Time-Frequency and Phase-Based Connectivity Analysis

In this unit, you’ll explore decomposing the EEG signal into different frequency bands and analyzing their power and phase.

In the first lesson, you’ll learn how to generate Morlet wavelets and apply them to simulated signals to extract their instantaneous power. You’ll then apply this method to real EEG data to detect the event-related desynchronization (ERD) that occurs during motor-imagery tasks.

In the second lesson, you’ll learn how to analyse the consistency of the phase difference between two neural recordings to estimate their connectivity using the phase locking value (PLV) and phase lag index (PLI). By using data from simulated sources with a known location and phase difference, you’ll be able to connect the connectivity estimates to the underlying sources and see how volume conduction can bias the results.