# Plots Power Spectral Density of EEG Data Uses a fast discrete Fourier transform (`eegfft`) to estimate the power spectral density of EEG data, and plots the power esimate using the `plot` (single channel) or `imagebar` (multi-channel) function. ```r eegpsd(x, Fs, lower, upper, units = "dB", xlab = NULL, ylab = NULL, zlab = NULL, ...) ``` ## Arguments - `x`: Vector or matrix (time by channel) of EEG data with `n` time points. - `Fs`: Sampling rate of `x` in Hz. - `lower`: Lower band in Hz. Smallest frequency to keep. - `upper`: Upper band in Hz. Largest frequency to keep. - `units`: Units for plot. Options include "dB" for decibals (default), "mV" for microvolts, and "mV^2" for squared microvolts. Note dB = 10*log10(mV^2). - `xlab`: x-axis label for the plot/image. - `ylab`: y-axis label for the plot/image. - `zlab`: z-axis label for the plot/image. - `...`: Optional inputs for the `plot` or `imagebar` function. ## Returns Produces a plot (single channel) or image (multi-channel). ## References Cooley, James W., and Tukey, John W. (1965) An algorithm for the machine calculation of complex Fourier series, Math. Comput. 19(90), 297-301. Singleton, R. C. (1979) Mixed Radix Fast Fourier Transforms, in Programs for Digital Signal Processing, IEEE Digital Signal Processing Committee eds. IEEE Press. ## Author(s) Nathaniel E. Helwig ## Examples ```r ########## EXAMPLE ########## # create data generating signals n <- 1000 # 1000 Hz signal s <- 2 # 2 seconds of data t <- seq(0, s, length.out = s * n) # time vector s1 <- sin(2*pi*t) # 1 Hz sinusoid s5 <- sin(2*pi*t*5) # 5 Hz sinusoid s10 <- sin(2*pi*t*10) # 10 Hz sinusoid s20 <- sin(2*pi*t*20) # 20 Hz sinusoid # create data set.seed(1) # set random seed e <- rnorm(s * n, sd = 0.25) # Gaussian error mu <- s1 + s5 + s10 + s20 # 1 + 5 + 10 + 20 Hz mean y <- mu + e # data = mean + error # plot psd (single channel) eegpsd(y, Fs = n, upper = 30, t = "b") # plot psd (multi-channel) ym <- cbind(s1, s5, s10, s20) eegpsd(ym, Fs = n, upper = 30, units = "mV") ```

eegpsd function