EEG complexity and nonlinear-dynamics measures

Computes per-channel entropy, fractal-dimension and detrended-fluctuation complexity measures — the nonlinear family that complements the spectral, connectivity and source tools. For epoched (3-D) data each measure is computed per epoch and averaged across epochs.

Usage

eegComplexity(
  pe,
  measures = c("sample_entropy", "permutation_entropy", "lempel_ziv", "higuchi_fd",
    "dfa", "hjorth_mobility", "spectral_entropy"),
  assay_name = NULL,
  m = 2L,
  r = 0.2,
  tau = 1L,
  perm_order = 3L,
  mse_scales = 1:8,
  max_samples = 4000L
)

Arguments

pe: A PhysioExperiment.
measures: Character vector of measures to compute (see Details).
assay_name: Assay to use (default: the object's default assay).
m: Embedding dimension for the entropy measures (default 2; permutation entropy uses perm_order).
r: Tolerance as a fraction of each channel's SD for sample/approximate/ multiscale entropy (default 0.2).
tau: Time delay for permutation entropy (default 1).
perm_order: Embedding order for permutation entropy (default 3).
mse_scales: Scales for multiscale entropy (default 1:8).
max_samples: Cap on samples per channel/epoch for the O(N^2) entropy measures; longer series are truncated with a warning (default 4000; NULL disables the cap).

Value

A data frame with one row per channel and one column per requested measure (plus a channel column). The multiscale-entropy per-scale curves are attached as attr(, "mse_scales") / attr(, "mse_curve").

Details

Available measures: "sample_entropy" (Richman–Moorman), "approximate_entropy" (Pincus), "permutation_entropy" (Bandt–Pompe), "multiscale_entropy" (Costa; returns the scale-averaged SampEn as a summary plus the per-scale curve in the attribute), "lempel_ziv" (Kaspar–Schuster), "higuchi_fd", "katz_fd", "dfa" (scaling exponent alpha), "hurst" (R/S), "hjorth_mobility", "hjorth_complexity", "spectral_entropy".

References

Richman & Moorman 2000; Bandt & Pompe 2002; Costa 2002; Higuchi 1988; Katz 1988; Peng 1994; Hjorth 1970.

Examples

pe <- make_eeg(n_time = 512, n_channels = 4, sr = 128)
cx <- eegComplexity(pe, measures = c("permutation_entropy", "hjorth_mobility",
                                     "spectral_entropy"))
cx
#>   channel permutation_entropy hjorth_mobility spectral_entropy
#> 1     Fp1           0.7578106       0.2321886        0.2514634
#> 2     Fp2           0.7763133       0.1718243        0.2659641
#> 3      F7           0.9175220       0.4730310        0.5049232
#> 4      F3           0.8686396       0.2233840        0.2696785