EPP-TB: The ERP Post-Processing Tool Box

Once completing pre-processing in eeglab/erplab/Net Station, you’re ready to get to the fun stuff: plotting and measuring with EPP-TB! This package is aimed at simple (readable), concise (one function per action) and reproducible code writing. Additionally, a major component is the ability to export data and plots to be used and further manipulated elsewhere (say, ggplot2?).

All user-end functions start with epp_*.

Getting Started

Prerequisites

To use EPP-TB you will need:

• Matlab (2015a+).
• eeglab (14.X.X) for importing .set files, and plotting topos.
• R (for plotting in R)
  • tidyverse + purrr.
  • scales for plotting in R.
  • mgcv for plotting topo plots.

Installing

You can install the package by downloading and adding the EPP-TB folder (and sub-folders) to your Matlab paths.

Importing Data into an EPP structure

Three import methods are currently supported:

• epp_loadeeglab - Import multiple .set files from eeglab (supports wavelet analysis based on Mike X. Cohen’s great book and code).
• epp_loaderplab - Import from erplab.
• epp_loadegimat - Import multiple .mat files exported from Net Station (EGI).

The resulting structure array has length(struct) equal to the number of conditions, and contains the following fields, per condition:

• Condition: the name of the conditions.
• timeLine: a vector of time points.
• IDs: a table with two variables - ID and nTrials (the number of trials a ERP has been averaged across).
• Data: a channels x time points x Subjects matrix for ERP data.

If a wavelet analysis has been preformed, the Data field is replaced with:

• ersp and itc: channels x frequencies x time points x Subjects matrices.

• Freqs: a vector of frequencies used.

Data Reduction / Reshaping

These functions can be used to compute new conditions or manipulate existing data:

• Merge 2 or more conditions with epp_combineconds().
• Compute differences between 2 conditions with epp_diffwave().
• Compute LRP with epp_LRP()
• Compute global field potentials with epp_GFP().
• Collapse TF data to frequency waveforms with epp_reshapeTF().
• Make grand-average ERP/ERSP/ITC with epp_makegrands() (useful for plotting large data sets).

Working with ID data

• Add data to study.IDs from a table with epp_appendID().
• Retain subjects that have data in all specified conditions with epp_matchsubjects().
• Select data from specific subjects by some variable in study.IDs with epp_filter_by().
• Pull data from study.IDs with epp_extractIDs().

Plotting

For all epp_plot* functions, which allow plotting data from epp structures, there is an accompanying general p_* function, that allows plotting from other data structures (with a little bit of wrangling).

ERP Plots

Grand averages

Grand averages can be plotted by specifying the conditions and channel indices (averaged across) to plot:

conds         = {'Crr','L5'};
channel_inds  = [5 6 11 12];
epp_plotgrands(study,conds,channel_inds)

You can also plot error envelopes:

epp_plotgrands(study,conds,channel_inds,'errorType','SE')
% can also be set to 'SD' or 'CIXX' 
% (with XX replaced with any percent: 'CI95','CI80', etc...).

Topo Plots

Topo plotting is dependent on eeglab functions. Additionally, you will need to provide a eeglab-like chanlocs structure to these functions, with length(chanlocs)==size(study.Data,1).

times = [185];
epp_plottopo(study,chanlocs,conds,channel_inds,times)

Butterfly and Trace Plots

Butterfly plots can be used to plot the mean ERP for each subject individually.

epp_plotbutterfly(study,conds,channel_inds)

Trace plots are similar to butterfly plots, but the mean activation (across subjects) is plotted for each channel separately.

channel_inds = []; % if left blank, all channels are plotted.
epp_plotbutterfly(study,conds,channel_inds,'trace',true)

Channel Plots

Similar to trace plots, channel plots give a picture of what is happening at each channel (like eeglab’s plottopo). These come in two flavors:

• Topo Plots - channel data is plotted in 2-d space, like a topo-plot.
• Grid Plots - channel data is plotted on a simple grid.

Channel topo plots are created with the following Matlab call:

channel_inds = []; % if left blank, all channels are plotted.
epp_plotchannels(study,conds,electrodes,'chanlocs',chanlocs)

Grid plots are called using the same call, without providing chanlocs.

TF Plots

Time-Frequency Plot

Time Frequency plots plot both ersp and itc:

epp_plotTF(study,conds,channel_inds)

Other Plots

All the listed above plotting methods also support TF data. For example:

times = [160 410];
bands = [4 8; 8 12];
% a matrix of frequencies, with each row containing a range of frequencies
% to plot (1st column is lower limit, 2nd column is upper limit of band).
epp_plottopo(study,chanlocs,conds,channel_inds,times,...
    'freqs',bands, 'type', 'ersp')

Exporting to R

All plots can be exported to R and plotted with ggplot2 by setting 'R',true in any of the plotting function (this is how the plots in this README were made). This produces two time-stamped files:

• A data file (*_data.csv)
• A code file (*_code.R), to plot said data using ggplot2.

Measuring

Measuring can be done via the epp_get* functions. These are wrapper functions for the various m_* functions (with one functions per method), which should not be called directly (unless you know what you’re doing).

Saving results always produces a 2-sheet .xlsx file, with the second sheet containing the parameters used in measuring - this is to insure reproducibility of results. Thus, if you have the output .xlsx file, you’ll never find yourself asking “what was the time window we used last time?”.

ERP Amplitude

The function epp_getAmp implements the methods for measuring amplitudes described in chapter 9 of Steven J. Luck’s intro to ERP book.

• (Local) Peak amplitude
• Point amplitude
• Mean amplitude
• Integral
• Area

ERP Latency

The function epp_getLat implements the methods for measuring latencies described in chapter 9 of Steven J. Luck’s intro to ERP book, as well as those described in Kiesel et al.’s Jakknife paper.

• (Local) Peak latency
• Relative criterion
• Fractional area
• Baseline deviation
• Absolute criterion

TF Power

The function epp_getTF measure the mean ersp / itc from selected channels, within a specified time-window,separately for specified frequency bands (as shown above, for epp_plottopoTF).

Authors

• Mattan S. Ben-Shachar [aut, cre].
• Rachel Rac [ctb].
• Michael Shmueli [ctb].

Acknowledgments

• Mike X. Cohen - who’s code was implemented in f_WaveletConv.m.
• Matt Craddock - who’s code was implemented in epp_plottopo.R.
• Winston Chang - who’s code for computing within-subject (and mixed model) CI’s was implemented in epp_plotgrands.m.