From e17ed15f548c3b3ce841b305d2ef85a037e38ef4 Mon Sep 17 00:00:00 2001 From: jschepers Date: Thu, 1 Aug 2024 13:38:08 +0000 Subject: [PATCH] Try to fix subscript --- joss_paper/paper.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index b41580f..e8135bf 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -64,7 +64,7 @@ The inter-onset distribution defines the distance between events in the case of ## 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. Panel **B** displays its log10(power) at normalized frequencies.\label{fig_noise_types}](plots/noise_types.svg) +![Illustration of the different noise types (indicated by colour). Panel **A** shows the noise over time. Panel **B** displays its log10(power) $\text{log_{10}(power)}$ at normalized frequencies.\label{fig_noise_types}](plots/noise_types.svg) # 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. @@ -96,7 +96,7 @@ design = | 1.11111 | car | | 0.555556 | car | -2\. Next, we create a signal consisting of two different **components**. For the first component, we use the prespecified N170 base with an intercept of 5 µV and a condition effect of 3 µV for the “face/car” condition i.e. faces will have a more negative signal than cars. For the second component, we use the prespecified P300 base and include a linear and a quadratic effect of the continuous variable: the larger the value of the continuous variable, the larger the simulated potential. +2\. Next, we create a signal consisting of two different **components**. For the first component, we use the prespecified N170 base with an intercept of 5 µV and a condition effect of 3 µV for the “face/car” condition i.e. faces will have a more negative signal than cars. For the second component, we use the prespecified P300 base and include a linear and a quadratic effect of the continuous variable: the larger the value of the continuous variable, the larger the simulated potential. ```julia n1 = LinearModelComponent(;