MEG Reading Group
Primer on mutual information for M/EEG
I will present a brief introduction to information theory with application to the analysis of MEG and EEG data. I will then introduce a new bin-less estimation method that I have recently developed and show examples of how to use it. I will show how this method can be used to calculate quantities like transfer entropy, as well as how it can be used to investigate relationships between signals by quantifying synergy / redundancy. Examples include relating extracted ERP features (peak amplitude / latency) to the full evoked timecourse, and relating the information content of EEG and fMRI data.