Overview

Teaching: 40 min
Exercises: 20 min

Questions

• What is Dynamic Functional Connectivity?

• How can we model EEG time-series to estimate dynamic functional connectivity?

• Which are the main parameters we need?

Objectives

• Modeling EEG signals as multivariate, multi-trial time series.

• Estimation of effective connectivity through Self-tuning Optimized Kalman (STOK) filter.

• Add structural connectivity as prior information before estimating the effective connectivity.

Hand over the stage to Maria and Jolan.

• Link to presentation.
• Link to exercises.

Key Points

• Dynamic Functional Connectivity takes into account that EEG is a nonstationary signal (i.e., signal statistical characteristics change with time).

• The proposed algorithms (STOK and siSTOK) to estimate Dynamic Functional Connectivity are powerful tools to integrate to effective connectivity brain structural information as a prior.

---