Dynamotypes for Dummies: A toolbox, atlas, and tutorial for simulating a comprehensive range of realistic synthetic seizures

Epileptic seizures involve the brain transitioning from a resting state to an abnormal state of synchronized bursting, akin to a bifurcation in dynamical systems where a parameter shift triggers a qualitative change in behavior. A comprehensive model was previously developed that used dynamical equations capable of simulating 16 “dynamotypes” of seizures that span the full range of theoretical first-order dynamics. The current work is a tool to understand and implement this model with the goal of generating a wide range of synthetic seizures. We present a dynamical atlas of all 16 possible onset-offset bifurcation combinations, each characterized by distinct features in simulated EEG-like recordings. We include a tutorial and GUI that generates diverse simulated seizures. In addition, we include methods to add realistic noise and filtering effects to enhance their resemblance to human EEG data. This toolbox has two purposes: it is a practical, educational demonstration of the dynamical principles underlying seizure bifurcations, and it provides the algorithms necessary to produce large numbers of realistic, diverse seizure patterns that have similar noise and filtering characteristics as human EEG. This generative model can aid in training seizure detection algorithms, understanding brain dynamical behavior for clinicians, and exploring the impact of noise on EEG recordings and detection algorithms. Significance Statement This work contains a tutorial, atlas, and generative model for a comprehensive, realistic seizure model based upon dynamical theory. This user-friendly tool is designed to teach the theoretical principles underlying the model, as well as implement it in order to generate a wide range of simulated seizures that have the same appearance as human EEG recordings. This work is thus broadly applicable to clinicians, students, and researchers.