From 1f808327194d8f69255d941b6b8aea4676422cc6 Mon Sep 17 00:00:00 2001 From: jschepers Date: Mon, 5 Aug 2024 12:32:34 +0000 Subject: [PATCH] adapted figure size --- joss_paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index c233e0e..9e2f620 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -59,7 +59,7 @@ To generate complex activations, it is possible to specify a vector of `<:Abstra The inter-onset distribution defines the distance between events in the case of a continuous EEG. Currently, `UniformOnset` and `LogNormalOnset` are implemented. By specifying the parameters of the inter-onset distribution, one indirectly controls the amount of overlap between two or more event-related responses. \autoref{fig_onset_distributions} illustrates the parameterization of the two implemented onset distributions. -![Illustration of the inter-onset distributions. The colours indicate different sets of parameter values. Please note that for the lognormal distribution, the parameters are defined on a logarithmic scale, while the distribution is shown on a linear scale. \label{fig_onset_distributions}](plots/onset_distributions.svg){height="230pt"} +![Illustration of the inter-onset distributions. The colours indicate different sets of parameter values. Please note that for the lognormal distribution, the parameters are defined on a logarithmic scale, while the distribution is shown on a linear scale. \label{fig_onset_distributions}](plots/onset_distributions.svg){height="220pt"} ## Noise types UnfoldSim.jl offers different noise types: `WhiteNoise`, `RedNoise`, `PinkNoise` and exponentially decaying autoregressive noise (`ExponentialNoise`) (see \autoref{fig_noise_types}). In the future, we will add simple autoregressive noise and noise based on actual EEG data.