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…
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…
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.
Wet and Dry Electrodes for EEG
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 in the EEG
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…
Optical Stimulation in Bidirectional BCI
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…
EEG Complexity Increases in Lockstep with Stimulus Consumption
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…