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 […]
This post discusses the core components of neural mass models and Bayesian inference in DCM applied to EEG.
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 […]
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 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.
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.
EEG recording technology remains similar in principle since its first use in 1923. However, a vast array of electrode types, both wet and dry, are […]
Education and travel both increase the scope for more complex, novel stimulus to the brain. In turn the complexity of the brain EEG signal increases […]
Optogenetics have been used for bi-directional BCI in mice but is unsuitable for freely moving subjects due to its tethered optical fibers or fixed imaging […]
More complex environments with expanded scope for stimulii to the brain have far reaching impact on various aspects of the brain. Sapien Labs’ Human Brain […]