Draws EEG Cap with Selected Electrodes

Description

Creates two- or three-dimensional plot of electroencephalography (EEG) cap with user-input electrodes. Three-dimensional plots are created using the eegcoord data and the plot3d function (from rgl package). Currently supports 84 scalp electrodes, and plots according to the international 10-10 system. Includes customization options (e.g., each electrode can have a unique plotting color, size, label color, etc.).

Usage

eegcap(electrodes = "10-10", type = c("2d", "3d"),
       plotlabels = TRUE, plotaxes = FALSE, main = "",
       xyzlab = NULL, cex.point = NULL, col.point = NULL,
       col.border = NULL, cex.label = NULL, col.label = NULL, 
       nose = TRUE, ears = TRUE, head = TRUE, 
       col.head = "AntiqueWhite", index = FALSE, 
       plt = c(0.03,0.97,0.03,0.97), ...)

Arguments

Value

Produces plot of EEG cap and possibly returns cap row indices.

Note

Currently supports 84 scalp electrodes (plus ears and nose): A1 A2 AF1 AF2 AF3 AF4 AF5 AF6 AF7 AF8 AFZ C1 C2 C3 C4 C5 C6 CP1 CP2 CP3 CP4 CP5 CP6 CPZ CZ F1 F2 F3 F4 F5 F6 F7 F8 F9 F10 FC1 FC2 FC3 FC4 FC5 FC6 FCZ FP1 FP2 FPZ FT7 FT8 FT9 FT10 FZ I1 I2 IZ NZ O1 O2 OZ P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 POZ PZ T7 T8 T9 T10 TP7 TP8 TP9 TP10

See eegcoord for the coordinates used to create plot. Setting index=TRUE returns the row indices of eegcoord that were used to plot the cap.

To save three-dimensional plots, use the rgl.postscript function (from rgl package).

Author(s)

Nathaniel E. Helwig <helwig@umn.edu>

References

Murdoch, D., and Adler, D. (2025). rgl: 3D Visualization Using OpenGL. doi:10.32614/CRAN.package.rgl, R package version 1.3.18, http://CRAN.R-project.org/package=rgl

Oostenveld, R., and Praamstra, P. (2001). The Five percent electrode system for high-resolution EEG and ERP measurements. Clinical Neurophysiology, 112, 713-719. doi:10.1016/S1388-2457(00)00527-7

Examples

##########   EXAMPLE 1   ##########

# plot 10-10 system (default):

# plot full cap 2d (default options)
eegcap()

# plot full cap 2d (different color for ears and nose)
data(eegcoord)
mycols <- rep("white",87)
enames <- rownames(eegcoord)
mycols[enames=="A1"] <- "green"
mycols[enames=="A2"] <- "light blue"
mycols[enames=="NZ"] <- "pink"
eegcap(col.point = mycols)



##########   EXAMPLE 2   ##########

# plot 10-20 system:

# plot 2d cap with labels
eegcap("10-20")

# plot 2d cap without labels
eegcap("10-20", plotlabels = FALSE)



##########   EXAMPLE 3   ##########

# plot custom subset of electrodes
myelectrodes <- c("FP1","FP2","FPZ","F7","F3","FZ",
                  "F4","F8","T7","C3","CZ","C4","T8",
                  "P7","P3","PZ","P4","P8","O1","O2")
eegcap(myelectrodes)

Draws 2D EEG Cap

Description

Creates two-dimensional plot of electroencephalography (EEG) cap with user-input electrodes. Currently supports 84 scalp electrodes, and plots according to the international 10-10 system. Includes customization options (e.g., each electrode can have a unique plotting color, size, label color, etc.).

Usage

eegcap2d(electrodes = "10-10", axes = FALSE, asp = 1, 
         cex.point = 2.75, col.point = "green", pch.point = 19,
         cex.border = 2.75, col.border = "black", pch.border = 21,
         cex.label = 0.5, col.label = "black", 
         head = TRUE, nose = TRUE, ears = TRUE, 
         main = "", xlab = "", ylab = "", 
         xlim = c(-13.7, 13.7), ylim = c(-13.7, 13.7), ...)

Arguments

Details

Currently supports 84 scalp electrodes (plus ears and nose): A1 A2 AF1 AF2 AF3 AF4 AF5 AF6 AF7 AF8 AFZ C1 C2 C3 C4 C5 C6 CP1 CP2 CP3 CP4 CP5 CP6 CPZ CZ F1 F2 F3 F4 F5 F6 F7 F8 F9 F10 FC1 FC2 FC3 FC4 FC5 FC6 FCZ FP1 FP2 FPZ FT7 FT8 FT9 FT10 FZ I1 I2 IZ NZ O1 O2 OZ P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 POZ PZ T7 T8 T9 T10 TP7 TP8 TP9 TP10

See eegcoord for the coordinates used to create plot.

Value

Produces plot of EEG cap.

Note

Unlike the eegcap function, this function does not use par$plt for the figure positioning.

Author(s)

Nathaniel E. Helwig <helwig@umn.edu>

References

Oostenveld, R., and Praamstra, P. (2001). The Five percent electrode system for high-resolution EEG and ERP measurements. Clinical Neurophysiology, 112, 713-719. doi:10.1016/S1388-2457(00)00527-7

See Also

See eegcap for a similar implementation, which also supports 3d EEG cap plotting.

Examples

##########   EXAMPLE 1   ##########

# plot 10-10 system (default):

# plot full cap (default options)
eegcap2d()

# plot full cap (different color for ears and nose)
data(eegcoord)
mycols <- rep(NA, 87)
enames <- rownames(eegcoord)
mycols[enames=="A1"] <- "green"
mycols[enames=="A2"] <- "light blue"
mycols[enames=="NZ"] <- "pink"
eegcap2d(col.point = mycols)



##########   EXAMPLE 2   ##########

# plot 10-20 system:

# plot cap with labels
eegcap2d("10-20")

# plot cap without labels
eegcap2d("10-20", cex.label = -1)



##########   EXAMPLE 3   ##########

# plot custom subset of electrodes
myelectrodes <- c("FP1","FP2","FPZ","F7","F3","FZ",
                  "F4","F8","T7","C3","CZ","C4","T8",
                  "P7","P3","PZ","P4","P8","O1","O2")
eegcap2d(myelectrodes)

Draws Dense EEG Cap with Selected Electrodes

Description

Creates two- or three-dimensional plot of dense electroencephalography (EEG) cap that spans user-input electrodes. Three-dimensional plots are created using the eegdense data and the plot3d function (from rgl package). Currently supports 933 scalp electrodes. Includes customization options (e.g., each electrode can have a unique plotting color, size, label color, etc.).

Usage

eegcapdense(electrodes = "10-10", type = c("2d", "3d"),
            plotlabels = TRUE, plotaxes = FALSE, main = "",
            xyzlab = NULL, cex.point = NULL, col.point = NULL,
            cex.label = NULL, col.label = NULL, nose = TRUE,
            ears = TRUE, head = TRUE, col.head = "AntiqueWhite",
            index = FALSE, zconst = 0.5, plt = c(0.03,0.97,0.03,0.97), ...)

Arguments

Value

Produces plot of EEG cap and possibly returns cap row indices.

Note

Currently supports 84 scalp electrodes (plus ears and nose): A1 A2 AF1 AF2 AF3 AF4 AF5 AF6 AF7 AF8 AFZ C1 C2 C3 C4 C5 C6 CP1 CP2 CP3 CP4 CP5 CP6 CPZ CZ F1 F2 F3 F4 F5 F6 F7 F8 F9 F10 FC1 FC2 FC3 FC4 FC5 FC6 FCZ FP1 FP2 FPZ FT7 FT8 FT9 FT10 FZ I1 I2 IZ NZ O1 O2 OZ P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 POZ PZ T7 T8 T9 T10 TP7 TP8 TP9 TP10

See eegdense for the coordinates used to create plot. Setting index=TRUE returns the row indices of eegdense that were used to plot the cap. Only returns row indices with z-coordinates >= (zmin-zconst), where zmin is minimum z-coordinate of input electrodes.

To save three-dimensional plots, use the rgl.postscript function (from rgl package).

Author(s)

Nathaniel E. Helwig <helwig@umn.edu>

References

Murdoch, D., and Adler, D. (2025). rgl: 3D Visualization Using OpenGL. doi:10.32614/CRAN.package.rgl, R package version 1.3.18, http://CRAN.R-project.org/package=rgl

Oostenveld, R., and Praamstra, P. (2001). The Five percent electrode system for high-resolution EEG and ERP measurements. Clinical Neurophysiology, 112, 713-719. doi:10.1016/S1388-2457(00)00527-7

Examples

##########   EXAMPLE 1   ##########

# plot 10-10 system (default):
eegcapdense()



##########   EXAMPLE 2   ##########

# plot 10-20 system:
eegcapdense("10-20", plotlabels = FALSE)



##########   EXAMPLE 3   ##########

# plot custom subset of electrodes
myelectrodes <- c("FP1","FP2","FPZ","F7","F3","FZ",
                  "F4","F8","T7","C3","CZ","C4","T8",
                  "P7","P3","PZ","P4","P8","O1","O2")
eegcapdense(myelectrodes)

EEG Cap Coordinates

Description

Three-dimensional electroencephalography (EEG) electrode coordinates (measured in cm), and corresponding projection onto two-dimensional xy plane. Contains 84 scalp electrodes, as well as nose and ears.

Usage

data(eegcoord)

Format

A data frame with 87 observations and the following 5 variables:

x

x-coordinate of 3d cap (numeric).

y

y-coordinate of 3d cap (numeric).

z

z-coordinate of 3d cap (numeric).

xproj

Projected x-coordinate of 2d cap (numeric).

yproj

Projected y-coordinate of 2d cap (numeric).

Electrode channel name labels can be obtained using rownames(eegcoord).

Author(s)

Nathaniel E. Helwig <helwig@umn.edu>

Source

Created by Nathaniel E. Helwig (2014) using:

Murdoch, D., and Adler, D. (2025). rgl: 3D Visualization Using OpenGL. doi:10.32614/CRAN.package.rgl, R package version 1.3.18, http://CRAN.R-project.org/package=rgl

Oostenveld, R., and Praamstra, P. (2001). The Five percent electrode system for high-resolution EEG and ERP measurements. Clinical Neurophysiology, 112, 713-719. doi:10.1016/S1388-2457(00)00527-7

Schlager S (2017). Morpho and Rvcg - Shape Analysis in R. In Zheng G, Li S, Szekely G (eds.), Statistical Shape and Deformation Analysis, 217-256. Academic Press. ISBN 9780128104934.

Examples

##########   EXAMPLE   ##########

data(eegcoord)
enames <- rownames(eegcoord)
# plot3d(eegcoord[,1],eegcoord[,2],eegcoord[,3],size=10,col="green")
# text3d(eegcoord[,1],eegcoord[,2],eegcoord[,3],texts=enames,col="blue")
plot(eegcoord[,4],eegcoord[,5],cex=2,col="green",pch=19)
text(eegcoord[,4],eegcoord[,5],labels=enames,col="blue")

Dense EEG Cap Coordinates

Description

Dense (hypothetical) three-dimensional electroencephalography (EEG) electrode coordinates, and corresponding projection onto two-dimensional plane. Dense cap spans the 84 scalp electrodes defined in eegcoord.

Usage

data(eegdense)

Format

A data frame with 977 observations and the following 5 variables:

x

x-coordinate of 3d cap (numeric).

y

y-coordinate of 3d cap (numeric).

z

z-coordinate of 3d cap (numeric).

xproj

Projected x-coordinate of 2d cap (numeric).

yproj

Projected y-coordinate of 2d cap (numeric).

Author(s)

Nathaniel E. Helwig <helwig@umn.edu>

Source

Created by Nathaniel E. Helwig (2014) using:

Murdoch, D., and Adler, D. (2025). rgl: 3D Visualization Using OpenGL. doi:10.32614/CRAN.package.rgl, R package version 1.3.18, http://CRAN.R-project.org/package=rgl

Oostenveld, R., and Praamstra, P. (2001). The Five percent electrode system for high-resolution EEG and ERP measurements. Clinical Neurophysiology, 112, 713-719. doi:10.1016/S1388-2457(00)00527-7

Schlager S (2017). Morpho and Rvcg - Shape Analysis in R. In Zheng G, Li S, Szekely G (eds.), Statistical Shape and Deformation Analysis, 217-256. Academic Press. ISBN 9780128104934.

Examples

##########   EXAMPLE   ##########

data(eegdense)
# plot3d(eegdense[,1],eegdense[,2],eegdense[,3],size=10,col="green")
plot(eegdense[,4],eegdense[,5],cex=1,col="green",pch=19)

Fast Fourier Transform of EEG Data

Description

Finds the strength (amplitude) and phase shift of the input signal(s) at a particular range of frequencies via a Discrete Fast Fourier Transform (FFT). Can input single or multi-channel data.

Usage

eegfft(x, Fs, lower, upper)

Arguments

Details

The fft function (or mvfft function) is used to implement the FFT (or multivatiate FFT). Given the FFT, the strength of the signal is the modulus (Mod), and the phase.shift is the angle (Arg).

Value

If x is a vector, returns a data frame with variables:

If x is a matrix with J channels, returns a list with elements:

Note

The strength of the signal has the same unit as the input (typically microvolts), and the phase shift is measured in radians (range -pi to pi).

Author(s)

Nathaniel E. Helwig <helwig@umn.edu>

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. doi:10.7551/mitpress/5222.003.0014

Singleton, R. C. (1979) Mixed Radix Fast Fourier Transforms, in Programs for Digital Signal Processing, IEEE Digital Signal Processing Committee eds. IEEE Press.

Examples

##########   EXAMPLE   ##########

### Data Generation ###

# parameters for signal
Fs <- 1000                             # 1000 Hz signal
s <- 3                                 # 3 seconds of data
t <- seq(0, s - 1/Fs, by = 1/Fs)       # time sequence
n <- length(t)                         # number of data points
freqs <- c(1, 5, 10, 20)               # frequencies
amp <- c(2, 1.5, 3, 1.75)              # strengths (amplitudes)
phs <- c(0, pi/6, pi/4, pi/2)          # phase shifts

# create data generating signals
mu <- rep(0, n)
for(j in 1:length(freqs)){
  mu <- mu + amp[j] * sin(2*pi*t*freqs[j] + phs[j])
}
set.seed(1)                           # set random seed
e <- rnorm(n)                         # Gaussian error
y <- mu + e                           # data = mean + error


### FFT of Noise-Free Data ###

# fft of noise-free data
ef <- eegfft(mu, Fs = Fs, upper = 40)
head(ef)
ef[ef$strength > 0.25,]

# plot frequency strength
par(mfrow = c(1,2))
plot(x = ef$frequency, y = ef$strength, t = "b",
     xlab = "Frequency (Hz)", 
     ylab = expression("Strength (" * mu * "V)"),
     main = "FFT of Noise-Free Data")

# compare to data generating parameters
cbind(amp, ef$strength[ef$strength > 0.25])
cbind(phs - pi/2, ef$phase[ef$strength > 0.25])


### FFT of Noisy Data ###

# fft of noisy data
ef <- eegfft(y, Fs = Fs, upper = 40)
head(ef)
ef[ef$strength > 0.25,]

# plot frequency strength
plot(x = ef$frequency, y = ef$strength, t = "b",
     xlab = "Frequency (Hz)", 
     ylab = expression("Strength (" * mu * "V)"),
     main = "FFT of Noisy Data")

# compare to data generating parameters
cbind(amp, ef$strength[ef$strength > 0.25])
cbind(phs - pi/2, ef$phase[ef$strength > 0.25])

Filters EEG Data

Description

Low-pass, high-pass, or band-pass filter EEG data using either a Butterworth filter (default) or a finite impulse response (FIR) filter.

Usage

eegfilter(x, Fs, lower, upper, method = "butter",
          order = 3L, forwardreverse = TRUE, 
          scale = FALSE, plot = FALSE)

Arguments

Details

For a low-pass filter, only enter the upper frequency to keep. For a high-pass filter, only enter the lower frequency to keep. For a band-pass filter, enter both the lower and upper frequency bounds.

Value

Filtered version of input data.

Author(s)

Nathaniel E. Helwig <helwig@umn.edu>

References

http://en.wikipedia.org/wiki/Butterworth_filter

http://en.wikipedia.org/wiki/Fir_filter

See Also

filter, filtfilt, butter, fir1

Examples

##########   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

# 4-th order Butterworth filter (2 to 15 Hz band-pass)
yf.but <- eegfilter(y, Fs = n, lower = 2, upper = 15, method = "butter", order = 4)

# 350-th order FIR filter (2 to 15 Hz band-pass)
yf.fir <- eegfilter(y, Fs = n, lower = 2, upper = 15, method = "fir1", order = 350)

# check quality of results
yftrue <- s5 + s10                    # true (filtered) mean signal
mean((yf.but - yftrue)^2)             # mse between yf.but and yftrue
mean((yf.fir - yftrue)^2)             # mse between yf.fir and yftrue

# plot true and estimated filtered signals
plot(t, yftrue, type = "l", lty = 1, lwd = 2, ylim = c(-3, 3))
lines(t, yf.but, col = "blue", lty = 2, lwd = 2)
lines(t, yf.fir, col = "red", lty = 3, lwd = 2)
legend("topright", legend = c("Truth", "Butterworth", "FIR"), 
       lty = 1:3, lwd = 2, col = c("black", "blue", "red"), bty = "n")

# power spectral density before and after filtering (dB)
par(mfrow=c(1,3), mar = c(5, 4.5, 4, 2) + 0.1)
eegpsd(y, Fs = n, upper = 50, t = "b",
       main = "Before Filtering", lwd = 2)
rect(2, -63, 15, 1, col = rgb(0.5,0.5,0.5,1/4))
legend("topright", legend = "2-15 Hz Filter", 
       fill = rgb(0.5,0.5,0.5,1/4), bty = "n")
eegpsd(yf.but, Fs = n, upper = 50, t = "b",
       main = "After Butterworth Filter", lwd = 2)
eegpsd(yf.fir, Fs = n, upper = 50, t = "b",
       main = "After FIR Filter", lwd = 2)
       
# power spectral density before and after filtering (mv^2)
par(mfrow=c(1,3), mar = c(5, 4.5, 4, 2) + 0.1)
eegpsd(y, Fs = n, upper = 50, unit = "mV^2", t = "b",
       main = "Before Filtering", lwd = 2)
rect(2, 0, 15, 1.05, col = rgb(0.5,0.5,0.5,1/4))
legend("topright", legend = "2-15 Hz Filter", 
       fill = rgb(0.5,0.5,0.5,1/4), bty = "n")
eegpsd(yf.but, Fs = n, upper = 50, unit = "mV^2", t = "b",
       main = "After Butterworth Filter", lwd = 2)
eegpsd(yf.fir, Fs = n, upper = 50, unit = "mV^2", t = "b",
       main = "After FIR Filter", lwd = 2)

Dummy Head for 3d EEG Plots

Description

Contains mesh3d object of dummy head, which is used in the plotting functions eegcap and eegspace. This is a transformed (translated, rotated, and rescaled) vesion of the dummyhead object from the Rvcg package.

Usage

data(eeghead)

Format

mesh3d object

Author(s)

Nathaniel E. Helwig <helwig@umn.edu>

Source

Created by Nathaniel E. Helwig (2014) using:

Murdoch, D., and Adler, D. (2025). rgl: 3D Visualization Using OpenGL. doi:10.32614/CRAN.package.rgl, R package version 1.3.18, http://CRAN.R-project.org/package=rgl

Schlager S (2017). Morpho and Rvcg - Shape Analysis in R. In Zheng G, Li S, Szekely G (eds.), Statistical Shape and Deformation Analysis, 217-256. Academic Press. ISBN 9780128104934.

Examples

##########   EXAMPLE   ##########

# data(eeghead)
# shade3d(eeghead)
# eeghead$material$color <- rep("black",length(eeghead$material$color))
# wire3d(eeghead)

Independent Component Analysis of EEG Data

Description

Computes temporal (default) or spatial ICA decomposition of EEG data. Can use Infomax (default), FastICA, or JADE algorithm. ICA computations are conducted via icaimax, icafast, or icajade from the ica package.

Usage

eegica(X, nc, center = TRUE, maxit = 100, tol = 1e-6,
       Rmat = diag(nc), type = c("time", "space"),
       method = c("imax", "fast", "jade"), ...)

Arguments

Details

ICA Model The ICA model can be written as X = tcrossprod(S, M) + E, where columns of S contain the source signals, M is the mixing matrix, and columns of E contain the noise signals. Columns of X are assumed to have zero mean. The goal is to find the unmixing matrix W such that columns of S = tcrossprod(X, W) are independent as possible.

Whitening Without loss of generality, we can write M = P %*% R where P is a tall matrix and R is an orthogonal rotation matrix. Letting Q denote the pseudoinverse of P, we can whiten the data using Y = tcrossprod(X,Q). The goal is to find the orthongal rotation matrix R such that the source signal estimates S = Y %*% R are as independent as possible. Note that W = crossprod(R,Q).

Infomax The Infomax approach finds the orthogonal rotation matrix R that (approximately) maximizes the joint entropy of a nonlinear function of the estimated source signals. See Bell and Sejnowski (1995) and Helwig (in prep) for specifics of algorithms.

FastICA The FastICA algorithm finds the orthogonal rotation matrix R that (approximately) maximizes the negentropy of the estimated source signals. Negentropy is approximated using

J(s) = [E\{G(s)\}-E\{G(z)\} ]^2

where E denotes the expectation, G is the contrast function, and z is a standard normal variable. See Hyvarinen (1999) for specifics of fixed-point algorithm.

JADE The JADE approach finds the orthogonal rotation matrix R that (approximately) diagonalizes the cumulant array of the source signals. See Cardoso and Souloumiac (1993,1996) and Helwig and Hong (2013) for specifics of the JADE algorithm.

Value

Note

If type="time", the data matrix is transposed before calling ICA algorithm (i.e., X = t(X)), and the columns of the tranposed data matrix are centered.

Author(s)

Nathaniel E. Helwig <helwig@umn.edu>

References

Bell, A.J. & Sejnowski, T.J. (1995). An information-maximization approach to blind separation and blind deconvolution. Neural Computation, 7, 1129-1159. doi:10.1162/neco.1995.7.6.1129

Cardoso, J.F., & Souloumiac, A. (1993). Blind beamforming for non-Gaussian signals. IEE Proceedings-F, 140, 362-370. doi:10.1049/ip-f-2.1993.0054

Cardoso, J.F., & Souloumiac, A. (1996). Jacobi angles for simultaneous diagonalization. SIAM Journal on Matrix Analysis and Applications, 17, 161-164. doi:10.1137/S0895479893259546

Helwig, N.E. (2022). ica: Independent Component Analysis, doi:10.32614/CRAN.package.ica, R package version 1.0-3. http://CRAN.R-project.org/package=ica

Helwig, N.E. & Hong, S. (2013). A critique of Tensor Probabilistic Independent Component Analysis: Implications and recommendations for multi-subject fMRI data analysis. Journal of Neuroscience Methods, 213, 263-273. doi:10.1016/j.jneumeth.2012.12.009

Hyvarinen, A. (1999). Fast and robust fixed-point algorithms for independent component analysis. IEEE Transactions on Neural Networks, 10, 626-634. doi:10.1109/72.761722

Examples

##########   EXAMPLE   ##########

# get "c" subjects of "eegdata" data
data(eegdata)
idx <- which(eegdata$group=="c")
eegdata <- eegdata[idx,]

# get average data (across subjects)
eegmean <- tapply(eegdata$voltage,list(eegdata$channel,eegdata$time),mean)

# remove ears and nose
acnames <- rownames(eegmean)
idx <- c(which(acnames=="X"),which(acnames=="Y"),which(acnames=="nd"))
eegmean <- eegmean[-idx,]

# get spatial coordinates (for plotting)
data(eegcoord)
cidx <- match(rownames(eegmean),rownames(eegcoord))

# temporal ICA with 4 components
icatime <- eegica(eegmean,4)
icatime$vafs
# quartz()
# par(mfrow=c(4,2))
# tseq <- (0:255)*1000/255
# for(j in 1:4){
#   par(mar=c(5.1,4.6,4.1,2.1))
#   sptitle <- bquote("VAF:  "*.(round(icatime$vafs[j],4)))
#   eegtime(tseq,icatime$S[,j],main=bquote("Component  "*.(j)),cex.main=1.5)
#   eegspace(eegcoord[cidx,4:5],icatime$M[,j],main=sptitle)
# }

# spatial ICA with 4 components
icaspace <- eegica(eegmean,4,type="space")
icaspace$vafs
# quartz()
# par(mfrow=c(4,2))
# tseq <- (0:255)*1000/255
# for(j in 1:4){
#   par(mar=c(5.1,4.6,4.1,2.1))
#   sptitle <- bquote("VAF:  "*.(round(icaspace$vafs[j],4)))
#   eegtime(tseq,icaspace$M[,j],main=bquote("Component  "*.(j)),cex.main=1.5)
#   eegspace(eegcoord[cidx,4:5],icaspace$S[,j],main=sptitle)
# }

Internal Functions for eegkit Package

Description

Internal functions for eegkit package.

Details

These functions are not to be called by the user.

EEG Cap for Dense Coordinates

Description

Contains mesh3d object of eegdense, which is used in the plotting function eegspace.

Usage

data(eegmesh)

Format

mesh3d object

Author(s)

Nathaniel E. Helwig <helwig@umn.edu>

Source

Created by Nathaniel E. Helwig (2014) using:

Murdoch, D., and Adler, D. (2025). rgl: 3D Visualization Using OpenGL. doi:10.32614/CRAN.package.rgl, R package version 1.3.18, http://CRAN.R-project.org/package=rgl

Oostenveld, R., and Praamstra, P. (2001). The Five percent electrode system for high-resolution EEG and ERP measurements. Clinical Neurophysiology, 112, 713-719. doi:10.1016/S1388-2457(00)00527-7

Schlager S (2017). Morpho and Rvcg - Shape Analysis in R. In Zheng G, Li S, Szekely G (eds.), Statistical Shape and Deformation Analysis, 217-256. Academic Press. ISBN 9780128104934.

Examples

##########   EXAMPLE   ##########

# data(eegmesh)
# wire3d(eegmesh)
# eegmesh$material$color <- rep("red",length(eegmesh$material$color))
# shade3d(eegmesh)

Plots Power Spectral Density of EEG Data

Description

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.

Usage

eegpsd(x, Fs, lower, upper, units = "dB", 
       xlab = NULL, ylab = NULL, zlab = NULL, ...)

Arguments

Value

Produces a plot (single channel) or image (multi-channel).

Author(s)

Nathaniel E. Helwig <helwig@umn.edu>

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.

Examples

##########   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")

Change Sampling Rate of EEG Data

Description

Turn a signal of length N into a signal of length n via linear interpolation.

Usage

eegresample(x, n)

Arguments

Details

Data are resampled using the "Linear Length Normalization" approach described in Helwig et al. (2011). Let \mathbf{x} = (x_1, \ldots, x_N)' denote the input vector of length N, and define a vector \mathbf{t} = (t_1, \ldots, t_n) with entries

t_i = 1 + (i - 1) \delta

for i = 1, \ldots, n where \delta = (N - 1) / (n - 1). The resampled vector is calculated as

y_i = x_{\lfloor t_i \rfloor} + (x_{\lceil t_i \rceil} - x_{\lfloor t_i \rfloor}) ( t_i - \lfloor t_i \rfloor)

for i = 1, \ldots, n where \lfloor \cdot \rfloor and \lceil \cdot \rceil denote the floor and ceiling functions.

Value

Resampled version of input data with n time points.

Note

Typical usage is to down-sample (i.e., decrease the sampling rate of) a signal: n < N.

Author(s)

Nathaniel E. Helwig <helwig@umn.edu>

References

Helwig, N. E., Hong, S., Hsiao-Wecksler E. T., & Polk, J. D. (2011). Methods to temporally align gait cycle data. Journal of Biomechanics, 44(3), 561-566. doi:10.1016/j.jbiomech.2010.09.015

Examples

##########   EXAMPLE 1   ##########

# create vector with N = 200 time points
N <- 200
x <- sin(4 * pi * seq(0, 1, length.out = N))

# down-sample (i.e., decrease sampling rate) to n = 100
y <- eegresample(x, n = 100)
mean((y - sin(4 * pi * seq(0, 1, length.out = 100)))^2)

# up-sample (i.e., increase sampling rate) to n = 500
z <- eegresample(x, n = 500)
mean((z - sin(4 * pi * seq(0, 1, length.out = 500)))^2)

# plot results
par(mfrow = c(1,3))
plot(x, main = "Original (N = 200)")
plot(y, main = "Down-sampled (n = 100)")
plot(z, main = "Up-sampled (n = 500)")


##########   EXAMPLE 2   ##########

# create matrix with N = 500 time points and 2 columns
N <- 500
x <- cbind(sin(2 * pi * seq(0, 1, length.out = N)),
           sin(4 * pi * seq(0, 1, length.out = N)))
           
# down-sample (i.e., decrease sampling rate) to n = 250
y <- eegresample(x, n = 250)
ytrue <- cbind(sin(2 * pi * seq(0, 1, length.out = 250)),
               sin(4 * pi * seq(0, 1, length.out = 250)))
mean((y - ytrue)^2)

# up-sample (i.e., increase sampling rate) to n = 1000
z <- eegresample(x, n = 1000)
ztrue <- cbind(sin(2 * pi * seq(0, 1, length.out = 1000)),
               sin(4 * pi * seq(0, 1, length.out = 1000)))
mean((z - ztrue)^2)

# plot results
par(mfrow = c(1,3))
plot(x[,1], main = "Original (N = 500)", cex = 0.5)
points(x[,2], pch = 2, col = "blue", cex = 0.5)
plot(y[,1], main = "Down-sampled (n = 250)", cex = 0.5)
points(y[,2], pch = 2, col = "blue", cex = 0.5)
plot(z[,1], main = "Up-sampled (n = 1000)", cex = 0.5)
points(z[,2], pch = 2, col = "blue", cex = 0.5)

Simulate Event-Related Potential EEG Data

Description

Simulates event-related potential EEG data from hypothetical visual-stimulus ERP study. Data are simulated using a linear combination of five spatiotemporal component functions: P100, N100, P200, N200, and P300 components. User can control the coefficient (weight) given to each component, as well as the time shift (delay) of each component.

Usage

eegsim(channel, time, coefs = rep(1,5), tshift = rep(0,5))

Arguments

Value

Returns a vector of simulated EEG data corresponding to the input channel(s), time point(s), coefficients, and time shifts.

Note

Simulates data for 39 parietal and occipital electrodes: CP1 CP2 CP3 CP4 CP5 CP6 CPZ I1 I2 IZ O1 O2 OZ P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 POZ PZ TP7 TP8 TP9 TP10

Returns simulated value of 0 for other electrodes.

Author(s)

Nathaniel E. Helwig <helwig@umn.edu>

References

Created by Nathaniel E. Helwig (2014) using data from:

Begleiter, H. (1995). EEG Database [Dataset]. UCI Machine Learning Repository. doi:10.24432/C5TS3D

Examples

##########   EXAMPLE   ##########

### plot spatiotemporal component functions

# data(eegcoord)
# chnames <- rownames(eegcoord)
# tseq <- seq(0,1,length.out=200)

# quartz(width=18,height=6)
# layout(matrix(c(1,2,3,4,5,6,7,8,9,10,11,11), 2, 6, byrow = TRUE))

# eegspace(eegcoord[,4:5],p1s(chnames),cex.point=1,main=expression(psi[p1]),cex.main=2,vlim=c(-3,9))
# eegtime(tseq,p1t(tseq),ylim=c(-1,1),asp=1/2,main=expression(tau[p1]),cex.main=2,
#         xlab="Time After Stimulus (sec)")
# eegspace(eegcoord[,4:5],p2s(chnames),cex.point=1,main=expression(psi[p2]),cex.main=2,vlim=c(-3,9))
# eegtime(tseq,p2t(tseq),ylim=c(-1,1),asp=1/2,main=expression(tau[p2]),cex.main=2,
#         xlab="Time After Stimulus (sec)")
# eegspace(eegcoord[,4:5],p3s(chnames),cex.point=1,main=expression(psi[p3]),cex.main=2,vlim=c(-3,9))
# eegtime(tseq,p3t(tseq),ylim=c(-1,1),asp=1/2,main=expression(tau[p3]),cex.main=2,
#         xlab="Time After Stimulus (sec)")
# eegspace(eegcoord[,4:5],n1s(chnames),cex.point=1,main=expression(psi[n1]),cex.main=2,vlim=c(-3,9))
# eegtime(tseq,n1t(tseq),ylim=c(-1,1),asp=1/2,main=expression(tau[n1]),cex.main=2,
#         xlab="Time After Stimulus (sec)")
# eegspace(eegcoord[,4:5],n2s(chnames),cex.point=1,main=expression(psi[n2]),cex.main=2,vlim=c(-3,9))
# eegtime(tseq,n2t(tseq),ylim=c(-1,1),asp=1/2,main=expression(tau[n2]),cex.main=2,
#         xlab="Time After Stimulus (sec)")
# plot(seq(-10,10),seq(-10,10),type="n",axes=FALSE,xlab="",ylab="")
# text(0,8,labels=expression(omega[p1]*" = "*psi[p1]*tau[p1]),cex=2)
# text(0,4,labels=expression(omega[n1]*" = "*psi[n1]*tau[n1]),cex=2)
# text(0,0,labels=expression(omega[p2]*" = "*psi[p2]*tau[p2]),cex=2)
# text(0,-4,labels=expression(omega[n2]*" = "*psi[n2]*tau[n2]),cex=2)
# text(0,-8,labels=expression(omega[p3]*" = "*psi[p3]*tau[p3]),cex=2)



### plot simulated data at various time points

# quartz(width=15,height=3)
# tseq <- c(50,150,250,350,450)/1000
# par(mfrow=c(1,5))
# for(j in 1:5){
#   eegspace(eegcoord[,4:5],eegsim(chnames,rep(tseq[j],87)),vlim=c(-6.8,5.5),
#            main=paste(tseq[j]*1000," ms"),cex.main=2)
# }

Spatial and/or Temporal Smoothing of EEG Data

Description

Smooths single- or multi-channel electroencephalography (EEG) with respect to space and/or time. Uses the bigspline, bigtps, and bigssa functions (from bigsplines package) for smoothing.

Usage

eegsmooth(voltage, space = NULL, time = NULL, nknots = NULL,
          rparm = NULL, lambdas = NULL, skip.iter = TRUE,
          se.fit = FALSE, rseed = 1234)

Arguments

Value

For temporal smoothing only: an object of class "bigspline" (see bigspline).

For spatial smoothing only: an object of class "bigtps" (see bigtps).

For spatial-temporal smoothing: an object of class "bigssa" (see bigssa).

Note

For temporal smoothing only (i.e., space=NULL), the input rparm should be a positive scalar less than 1. Larger values produce faster (but less accurate) approximations. Default is 0.01, which I recommend for temporal smoothing; rparm=0.005 may be needed for particuarly rough signals, and rparm=0.02 could work for smoother signals.

For spatial smoothing only (i.e., time=NULL), the input rparm should be a positive scalar giving the rounding unit for the spatial coordinates. For example, rparm=0.1 rounds each coordinate to the nearest 0.1 (same as round(space,1)).

For spatial-temporal smoothing (i.e., both space and time are non-null), the input rparm should be a list of the form rparm=list(space=0.1,time=0.01), where the 0.1 and 0.01 can be replaced by your desired rounding parameters.

Setting rparm=NA will use the full data solution; this is more computationally expensive, and typically produces a solution very similar to using rparm=0.01 (see references).

Author(s)

Nathaniel E. Helwig <helwig@umn.edu>

References

Helwig, N. E. (2013). Fast and stable smoothing spline analysis of variance models for large samples with applications to electroencephalography data analysis. Unpublished doctoral dissertation. University of Illinois at Urbana-Champaign. https://www.ideals.illinois.edu/items/44427

Helwig, N.E. (2015). bigsplines: Smoothing Splines for Large Samples. doi:10.32614/CRAN.package.bigsplines, R package version 1.1-1, https://CRAN.R-project.org/package=bigsplines

Helwig, N. E. & Ma, P. (2015). Fast and stable multiple smoothing parameter selection in smoothing spline analysis of variance models with large samples. Journal of Computational and Graphical Statistics, 24(3), 715-732. doi:10.1080/10618600.2014.926819

Helwig, N. E. & Ma, P. (2016). Smoothing spline ANOVA for super large samples: Scalable computation via rounding parameters. Statistics and Its Interface, 9(4), 433-444. doi:10.4310/SII.2016.v9.n4.a3

Examples

##########   EXAMPLE 1: Temporal    ##########

# get "PZ" electrode of "c" subjects in "eegdata" data
data(eegdata)
idx <- which(eegdata$channel=="PZ" & eegdata$group=="c")
eegdata <- eegdata[idx,]

# temporal smoothing
eegmod <- eegsmooth(eegdata$voltage,time=eegdata$time)

# define data for prediction
time <- seq(min(eegdata$time),max(eegdata$time),length.out=100)
yhat <- predict(eegmod,newdata=time,se.fit=TRUE)

# plot results using eegtime
eegtime(time*1000/255,yhat$fit,voltageSE=yhat$se.fit,ylim=c(-4,4),main="Pz")



##########   EXAMPLE 2: Spatial   ##########

# get time point 65 (approx 250 ms) of "c" subjects in "eegdata" data
data(eegdata)
idx <- which(eegdata$time==65L & eegdata$group=="c")
eegdata <- eegdata[idx,]

# remove ears, nose, and reference (Cz)
idx <- c(which(eegdata$channel=="X"),which(eegdata$channel=="Y"),
         which(eegdata$channel=="nd"),which(eegdata$channel=="Cz"))
eegdata <- eegdata[-idx,]

# match to eeg coordinates
data(eegcoord)
cidx <- match(eegdata$channel,rownames(eegcoord))

# spatial smoothing
eegmod <- eegsmooth(eegdata$voltage,space=eegcoord[cidx,1:3])

# use dense cap for prediction
mycap <- levels(factor(eegdata$channel))
ix <- eegcapdense(mycap,type="2d",index=TRUE)
data(eegdense)
space <- eegdense[ix,1:3]
yhat <- predict(eegmod,newdata=space)

# plot results using eegspace
#eegspace(space,yhat)
eegspace(eegdense[ix,4:5],yhat)



##########   EXAMPLE 3: Spatial-Temporal (not run)   ##########

# # get "c" subjects of "eegdata" data
# data(eegdata)
# idx <- which(eegdata$group=="c")
# eegdata <- eegdata[idx,]

# # remove ears, nose, and reference (Cz)
# idx <- c(which(eegdata$channel=="X"),which(eegdata$channel=="Y"),
#          which(eegdata$channel=="nd"),which(eegdata$channel=="Cz"))
# eegdata <- eegdata[-idx,]

# # match to eeg coordinates
# data(eegcoord)
# cidx <- match(eegdata$channel,rownames(eegcoord))

# # spatial-temporal smoothing
# eegmod <- eegsmooth(eegdata$voltage,space=eegcoord[cidx,1:3],time=eegdata$time)

# # time main effect
# newdata <- list(time=seq(min(eegdata$time),max(eegdata$time),length.out=100))
# yhat <- predict(eegmod,newdata=newdata,se.fit=TRUE,include="time")
# eegtime(newdata$time,yhat$fit,voltageSE=yhat$se.fit,ylim=c(-2,4),main="Time Main Effect")

# # space main effect
# mycap <- levels(factor(eegdata$channel))
# ix <- eegcapdense(mycap,type="2d",index=TRUE)
# data(eegdense)
# newdata <- list(space=eegdense[ix,1:3])
# yhat <- predict(eegmod,newdata=newdata,include="space")
# eegspace(newdata$space,yhat)

# # interaction effect (spatial map at time point 65)
# newdata <- list(space=eegdense[ix,1:3],time=rep(65,nrow(eegdense[ix,])))
# yhat <- predict(eegmod,newdata=newdata,include="space:time")
# eegspace(newdata$space,yhat)

# # full prediction (spatial map at time point 65)
# newdata <- list(space=eegdense[ix,1:3],time=rep(65,nrow(eegdense[ix,])))
# yhat <- predict(eegmod,newdata=newdata)
# eegspace(newdata$space,yhat)

Plots Multi-Channel EEG Spatial Map

Description

Creates plot of multi-channel electroencephalography (EEG) spatial map. User can control the plot type (2d or 3d), the colormap, color, etc.

Usage

eegspace(space, voltage, vlim = NULL, mycolors = NULL, ncolor = 25,
         colorbar = TRUE, nctick = 5, rtick = 1, cex.axis = 1,
         barloc = NULL, colorlab = NULL, colorlabline = 3, cex.lab = 1,
         plotaxes = FALSE, main = "", xyzlab = NULL, cex.point = 1,
         cex.main = 1, nose = TRUE, ears = TRUE, head = TRUE,
         col.head = "AntiqueWhite", mar = NULL, ...)

Arguments

Value

Produces plot of EEG spatial map with NULL return value.

Note

For 3d plots, barloc can be one of four options: "backright", "backleft", "frontright", or "frontleft". For 2d plots, barloc can be either "right" or "left".

Currently supports spatial maps registered to the 84-channel cap produced by eegcap and eegcoord.

Author(s)

Nathaniel E. Helwig <helwig@umn.edu>

References

Begleiter, H. (1995). EEG Database [Dataset]. UCI Machine Learning Repository. doi:10.24432/C5TS3D

Examples

##########   EXAMPLE   ##########

# get time point 65 (approx 250 ms) from "eegdata" data
data(eegdata)
idx <- which(eegdata$time==65L)
eegdata <- eegdata[idx,]

# get average spatial map
eegmean <- tapply(eegdata$voltage,list(eegdata$channel,eegdata$group),mean)

# remove ears and nose
acnames <- rownames(eegmean)
idx <- c(which(acnames=="X"),which(acnames=="Y"),which(acnames=="nd"),which(acnames=="Cz"))
eegmean <- eegmean[-idx,]

# match to eeg coordinates
data(eegcoord)
cidx <- match(rownames(eegmean),rownames(eegcoord))

# # plot average control voltage in 3d
# open3d()
# eegspace(eegcoord[cidx,1:3],eegmean[,2])

# plot average control voltage in 2d
eegspace(eegcoord[cidx,4:5],eegmean[,2])

# # change 3d bar location and use play3d to rotate (not run)
# open3d()
# par3d(windowRect=c(0,0,600,600))
# eegspace(eegcoord[cidx,1:3],eegmean[,2],barloc="frontleft")
# play3d(spin3d(axis=c(0,0,1),rpm=5),duration=20)

# change 2d bar location
eegspace(eegcoord[cidx,4:5],eegmean[,2],barloc="left")

Plots Single-Channel EEG Time Course

Description

Creates plot of single-channel electroencephalography (EEG) time course with optional confidence interval. User can control the plot orientation, line types, line colors, etc.

Usage

eegtime(time, voltage, flipvoltage = TRUE, vlty = 1, vlwd = 2,
        vcol = "blue", voltageSE = NULL, slty = NA, slwd = 1,
        scol = "cyan", salpha = 0.65, conflevel = 0.95,
        plotzero = TRUE, zlty = 1, zlwd = 0.5, zcol = "black",
        xlim = NULL, ylim = NULL, xlab = NULL, ylab = NULL,
        nxtick = 6, nytick = 6, xticks = NULL, yticks = NULL,
        add = FALSE, ...)

Arguments

Value

Produces plot of EEG time course with NULL return value.

Note

Confidence intervals are formed using the normal (Gaussian) distribution.

Author(s)

Nathaniel E. Helwig <helwig@umn.edu>

References

Begleiter, H. (1995). EEG Database [Dataset]. UCI Machine Learning Repository. doi:10.24432/C5TS3D

Examples

##########   EXAMPLE   ##########

# get "PZ" electrode from "eegdata" data
data(eegdata)
idx <- which(eegdata$channel=="PZ")
eegdata <- eegdata[idx,]

# get average and standard error (note se=sd/sqrt(n))
eegmean <- tapply(eegdata$voltage,list(eegdata$time,eegdata$group),mean)
eegse <- tapply(eegdata$voltage,list(eegdata$time,eegdata$group),sd)/sqrt(50) 

# plot results with legend
tseq <- seq(0,1000,length.out=256)
eegtime(tseq,eegmean[,2],voltageSE=eegse[,2],ylim=c(-10,6),main="Pz")
eegtime(tseq,eegmean[,1],vlty=2,vcol="red",voltageSE=eegse[,1],scol="pink",add=TRUE)
legend("bottomright",c("controls","alcoholics"),lty=c(1,2),
       lwd=c(2,2),col=c("blue","red"),bty="n")

Plots Multi-Channel EEG Time Course

Description

Creates plot of multi-channel electroencephalography (EEG) time courses with subplots positioned according to electrode locations. User can control the plot orientation, line types, line colors, etc.

Usage

eegtimemc(time, voltmat, channel, size = c(0.75,0.75),
          vadj = 0.5, hadj = 0.5, xlab = "", ylab = "",
          voltSE = NULL, vlty = 1, slty = NA, vlwd = 1,
          slwd = 1, vcol = "blue", scol = "cyan", ...)

Arguments

Value

Produces plot of EEG time course with NULL return value.

Note

Currently supports 84 scalp electrodes (plus ears and nose): A1 A2 AF1 AF2 AF3 AF4 AF5 AF6 AF7 AF8 AFZ C1 C2 C3 C4 C5 C6 CP1 CP2 CP3 CP4 CP5 CP6 CPZ CZ F1 F2 F3 F4 F5 F6 F7 F8 F9 F10 FC1 FC2 FC3 FC4 FC5 FC6 FCZ FP1 FP2 FPZ FT7 FT8 FT9 FT10 FZ I1 I2 IZ NZ O1 O2 OZ P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 POZ PZ T7 T8 T9 T10 TP7 TP8 TP9 TP10

Subplots are created using eegtime, so input ... can be any optional input for eegtime.

Inspired by Frank Harrell's subplot function (in Hmisc package).

Author(s)

Nathaniel E. Helwig <helwig@umn.edu>

References

Begleiter, H. (1995). EEG Database [Dataset]. UCI Machine Learning Repository. doi:10.24432/C5TS3D

Harrell Jr F (2025). Hmisc: Harrell Miscellaneous. doi:10.32614/CRAN.package.Hmisc, R package version 5.2-3, https://CRAN.R-project.org/package=Hmisc

Examples

##########   EXAMPLE   ##########

# # get control ("c") data from "eegdata" data
# data(eegdata)
# idx <- which(eegdata$group=="c")
# eegdata <- eegdata[idx,]

# # get average 
# eegmean <- tapply(eegdata$voltage,list(eegdata$time,eegdata$channel),mean)
# eegse <- tapply(eegdata$voltage,list(eegdata$time,eegdata$channel),sd)/sqrt(50)

# # plot time course for all electrodes
# dev.new(height=15,width=15, noRStudioGD = TRUE)
# tseq <- seq(0,1000,length.out=256)
# eegtimemc(tseq,eegmean,colnames(eegmean),ylim=c(-11,14),voltSE=eegse)