2025-10-03 –, Coffee room
Language: English
UnfoldSim.jl is used to simulate multivariate time series, with a focus on continuous EEG data based on event-related potentials (ERPs). Its modular approach allows users to specify complex experimental designs, parameterized response functions, event order and temporal structure, and different noise specifications. Using abstract types and multiple dispatch, each ingredient can easily be exchanged and customized, allowing users to tailor the simulation to their needs.
The use cases for simulated (EEG) data are manifold. They include conducting power analyses, testing and comparing different analysis tools, validating statistical methods, creating example data for educational purposes, and illustrating conceptual issues.
In our work, we often analyze data containing (temporally) overlapping events (e.g., stimulus onset and button press, or consecutive eye-fixations), non-linear effects, and complex experimental designs. Therefore, we created a simulation package that can be used to simulate EEG data with these properties.
However, the properties required for the simulated data vary by application, and therefore, a flexible and modular simulation approach is needed. For this reason, one of the fundamental design principles of UnfoldSim.jl is ‘modularity’, allowing users to adapt the simulation to their specific research question or application.
For a successful simulation, the user provides four ingredients:
1. An experimental design, with both categorical and continuous variables, and support for sequences of different event types (e.g. stimulus - response).
2. Event basis functions specified via linear or hierarchical models.
3. An inter-event onset distribution describing the temporal spacing of the events.
4. A noise specification.
Many of the simulation ingredients can also be nested to allow more complex functionalities as for example multi-channel simulations for EEG data.
This talk will give an overview of the building blocks of the package (e.g., the implementation of the simulation ingredients using abstract types) and guide you through a simple simulation example to illustrate the simulation workflow and showcase the influence of different parameters on the simulated data.
