Analysis functions
For example, one can measure the full width at half maximum (FWHM) of the activation patterns or their centre of activity (CoA).
# Load a typical activation pattern of 30 cycles (from locomotion)
data("act_pattern")
# Reduce activation pattern to the first cycle
act_sub <- act_pattern$signal[1:which(act_pattern$time == max(act_pattern$time))[1]]
# Calculate FWHM of the first cycle
act_sub_FWHM <- FWHM(act_sub)
# Calculate CoA of the first cycle
act_sub_CoA <- CoA(act_sub)
# Half maximum (for the plots)
hm <- min(act_sub) + (max(act_sub) - min(act_sub)) / 2
hm_plot <- act_sub
hm_plot[which(hm_plot > hm)] <- hm
hm_plot[which(hm_plot < hm)] <- NA
# Plots
plot(act_sub, ty = "l", xlab = "Time", ylab = "Amplitude")
lines(hm_plot, lwd = 3, col = 2) # FWHM (horizontal, in red)
graphics::abline(v = act_sub_CoA, lwd = 3, col = 4) # CoA (vertical, in blue)
Or perhaps one might want to investigate the nonlinear behaviour of a long activation pattern.
act <- act_pattern$signal
# Calculate the local complexity or Higuchi's fractal dimension (HFD)
nonlin_HFD <- HFD(act)$Higuchi
# Calculate the global complexity or Hurst exponent (H)
nonlin_H <- Hurst(act, min_win = max(act_pattern$time))$Hurst
message("Higuchi's fractal dimension: ", round(nonlin_HFD, 3))
#> Higuchi's fractal dimension: 1.047
message("Hurst exponent: ", round(nonlin_H, 3))
#> Hurst exponent: 0.338