How do you compare and select the best Bayesian model in Dynamic Causal Modeling or DCM of EEG data? In the previous two blogs we learnt about the basic components […]
Bayesian Model Selection For DCM Using EEG
Lab Talk
Dynamic Causal Modelling (DCM): DCM – Neural Mass Models and Bayesian Inference
This post discusses the core components of neural mass models and Bayesian inference in DCM applied to EEG.
Dynamic Causal Modeling and the Application of Bayes Theorem
Dynamic Causal Modeling (DCM) takes a probabilistic Bayesian framework to infer effective or causal connectivity, essentially to model how a stimulus would influence the connectivity between regions. In the previous […]
Perspectives on the Future of EEG from EEG2021
Where is the future of EEG? The EEG 2021 Symposium held last week discussed various aspects of the field and where it is going. Here are some highlights.
A Primer on Bayes Theorem (for Neuroimaging)
A Bayesian framework, one that works with conditional probabilities, has numerous applications in Neuroimaging in general and in EEG specifically. But first, a primer on Bayes theorem and how it works.
EEG Signal Quality in Wet Versus Dry Electrodes
Dry electrodes have some clear advantages but how does their signal quality compare to wet electrodes? Choosing one over the other may be a tradeoff between time, signal quality and stability.