Statistical Challenges for Complex Brain Signals and Images (23w5030)


Carolina Euan (Lancaster University)

Mark Fiecas (University of Minnesota, School of Public Health.)

Hernando Ombao (King Abdullah University)


This workshop will focus on developing novel statistical methodology to analyze brain signals, which is crucial to understanding normal brain function and alterations associated with neurological and mental diseases. Brain signals are complex and are a reflection of the complexity of the unobserved brain processes. Thus, the primary considerations for developing statistical models are flexibility, generalizability, and incorporation of known biology. In this workshop, we will discuss the most recent challenges in this area and how the current methods need to be improved to better describe the observed brain signals.

We will have three brainstorming sessions focused on the following tracks: Track 1: Challenges in developing high dimensional models for brain signals. Track 2: Computational challenges for pre-processing, model implementation, visualization, and software development. Track 3: Machine Learning algorithms and approaches to complement statistical techniques.