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joss_paper/paper.md

@@ -59,12 +59,12 @@ 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="250pt"}
+![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"}
 
 ## 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.
 
-![Illustration of the different noise types (indicated by colour). Panel **A** shows the noise over time. Please note that the noise signals are shifted by 5&nbsp;µV for visualisation purposes. Panel **B** displays its $\text{log}_{\text{10}}\text{(power)}$ at normalized frequencies. \label{fig_noise_types}](plots/noise_types.svg){height="230pt"}
+![Illustration of the different noise types (indicated by colour). Panel **A** shows the noise over time. Please note that the noise signals are shifted by 5&nbsp;µV for visualisation purposes. Panel **B** displays its $\text{log}_{\text{10}}\text{(power)}$ at normalized frequencies. \label{fig_noise_types}](plots/noise_types.svg){height="250pt"}
 
 # Simulation example
 In this section, one can find an example of how to use `UnfoldSim.jl` to simulate continuous EEG data. Additional examples can be found in the [`UnfoldSim.jl` documentation](https://unfoldtoolbox.github.io/UnfoldSim.jl/dev/). Moreover, to get started, the `UnfoldSim.jl` package offers the function `predef_eeg` which, depending on the input, simulates continuous EEG data either for a single subject or multiple subjects.