means | classify_kmeans |
| Centre of activity | CoA |
| Cosine similarity | cossim |
| Filtered EMG example | FILT_EMG |
| To filter raw EMG | filtEMG |
| Full width at half maximum | FWHM |
| Higuchi's fractal dimension | HFD |
| Hurst exponent | Hurst |
| To time-normalise filtered EMG | normEMG |
| Plot muscle synergies | plot_classified_syns |
| Plot 2D UMAP of muscle synergies | plot_classified_syns_UMAP |
| Plot EMG averaged across all cycles | plot_meanEMG |
| Plot raw EMG | plot_rawEMG |
| Plot muscle synergies (individual trials) | plot_syn_trials |
| Raw EMG example | RAW_DATA |
| Import RData or ASCII data into R | rawdata |
| Short-term maximum Lyapunov exponents | sMLE |
| Subset raw EMG | subsetEMG |
| Muscle synergies example | SYNS |
| Non-negative matrix factorisation | synsNMF |
