Dynamic and Interactive EEG Graphics.

diegr

Overview

The name diegr comes from Dynamic and Interactive EEG Graphics using R. The diegr package enables researchers to visualize high-density electroencephalography (HD-EEG) data with animated and interactive graphics, supporting both exploratory and confirmatory analyses of sensor-level brain signals.

The package diegr includes:

• interactive boxplots (boxplot_epoch, boxplot_subject, boxplot_rt)
• interactive epoch waveforms (interactive_waveforms)
• topographic maps in 2D (topo_plot)
• scalp plots in 3D (scalp_plot)
• functions for computing baseline correction, pointwise and jackknife mean (baseline_correction, compute_mean)
• functions for plotting the mean with pointwise confidence interval (plot_time_mean, plot_topo_mean)
• animations of time course of the raw signal or the average in 2D and 3D (animate_topo, animate_topo_mean, animate_scalp)

Installation

You can install the development version of MyPackage from GitHub with:

# install.packages("devtools")
devtools::install_github("gerslovaz/diegr") 

Data

Because of large volumes of data obtained from HD-EEG measurements, the package allows users to work directly with database tables (in addition to common formats such as data frames or tibbles). Such a procedure is more efficient in terms of memory usage.

The database you want to use as input to diegr functions must contain columns with the following structure::

• subject - ID of subjects,
• epoch - epoch numbers
• time - numbers of time points (as sampling points, not in ms),
• sensor - sensor labels,
• signal - the EEG signal amplitude in microvolts (in some functions it is possible to set the name of the column containing the amplitude arbitrarily).

The package contains some included training datasets:

• epochdata: epoched HD-EEG data (anonymized short slice from big HD-EEG study presented in Madetko-Alster, 2025) arranged as mentioned above,
• HCGSN256: a list with Cartesian coordinates of HD-EEG sensor positions in 3D space on the scalp surface and their projection into 2D space
• rtdata: response times (time between stimulus presentation and pressing the button) from the experiment involving a simple visual motor task (anonymized short slice from big HD-EEG study presented in Madetko-Alster, 2025).

For more information about the structure of built-in data see the package vignette vignette("diegr", package = "diegr").

Quick examples

Interactive boxplot

This is a basic example which shows how to plot interactive epoch boxplots from chosen electrode in different time points for one subject:

library(diegr)
data("epochdata")

boxplot_epoch(epochdata, amplitude = "signal", subject = 1, channel = "E65", time_lim = c(10:20))

Note: The README format does not allow the inclusion of plotly interactive elements, only the static preview of the result is shown.

Topographic map

data("HCGSN256")
# creating a mesh
M1 <- point_mesh(dimension = 2, n = 30000, type = "polygon", sensor_select = unique(epochdata$sensor))
# filtering a subset of data to display 
data_short <- epochdata |>
  dplyr::filter(subject == 1 & epoch == 10 & time == 15) 
# function for displaying a topographic map of the chosen signal on the created mesh M1
topo_plot(data_short, amplitude = "signal", mesh = M1)

Computing and displaying the average

Compute the average signal for subject 2 from the channel E65 (exclude the oulier epochs 14 and 15) and then display it along with CI bounds

# extract required data
edata <- epochdata |>
dplyr::filter(subject == 2 & sensor == "E65" & epoch %in% 1:13)
# baseline correction
data_base <- baseline_correction(edata, baseline_range = 1:10)
# compute average
data_mean <- compute_mean(data_base, amplitude = "signal_base", subject = 2,
                          channel = "E65", type = "point")
# plot the average line with CI in blue colors
plot_time_mean(data = data_mean, t0 = 10, color = "blue", fill = "lightblue")

For detailed examples and usage explanation, please see the package vignette: vignette("diegr", package = "diegr").

References Madetko-Alster N., Alster P., Lamoš M., Šmahovská L., Boušek T., Rektor I. and Bočková M. The role of the somatosensory cortex in self-paced movement impairment in Parkinson’s disease. Clinical Neurophysiology. 2025, vol. 171, 11-17. https://doi.org/10.1016/j.clinph.2025.01.001

License This package is distributed under the MIT license. See LICENSE file for details.

Citation Use citation("diegr") to cite this package.