Aperiodic (1/f) spectral parameterization of EEG

Separates each channel's power spectrum into an aperiodic (1/f) component and periodic (oscillatory) peaks, following the specparam / FOOOF model (Donoghue et al. 2020). The aperiodic exponent is a widely used index of the excitation/inhibition balance and cortical state. Delegates the fit to PhysioAnalysis::specparam().

Usage

eegAperiodic(
  pe,
  freq_range = c(1, 45),
  aperiodic_mode = c("fixed", "knee"),
  max_n_peaks = 6L,
  peak_width_limits = c(1, 12),
  min_peak_height = 0.05,
  peak_threshold = 2,
  assay_name = NULL
)

Arguments

pe: A PhysioExperiment.
freq_range: Frequency range to fit, in Hz (default c(1, 45)).
aperiodic_mode: "fixed" (offset + exponent) or "knee" (offset + knee + exponent), the latter for spectra with a bend in log-log space.
max_n_peaks: Maximum number of oscillatory peaks per channel (default 6).
peak_width_limits: Min/max peak width in Hz (default c(1, 12)).
min_peak_height: Minimum peak height above the aperiodic fit (default 0.05).
peak_threshold: Peak detection threshold in SD of the flattened spectrum (default 2).
assay_name: Assay to use (default: the object's default assay).

Value

An eeg_aperiodic object: a list with aperiodic (per-channel data frame: channel, exponent, offset, optionally knee, r_squared, error), peaks (per-channel CF/PW/BW), exponent (a named per-channel vector, e.g. for eegPlotTopomap()), and the underlying specparam_result.

References

Donoghue et al. 2020, Nat Neurosci (specparam / FOOOF).

Examples

if (FALSE) { # \dontrun{
pe <- make_eeg(n_time = 2500, n_channels = 8, sr = 250)
ap <- eegAperiodic(pe, freq_range = c(2, 40))
ap$aperiodic          # per-channel exponent / offset
} # }