In ymatts/emgR: surface electromyography (sEMG) analyzer with R

emgR overview

• Analysis suites for surface electromyography (sEMG) data
• Basic process functionality
  • Bandpass filter
  • Qunatification
  • Normalization
• Advanced analysis
  • Waveform simililarity analysis
    • Coherence analysis (incl. wavelet)
    • Auto/cross correlation analysis
  • Similiarity based muscle network analysis

Install emgR package

# install.packages("devtools") if not installed
#detach("package:emgR",unload=T)
#remove.packages("emgR")
devtools::install_github("https://github.com/ymatts/emgR")
library(emgR)

Data acquisition

• From Santuz et al. (2018)
• 13 muscles sEMG (1000 Hz) records during gait cycle
• Time data will be required for constructing emgR object

data(emg)
head(emg,3)

Constructing object

• emgList is a method for constructing emgR object
• emgList object is simply a list data structure
• Input time (sec) and multi channel sEMG data
  • Time is in row and Muscles are in colums

dat = emg[emg$pid=="P0001",]
nCycle = 3
dat = dat[dat$cycle %in% (1:nCycle),]
tt = dat$Time # Time record
xx = dat[,-c(1:6)] # EMG record
obj = emgR::emgList(x = xx,t = tt)
obj

Envelope

• Bandpass filter using linear envelope
• Filtered objects will be overwride in emgR

obj = emgR::envelope(obj,order = 4,filtFreq = c(5,100))
obj

Normalize sEMG signals

• Several normalizing methods will be implemted
  • Default setting use simple Maximum Voluntary Contruct (MVC)

obj = emgR::normalize(obj)
obj

envelope

matplot(obj$filter$emg,type="l")

Normalized time (down sampling)

obj = emgR::downSample(obj,nn = 100)
obj

Envelope with normalized time

matplot(obj$filter$emg,type="l",xlab="normalized time",ylab="mV")

Muscle synergy (2D-array)

• Derive muscle synergy based on Non-negative matrix factorzation (NMF) algorithm
• This part is build on NMF package
• muscleSynergy2D.rankEst explore best ranks

obj = emgR::muscleSynergy2D.rankEst(obj)
plot(obj$synergy$estimRank)

Muscle synergy (2D-array)

• Perform NMF based on best fit rank
• Show synergy basis

obj = emgR::muscleSynergy2D(obj,rank = 4)
syns = 1:obj$synergy$rank
matplot(obj$synergy$W,type="l",main="Synergy basis (W)",ylab="mV",xlab="normalized time")
legend("topright",legend = paste("Synergy",syns),col=syns,lty = syns)

Muscle synergy (2D-array)

• Show synergy weight

pheatmap(obj$synergy$H,labels_row = paste("Synergy",syns))

ymatts/emgR documentation built on March 7, 2020, 12:52 a.m.