Title:	Extract Muscle Synergies from Electromyography
Version:	1.2.5
Description:	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.
License:	MIT + file LICENSE
URL:	https://github.com/alesantuz/musclesyneRgies
BugReports:	https://github.com/alesantuz/musclesyneRgies/issues
Depends:	R (≥ 4.1.0)
Imports:	FNN, ggplot2, graphics, grDevices, gridExtra, plyr, proxy, reshape2, signal, stats, umap
Suggests:	knitr, rmarkdown, testthat (≥ 3.0.0)
VignetteBuilder:	knitr
Config/testthat/edition:	3
Encoding:	UTF-8
Language:	en-GB
LazyData:	true
RoxygenNote:	7.2.1
NeedsCompilation:	no
Packaged:	2022-07-19 16:59:00 UTC; aless
Author:	Alessandro Santuz [aut, cre]
Maintainer:	Alessandro Santuz <alessandro.santuz@gmail.com>
Repository:	CRAN
Date/Publication:	2022-07-19 17:10:02 UTC

Centre of activity

Description

Centre of activity

Usage

CoA(x)

Arguments

Value

The centre of activity of the time series, calculated with circular statistics

References

Martino, G. et al. Locomotor patterns in cerebellar ataxia. J. Neurophysiol. 112, 2810–2821 (2014).

Examples

# Number of users connected to the Internet through a server every minute
ts <- datasets::WWWusage[1:80]

# Calculate CoA
ts_CoA <- CoA(ts)

# Plot
plot(ts, ty = "l", xlab = "Time", ylab = "Number of users")
graphics::abline(v = ts_CoA, lwd = 2, lty = 2)

Filtered EMG example

Description

A list containing filtered and time-normalised electromyographic (EMG) human data from the right-side lower limb recorded during one walking trial.

Usage

FILT_EMG

Format

A list containing one object of class EMG with elements cycles and emg, both data frames.

ID0012_TW_01

Object of class EMG containing the two following data frames:

cycles

Gait cycle-timings, in seconds.

emg

Filtered and time-normalised EMG, first column is time in points, muscles named as:

ME=gluteus medius
MA=gluteus maximus
FL=tensor fasciae latae
RF=rectus femoris
VM=vastus medialis
VL=vastus lateralis
ST=semitendinosus
BF=biceps femoris
TA=tibialis anterior
PL=peroneus longus
GM=gastrocnemius medialis
GL=gastrocnemius lateralis
SO=soleus

Source

doi:10.1016/j.isci.2019.100796

Full width at half maximum

Description

Full width at half maximum

Usage

FWHM(x, sub_minimum = TRUE)

Arguments

Value

The full width at half maximum of the time series.

References

Martino, G. et al. Locomotor patterns in cerebellar ataxia. J. Neurophysiol. 112, 2810–2821 (2014).

Examples

# Number of users connected to the Internet through a server every minute
ts <- datasets::WWWusage

# Calculate FWHM
ts_FWHM <- FWHM(ts)

# Half maximum (for the plots)
hm <- min(ts) + (max(ts) - min(ts)) / 2
hm_plot <- ts
hm_plot[which(hm_plot > hm)] <- hm
hm_plot[which(hm_plot < hm)] <- NA

# Plots
plot(ts, ty = "l", xlab = "Time", ylab = "Number of users")
lines(hm_plot, lwd = 3, col = 2)

Higuchi's fractal dimension

Description

Higuchi's fractal dimension

Usage

HFD(P, k_max = 10)

Arguments

Details

The Higuchi’s fractal dimension is a measure of local complexity and it increases together with the “roughness” of the time series at a single cycle level (thus the term “local”). Higuchi’s fractal dimension values range from 1 to 2, with increasing values correlating to increasingly complex data and Higuchi’s fractal dimension = 1.5 indicating random Gaussian noise (Higuchi, 1988; Anmuth et al., 1994; Kesić & Spasić, 2016) For motor primitives, only the most linear part of the log-log plot should be used, as reported in Santuz, Akay (2020).

Value

A list with elements:

• loglog containing the log-log plot from which the HFD is calculated
  

• Higuchi containing the Higuchi's fractal dimension of the time series.

References

Higuchi, T. Approach to an irregular time series on the basis of the fractal theory. Phys. D Nonlinear Phenom. 31, 277–283 (1988).

Anmuth C. J., Goldberg G. & Mayer N. H. Fractal dimension of electromyographic signals recorded with surface electrodes during isometric contractions is linearly correlated with muscle activation. Muscle Nerve 17, 953–954 (1994).

Kesić S. & Spasić S. Z. Application of Higuchi’s fractal dimension from basic to clinical neurophysiology: A review. Comput Methods Programs Biomed 133, 55–70 (2016).

Santuz, A. & Akay, T. Fractal analysis of muscle activity patterns during locomotion: pitfalls and how to avoid them. J. Neurophysiol. 124, 1083–1091 (2020).

Examples

# Measurements of the annual flow of the river Nile at Aswan
flow <- datasets::Nile

# Calculate HFD
fractal_dimension <- HFD(flow)$Higuchi
message("Higuchi's fractal dimension: ", round(fractal_dimension, 3))

# Thirty-cycle locomotor primitive from Santuz & Akay (2020)
data(primitive)
fractal_dimension <- HFD(primitive$signal)$Higuchi
message("Higuchi's fractal dimension: ", round(fractal_dimension, 3))

Hurst exponent

Description

Hurst exponent

Usage

Hurst(P, min_win = 2)

Arguments

Details

Hurst calculates the Hurst exponent based on the R/S approach as in Hurst (1951). The Hurst exponent is a measure of global complexity and it increases if the “accuracy” of the time series decreases across several cycles (thus the term “global”). The Hurst exponent can vary between 0 and 1. For 0.5 < Hurst exponent < 1, in the long-term high values in the time series (the motor primitive in our case) will be probably followed by other high values and a positive or negative trend is visible (Mandelbrot, 1983; Gneiting & Schlather, 2004). For 0 < Hurst exponent < 0.5, in the long term high values in the series will be probably followed by low values, with a frequent switch between high and low values (Mandelbrot, 1983; Gneiting & Schlather, 2004). Hurst exponent = 0.5 corresponds to a completely random series (Mandelbrot, 1983; Qian & Rasheed, 2004). In other words, values of Hurst exponent approaching 0.5 from both ends indicate more complex (or random) behaviour of the time series (Hurst, 1951). For motor primitives, the minimum window length should be bigger than the period (i.e. the length of each cycle), as reported in Santuz, Akay (2020).

Value

A list with elements:

• loglog containing the log-log plot from which the HFD is calculated
  

• Hurst containing the Higuchi's fractal dimension of the time series.

References

Hurst, H. E. Long-term storage capacity of reservoirs. Trans. Am. Soc. Civ. Eng. 116, 770-808 (1951).

Mandelbrot B. B. The Fractal Geometry of Nature. W. H. Freeman and Co., New York (1983).

Gneiting T .& Schlather M. Stochastic Models That Separate Fractal Dimension and the Hurst Effect. SIAM Rev 46, 269–282 (2004).

Qian B. & Rasheed K. Hurst exponent and financial market predictability. In Proceedings of the Second IASTED International Conference on Financial Engineering and Applications, pp. 203–209 (2004).

Santuz, A. & Akay, T. Fractal analysis of muscle activity patterns during locomotion: pitfalls and how to avoid them. J. Neurophysiol. 124, 1083-1091 (2020).

Examples

# Measurements of the annual flow of the river Nile at Aswan
flow <- datasets::Nile

# Calculate Hurst exponent
H <- Hurst(flow)$Hurst
message("Hurst exponent: ", round(H, 3))

# Thirty-cycle locomotor primitive from Santuz & Akay (2020)
data(primitive)
H <- Hurst(primitive$signal, min_win = max(primitive$time))$Hurst
message("Hurst exponent: ", round(H, 3))

Raw EMG example

Description

A list containing electromyographic (EMG) human data from the right-side lower limb recorded during one walking trial.

Usage

RAW_DATA

Format

A list containing one object of class EMG with elements cycles and emg, both data frames.

ID0012_TW_01

Object of class EMG containing the two following data frames:

cycles

Gait cycle-timings, in seconds.

emg

Raw EMG, first column is time in seconds, muscles named as:

ME=gluteus medius
MA=gluteus maximus
FL=tensor fasciae latae
RF=rectus femoris
VM=vastus medialis
VL=vastus lateralis
ST=semitendinosus
BF=biceps femoris
TA=tibialis anterior
PL=peroneus longus
GM=gastrocnemius medialis
GL=gastrocnemius lateralis
SO=soleus

Source

doi:10.1016/j.isci.2019.100796

Muscle synergies example

Description

A list created by synsNMF containing muscle synergies extracted from 15 humans walking on a treadmill.

Usage

SYNS

Format

A list containing 15 objects of class musclesyneRgies, each of which represents a walking trial from a different person.

ID0012_TW_01
ID0001_TW_01
ID0002_TW_01
ID0003_TW_01
ID0004_TW_01
ID0005_TW_01
ID0006_TW_01
ID0007_TW_01
ID0008_TW_01
ID0009_TW_01
ID0010_TW_01
ID0011_TW_01
ID0012_TW_01
ID0013_TW_01
ID0014_TW_01
ID0015_TW_01

Objects of class musclesyneRgies containing the following items:

syns

Factorisation rank or minimum number of synergies.

M

Motor modules (time-invariant coefficients)

P

Motor primitives (time-dependent coefficients)

V

Original data, muscles named as:

ME=gluteus medius
MA=gluteus maximus
FL=tensor fasciae latae
RF=rectus femoris
VM=vastus medialis
VL=vastus lateralis
ST=semitendinosus
BF=biceps femoris
TA=tibialis anterior
PL=peroneus longus
GM=gastrocnemius medialis
GL=gastrocnemius lateralis
SO=soleus

Vr

Reconstructed data, muscles named as in Vr

iterations

Number of iterations to convergence

R2

Quality of reconstruction (coefficient of determination)

classification

Classification type (e.g., none, k-means, NMF, etc.)

Source

doi:10.1016/j.isci.2019.100796

Muscle synergy classification with k-means

Description

Muscle synergy classification with k-means

Usage

classify_kmeans(x, MSE_lim = 0.001, inspect = FALSE, show_plot = FALSE)

Arguments

Details

This function must be applied to a list with a sufficient amount of trials, otherwise the classification will not work. Typically, at least 10 trials for the same condition are needed for satisfactory classification. If show_plot is TRUE (default) plots are also shown in the active graphic device. Plots can then be saved with the preferred export method, such as ggplot2::ggsave. The algorithm used is the default for stats::kmeans (Hartigan and Wong, 1979), which is known for its robustness to local minima. Nonetheless, the stochastic nature of the algorithm should prompt the user to attempt a few classifications and analyse their stability, before drawing conclusions on e.g. the number of fundamental synergies and/or their function. While the default parameters are optimised for human locomotion, it is suggested to test the function with different mean squared error thresholds, which is a crucial quantity to determine the number of clusters. Inspection and plotting are as well highly recommended to gain more insight into the classification process.

Value

List of musclesyneRgies objects, each with elements:

• syns factorisation rank or minimum number of synergies
  

• M motor modules (time-invariant coefficients)
  

• P motor primitives (time-dependent coefficients)
  

• V original data
  

• Vr reconstructed data
  

• iterations number of iterations to convergence
  

• R2 quality of reconstruction (coefficient of determination)

• rank_type was the rank fixed or variable?
  

• classification classification type (k-means)

Examples

# Load some data
data(SYNS)
# Classify synergies
SYNS_classified <- classify_kmeans(SYNS)

To filter raw EMG

Description

To filter raw EMG

Usage

filtEMG(
  x,
  demean = TRUE,
  rectif = "fullwave",
  HPf = 50,
  HPo = 4,
  LPf = 20,
  LPo = 4,
  min_sub = TRUE,
  ampl_norm = TRUE
)

Arguments

Details

Lists in the correct format can be created with the function rawdata(). The first column of each emg element must be time in the same units as those used for cycles (e.g., [s] or [ms]).

Value

Object of class EMG with elements:

• cycles data frame containing cycle timings, with as many columns as many cycle subdivisions are wanted
  

• emg data frame containing filtered EMG data in columns, first column is time

References

Santuz, A., Ekizos, A., Janshen, L., Baltzopoulos, V. & Arampatzis, A. On the Methodological Implications of Extracting Muscle Synergies from Human Locomotion. Int. J. Neural Syst. 27, 1750007 (2017).

Examples

# Load some data
data("RAW_DATA")
# Filter raw EMG
filtered_EMG <- lapply(
  RAW_DATA,
  function(x) {
    filtEMG(x,
      HPf = 50,
      HPo = 4,
      LPf = 20,
      LPo = 4
    )
  }
)

To time-normalise filtered EMG

Description

To time-normalise filtered EMG

Usage

normEMG(x, trim = TRUE, cy_max = NA, cycle_div = NA)

Arguments

Details

Lists in the correct format can be created with the function rawdata(). The first column of each emg element must be time in the same units as those used for cycles (e.g., [s] or [ms]).

Value

Object of class EMG with elements:

• cycles data frame containing cycle timings, with as many columns as many cycle subdivisions are wanted
  

• emg data frame containing filtered and time-normalised EMG data in columns, first column is time

References

Santuz, A., Ekizos, A., Janshen, L., Baltzopoulos, V. & Arampatzis, A. On the Methodological Implications of Extracting Muscle Synergies from Human Locomotion. Int. J. Neural Syst. 27, 1750007 (2017).

Examples

# Load some data
data("RAW_DATA")
# Filter raw EMG
filtered_EMG <- lapply(RAW_DATA, function(x) {
  filtEMG(x, HPf = 50, HPo = 4, LPf = 20, LPo = 4)
})
# Time-normalise filtered EMG, including three cycles and trimming first and last
filt_norm_EMG <- lapply(filtered_EMG, function(x) {
  normEMG(
  x,
  cy_max = 3,
  cycle_div = c(100, 100))
})

Plot muscle synergies

Description

Plot muscle synergies

Usage

plot_classified_syns(
  x,
  dark_mode = FALSE,
  line_size = 0.9,
  dot_size = 0.1,
  line_col = "black",
  sd_col = "grey80",
  condition = NA,
  show_plot = TRUE
)

Arguments

Details

If show_plot is TRUE (default) plots are also shown in the active graphic device. Plots can then be saved with the preferred export method, such as ggplot2::ggsave.

Value

Global plot containing the average classified muscle synergies and individual trials (motor modules) or standard deviations (motor primitives)

Examples

# Load some data
data(SYNS)

# Classify synergies with k-means
SYNS_classified <- classify_kmeans(SYNS)

# Save plot of classified synergies
pp <- plot_classified_syns(SYNS_classified,
  dark_mode = TRUE,
  line_col = "tomato1",
  sd_col = "tomato4",
  condition = "TW",
  show_plot = FALSE
)

Plot 2D UMAP of muscle synergies

Description

Plot 2D UMAP of muscle synergies

Usage

plot_classified_syns_UMAP(x, condition, show_plot = TRUE)

Arguments

Details

If show_plot is TRUE (default) plots are also shown in the active graphic device. Plots can then be saved with the preferred export method, such as ggplot2::ggsave.

Value

2D UMAP plot of classified synergies.

Examples

# Load some data
data(SYNS)

# Classify synergies with k-means
SYNS_classified <- classify_kmeans(SYNS)

# Save plot
pp <- plot_classified_syns_UMAP(SYNS_classified,
  condition = "TW",
  show_plot = FALSE
)

Plot EMG averaged across all cycles

Description

Plot EMG averaged across all cycles

Usage

plot_meanEMG(
  x,
  trial,
  row_number = NA,
  col_number = 1,
  dark_mode = FALSE,
  line_size = 0.6,
  line_col = "black",
  show_plot = TRUE
)

Arguments

Details

If show_plot is TRUE (default) plots are also shown in the active graphic device. Plots can then be saved with the preferred export method, such as ggplot2::ggsave.

Value

Exports average filtered and normalised EMG.

Examples

# Load some data
data(FILT_EMG)

# Save a plot of the only present trial with the average filtered and time-normalised EMG
pp <- plot_meanEMG(FILT_EMG[[1]],
  trial = names(FILT_EMG)[1],
  row_number = 4,
  col_number = 4,
  dark_mode = TRUE,
  line_col = "tomato3",
  show_plot = FALSE
)

Plot raw EMG

Description

Plot raw EMG

Usage

plot_rawEMG(
  x,
  trial,
  plot_time = 3,
  start = 1,
  row_number = NA,
  col_number = 1,
  dark_mode = FALSE,
  line_size = 0.3,
  line_col = "black",
  show_plot = TRUE
)

Arguments

Details

If show_plot is TRUE (default) plots are also shown in the active graphic device. Plots can then be saved with the preferred export method, such as ggplot2::ggsave.

Value

Plots raw EMG trials of the specified length.

Examples

# Load some data
data(RAW_DATA)

# Save a plot with the first (and only) trial in RAW_DATA, first three seconds, in dark mode
plot_rawEMG(RAW_DATA[[1]],
  trial = names(RAW_DATA)[1],
  row_number = 4,
  col_number = 4,
  dark_mode = TRUE,
  line_col = "tomato3",
  show_plot = FALSE
)

Plot muscle synergies (individual trials)

Description

Plot muscle synergies (individual trials)

Usage

plot_syn_trials(
  x,
  max_syns,
  trial,
  dark_mode = FALSE,
  line_size = 0.6,
  line_col = "black",
  sd_col = "grey80",
  show_plot = TRUE
)

Arguments

Details

If show_plot is TRUE (default) plots are also shown in the active graphic device. Plots can then be saved with the preferred export method, such as ggplot2::ggsave.

Value

Plots of the unclassified synergies, trial by trial.

Examples

# Load some data
data(SYNS)

# Find maximum number of synergies
max_syns <- max(unlist(lapply(SYNS, function(x) x$syns)))

# Save a plot with the first (and only, in this case) trial in the list
pp <- plot_syn_trials(SYNS[[1]],
  max_syns = max_syns,
  trial = names(SYNS)[1],
  dark_mode = TRUE,
  line_size = 0.8,
  line_col = "tomato1",
  sd_col = "tomato4",
  show_plot = FALSE
)

Single motor primitive example (30 cycles)

Description

A data frame containing one motor primitive extracted from one wild type mouse walking on a treadmill.

Usage

primitive

Format

A data frame of two columns:

time

Normalised time in points.

signal

Motor primitive

Source

doi:10.1152/jn.00360.2020

All motor primitives of one synergy example (30 cycles)

Description

A demo and incomplete musclesyneRgies object containing time info and three motor primitives extracted from one wild type mouse walking on a treadmill.

Usage

primitives

Format

A data frame of four columns:

time

Normalised time in points.

Syn1

Motor primitive of synergy 1

Syn2

Motor primitive of synergy 2

Syn3

Motor primitive of synergy 3

Source

doi:10.1152/jn.00360.2020

Import RData or ASCII data into R

Description

Import RData or ASCII data into R

Usage

rawdata(path_cycles = NA, path_emg = NA, header_cycles, header_emg = TRUE)

Arguments

Details

Supported are R lists saved as RData files or tab- or comma-separated files readable through read.table() or read.csv(). The first column of each raw emg file must be time in the same units as those used for the cycle timings (e.g., [s] or [ms]). If reading from RData files, please call cycles CYCLE_TIMES.RData and raw EMG RAW_EMG.RData. Lists must be saved with save().

Value

List of objects of class EMG, each with elements:

• cycles data frame containing cycle timings, with as many columns as many cycle subdivisions are wanted
  

• emg data frame containing raw EMG data in columns, first column must be time in the same units as in the cycle timings

Examples

# Load built-in data set
data("RAW_DATA")

# Get current working directory
data_path <- getwd()
data_path <- paste0(data_path, .Platform$file.sep)

# Create two conveniently-named subfolders if they don't already exist
# (if they exist, please make sure they're empty!)
dir.create("cycles", showWarnings = FALSE)
dir.create("emg", showWarnings = FALSE)

# Export ASCII data from built-in data set to the new subfolders
write.table(RAW_DATA[[1]]$cycles,
  file = paste0(data_path, "cycles", .Platform$file.sep, names(RAW_DATA)[1], ".txt"),
  sep = "\t", row.names = FALSE, col.names = FALSE
)
write.table(RAW_DATA[[1]]$emg,
  file = paste0(data_path, "emg", .Platform$file.sep, names(RAW_DATA)[1], ".txt"),
  sep = "\t", row.names = FALSE
)

# Run the function to parse ASCII files into objects of class `EMG`
raw_data_from_files <- rawdata(
  path_cycles = paste0(data_path, "/cycles/"),
  path_emg = paste0(data_path, "/emg/"),
  header_cycles = FALSE
)

# Check data in the new folders if needed before running the following (will delete!)

# Delete folders
unlink("cycles", recursive = TRUE)
unlink("emg", recursive = TRUE)

Short-term maximum Lyapunov exponents

Description

Short-term maximum Lyapunov exponents

Usage

sMLE(synergies, mean_period, future_pts, norm, pts, R2_threshold = 0.9)

Arguments

Details

The mean period is intended to exclude temporally close points. In gait, values are usually plus/minus half gait cycle. Future points usually correspond in gait to one to two gait cycles. Please consider that a sufficient amount of cycles in order to compute meaningful sMLE. For locomotor primitives, 30 gait cycles have been shown to be sensitive to perturbations (Santuz et al. 2020). However, in the more classical and widespread use on kinematic data, more are usually needed (Kang and Dingwell, 2006).

Value

A list with elements:

• divergences containing the average logarithmic divergence curve
  

• sMLE the short-term Maximum Lyapunov exponent
  

• R2 the goodness of fit of the most linear part of the divergence curve

References

Rosenstein, M.T., Collins, J.J., and De Luca, C.J. (1993). A practical method for calculating largest Lyapunov exponents from small data sets. Phys. D 65, 117–134.

Santuz A, Brüll L, Ekizos A, Schroll A, Eckardt N, Kibele A, et al. Neuromotor Dynamics of Human Locomotion in Challenging Settings. iScience. 2020;23: 100796.

Kang H.G., and Dingwell J.B. (2006). Intra-session reliability of local dynamic stability of walking. Gait Posture. 24(3) 386-390.

Examples

# Load some primitives
data("primitives")
# Calculate sMLE of motor primitives in the muscle synergy space
short_term_MLE <- sMLE(primitives,
  mean_period = 80,
  future_pts = 200,
  norm = "z",
  pts = 30
)

Subset raw EMG

Description

Subset raw EMG

Usage

subsetEMG(x, cy_max, cy_start = 1)

Arguments

Details

Lists in the correct format can be created with the function rawdata(). The first column of each emg element must be time in the same units as those used for cycles (e.g., [s] or [ms]). For locomotion, thirty cycles are enough for proper synergy extraction (Oliveira et al. 2014).

Value

Object of class EMG with elements:

• cycles data frame containing cycle timings, with as many columns as many cycle subdivisions are wanted
  

• emg data frame containing raw EMG data in columns, first column is time

References

Oliveira, A. S. C., Gizzi, L., Farina, D. & Kersting, U. G. Motor modules of human locomotion: influence of EMG averaging, concatenation, and number of step cycles. Front. Hum. Neurosci. 8, 335 (2014).

Examples

# Load some data
data("RAW_DATA")
# Subset example raw data to the first 3 cycles
RAW_DATA_sub <- lapply(
  RAW_DATA,
  function(x) {
    subsetEMG(x,
      cy_max = 3,
      cy_start = 1
    )
  }
)

Non-negative matrix factorisation

Description

Non-negative matrix factorisation

Usage

synsNMF(
  V,
  R2_target = 0.01,
  runs = 5,
  max_iter = 1000,
  last_iter = 20,
  MSE_min = 1e-04,
  fixed_syns = NA
)

Arguments

Details

The first column of V must always contain time information.

Value

Object of class musclesyneRgies with elements:

• syns factorisation rank or minimum number of synergies
  

• M motor modules (time-invariant coefficients)
  

• P motor primitives (time-dependent coefficients)
  

• V original data
  

• Vr reconstructed data
  

• iterations number of iterations to convergence
  

• R2 quality of reconstruction (coefficient of determination)

• rank_type was the rank fixed or variable?
  

• classification classification type (e.g., none, k-means, NMF, etc.)

References

Lee, D. D. & Seung, H. S. Learning the parts of objects by non-negative matrix factorization. Nature 401, 788-91 (1999).

Santuz, A., Ekizos, A., Janshen, L., Baltzopoulos, V. & Arampatzis, A. On the Methodological Implications of Extracting Muscle Synergies from Human Locomotion. Int. J. Neural Syst. 27, 1750007 (2017).

Févotte, C., Idier, J. Algorithms for Nonnegative Matrix Factorization with the Beta-Divergence Neural Computation 23, 9 (2011).

Examples

# Note that for bigger data sets one might want to run computation in parallel
# Load some data
data(FILT_EMG)
# Extract synergies (careful, rank is imposed here!)
SYNS <- lapply(FILT_EMG, synsNMF, fixed_syns = 4)