eegsmooth: Spatial and/or Temporal Smoothing of EEG Data
In eegkit: Toolkit for Electroencephalography Data

Description Usage Arguments Value Note Author(s) References Examples

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.

1
2
3

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

`voltage`	Vector of recorded EEG voltage at each row in `space`.
`space`	Matrix of electrode coordinates (in three-dimensions) at which EEG was recorded. If `space=NULL`, data are temporally smoothed only.
`time`	Vector of time points at which EEG was recorded. If `time=NULL`, data are spatially smoothed only.
`nknots`	Number of knots to sample for smoothing. Positive integer.
`rparm`	Rounding parameter(s) to use for smoothing. See Notes and Examples.
`lambdas`	Smoothing parameter(s) to use for smoothing.
`skip.iter`	If `FALSE`, iterative spatial-temporal smoothing is skipped. Ignored if `space=NULL` or `time=NULL`.
`se.fit`	If `TRUE`, standard errors of smoothed values are calculated.
`rseed`	Random seed to use for knot selection. Set `rseed=NULL` to obtain different knots each time, or set `rseed` to any positive integer to use a different random seed.

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).

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).

Nathaniel E. Helwig <helwig@umn.edu>

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.

Helwig, N.E. (2015). bigsplines: Smoothing Splines for Large Samples. http://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.

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.

##########   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)