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Event-Related Spectral Perturbation (ERSP)

Source: R/eeg-timefreq.R
eegERSP.Rd

Computes Event-Related Spectral Perturbation for epoched (3D) EEG data. ERSP quantifies event-related changes in spectral power relative to a baseline period, expressed in decibels (dB). Uses the Morlet wavelet transform to compute time-frequency decomposition for each epoch, then averages power across epochs and normalizes to baseline.

Usage

eegERSP(
  x,
  baseline = c(1, 50),
  frequencies = NULL,
  n_cycles = 7,
  assay_name = NULL,
  output_assay = "ersp_data"
)

Arguments

x

A PhysioExperiment object with epoched (3D) EEG data (time x channels x epochs).

baseline

Numeric vector of length 2 specifying the baseline time window in sample indices (e.g., c(1, 50) for the first 50 samples). Default is c(1, 50).

frequencies

Numeric vector of frequencies in Hz to analyze. If NULL, defaults to seq(1, 50, by = 1).

n_cycles

Number of cycles for the Morlet wavelet (default: 7).

assay_name

Name of the input assay. If NULL, the default assay is used.

output_assay

Name the result is stored under. Despite the argument's name this is a key in metadata, NOT an assay: after the call assay(x, "the value given") does not exist while metadata(x)$the value given holds the result (default: "the value given"). (default: "ersp").

Value

Modified PhysioExperiment with:

  • 3D ERSP array (time x frequencies x channels) in dB in output_assay

  • Baseline info and frequency vector in metadata(x)$ersp, a list containing frequencies (numeric vector), baseline (numeric vector), n_cycles (integer), and n_epochs (integer)

References

Tallon-Baudry, C., et al. (1997). Oscillatory gamma-band activity during conscious perception. Trends in Cognitive Sciences, 3(4), 151-162.

Makeig, S. (1993). Auditory event-related dynamics of the EEG spectrum and effects of exposure to tones. Electroencephalography and Clinical Neurophysiology, 86(4), 283-293.

See also

eegITC(), eegMorletWavelet(), eegPlotSpectrogram()

Examples

pe <- make_eeg_erp(n_epochs = 20, n_channels = 2, sr = 250)
pe_ersp <- eegERSP(pe, baseline = c(1, 50), frequencies = seq(5, 30, by = 5))
# results are stored in metadata(x)$ersp
str(S4Vectors::metadata(pe_ersp)$ersp, max.level = 1)
#> List of 4
#>  $ frequencies: num [1:6] 5 10 15 20 25 30
#>  $ baseline   : num [1:2] 1 50
#>  $ n_cycles   : num 7
#>  $ n_epochs   : int 20