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 waveforms (interactive_waveforms)
• topographic maps in 2D (topo_plot)
• scalp plots in 3D (scalp_plot)
• functions for computing summary statistics, baseline correction, pointwise and jackknife mean (summary_stats_rt,baseline_correction, compute_mean)
• functions for easy selection of subdata or region of interest (pick_data, pick_region)
• 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)

You can install the current version of diegr from CRAN with:

install.packages("diegr")

or the latest development version from GitHub with:

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

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:

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

Note: It is not necessary for the data to contain all variables, but if it does, they must be named according to the structure presented above.

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) for 2 subjects and 204 selected sensors in 50 time points,
• 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").

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")

epochdata |>
  pick_data(subject_rg = 1, sensor_rg = "E65") |>
boxplot_epoch(amplitude = "signal", 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.

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 |>
  pick_data(subject_rg = 1, time_rg = 15, epoch_rg = 10)
# or you can use dplyr::filter()
# 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)

Compute the average signal for subject 2 from the channels E65 and E34 (exclude the oulier epochs 14 and 15) and then display it along with CI bounds (use plot_time_mean conditioned by sensor)

# extract required data
edata <- epochdata |>
  pick_data(subject_rg = 2, sensor_rg = c("E34", "E65"), epoch_rg = 1:13)
# baseline correction
data_base <- baseline_correction(edata, baseline_range = 1:10)
# compute average
data_mean <- data_base |> 
  compute_mean(amplitude = "signal_base", type = "point", domain = "time")
# plot the average line with CI
plot_time_mean(data = data_mean, t0 = 10, condition_column = "sensor", legend_title = "Sensor")

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.