ERP Plot

ERP plot is plot type for visualisation of Event-related potentials. It can fully represent time and experimental condition dimensions using lines.

Setup

Package loading

using Unfold
using UnfoldMakie
using DataFrames
using CairoMakie
using DataFramesMeta
using UnfoldSim
using UnfoldMakie
include("../../../example_data.jl");

Data generation

Let's generate some data. We'll fit a model with a 2 level categorical predictor and a continuous predictor with interaction.

data, evts = UnfoldSim.predef_eeg(; noiselevel = 12, return_epoched = true)
data = reshape(data, (1, size(data)...))
f = @formula 0 ~ 1 + condition + continuous
se_solver = (x, y) -> Unfold.solver_default(x, y, stderror = true);

m = fit(
    UnfoldModel,
    Dict(Any => (f, range(0, step = 1 / 100, length = size(data, 2)))),
    evts,
    data,
    solver = se_solver,
)
results = coeftable(m)
res_effects = effects(Dict(:continuous => -5:0.5:5), m);

┌ Warning: using `Dict(:A=>(@formula,times/basisfunction))` is deprecated, please use `[:A=>(@formula,times/basisfunction)]` from now on
└ @ Unfold ~/.julia/packages/Unfold/cFXR7/src/fit.jl:74

Figure plotting

plot_erp(results)

To change legend title use mapping.color:

plot_erp(results, mapping = (; color = :coefname => "Conditions"))

Additional features

Effect plot

Effect plot shows how ERP voltage is affected by variation of some variable (here: :contionous).

• categorical_color::Bool = true

  Treat :color as continuous or categorical variable in case of numeric :color column.

• categorical_group::Bool = true

  Treat :group as categorical variable by default in case of numeric :group column.

plot_erp(
    res_effects;
    mapping = (; y = :yhat, color = :continuous, group = :continuous),
    layout = (; use_colorbar = false),
)

Significance lines

• significance::Array = [] - show a significance (see below).

Here we manually specify p-value lines. If array is not empty, plot shows colored lines under the plot representing the p-values. Below is an example in which p-values are given:

m = example_data("UnfoldLinearModel")
results = coeftable(m)
significancevalues = DataFrame(
    from = [0.01, 0.2],
    to = [0.3, 0.4],
    coefname = ["(Intercept)", "condition: face"], # if coefname not specified, line should be black
)
plot_erp(results; :significance => significancevalues)

Error ribbons

• stderror::bool = false - add an error ribbon, with lower and upper limits based on the :stderror column.

Display a colored band on the graph to indicate lower and higher estimates based on the standard error. For the generalizability of your results, it is always better to include error bands.

f = Figure()
results.coefname =
    replace(results.coefname, "condition: face" => "face", "(Intercept)" => "car")
results = filter(row -> row.coefname != "continuous", results)
plot_erp!(
    f[1, 1],
    results;
    axis = (title = "Bad example", titlegap = 12),
    :stderror => false,
    mapping = (; color = :coefname => "Conditions"),
)

plot_erp!(
    f[2, 1],
    results;
    axis = (title = "Good example", titlegap = 12),
    :stderror => true,
    mapping = (; color = :coefname => "Conditions"),
)

ax = Axis(f[2, 1], width = Relative(1), height = Relative(1))
xlims!(ax, [-0.04, 1])
ylims!(ax, [-0.04, 1])
hidespines!(ax)
hidedecorations!(ax)
text!(0.98, 0.2, text = "* Confidence\nintervals", align = (:right, :top))
f

There are two ways to implement it. First is using :stderror = true after ;.

results.se_low = results.estimate .- 0.5
results.se_high = results.estimate .+ 0.15
plot_erp(select(results, Not(:stderror)); stderror = true)

Second way is to specify manually lower and higher borders of the error bands.

Faceting

Creation of column facets for each channel.

m7 = example_data("7channels")
results7 = coeftable(m7)
plot_erp(results7, mapping = (; col = :channel, group = :channel))

Configurations of ERP plot

plot_erp!(f::Union{GridPosition, GridLayout, Figure}, plot_data::Union{DataFrame, AbstractMatrix, AbstractVector{<:Number}}; kwargs...)
plot_erp(times, plot_data::Union{DataFrame, AbstractMatrix, AbstractVector{<:Number}}; kwargs...)

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