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Compute EEG band power per epoch

Source: R/compute_band_power.R
compute_band_power.Rd

Estimates power spectral density (PSD) using Welch's method and integrates power within standard EEG frequency bands for each epoch and channel.

Usage

compute_band_power(
  psg,
  channels = NULL,
  bands = list(delta = c(0.5, 4), theta = c(4, 8), alpha = c(8, 12), sigma = c(12, 16),
    beta = c(16, 30), gamma = c(30, 45)),
  relative = FALSE,
  win_sec = 4,
  noverlap = 200L,
  nfft = 1024L
)

Arguments

psg

An mrpheus_psg object from prepare_psg().

channels

Character vector. Channel labels to include. If NULL (default), all non-bad EEG channels are used.

bands

Named list of length-2 numeric vectors defining frequency bands in Hz. Default matches YASA's bandpower():

list(delta = c(0.5, 4), theta = c(4, 8), alpha = c(8, 12),
     sigma = c(12, 16), beta = c(16, 30), gamma = c(30, 45))

relative

Logical. If TRUE, each band power is divided by the sum of all band powers (dimensionless). Matches YASA relative = True. Default FALSE.

win_sec

Numeric. Welch window length in seconds. Default 4.

noverlap

Integer. Number of overlapping samples between Welch windows. Default 200. Must be less than win_sec * sample_rate.

nfft

Integer. FFT length. Default 1024. If smaller than the window length in samples it is automatically raised to the next power of 2.

Value

A tibble with columns epoch, channel, one column per named band, and total_power (sum of all band powers, in V^2/Hz units before any relative scaling).

Details

The Welch implementation matches scipy.signal.welch with YASA's default parameters (win_sec = 4, noverlap = 200, nfft = 1024, Hann window, constant detrend, mean averaging). Relative power matches YASA's relative = True behaviour: each band is divided by the sum of all band powers (not the total PSD integral).

Examples

if (FALSE) { # \dontrun{
bp <- compute_band_power(psg)
bp <- compute_band_power(psg, channels = "EEG Fpz-Cz", relative = TRUE)

# Custom bands
bp <- compute_band_power(
  psg,
  bands = list(slow = c(0.5, 1), delta = c(1, 4), theta = c(4, 8))
)
} # }