synsNMF {musclesyneRgies}

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

V

EMG data frame to be reconstructed, usually filtered and time-normalised

R2_target

Threshold to stop iterations for a certain factorisation rank

runs

Number of repetitions for each rank to avoid local minima

max_iter

Maximum number of iterations allowed for each rank

last_iter

How many of the last iterations should be checked before stopping?

MSE_min

Threshold on the mean squared error to choose the factorisation rank or minimum number of synergies

fixed_syns

To impose the factorisation rank or number of synergies

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)