Dynamic causal modeling for EEG and MEG.

Implementation and Analysis of Dynamic Causal Modeling for EEG/MEG Data

Dynamic Causal Models for Human Electrophysiology: EEG, MEG, and Local Field Potentials

Dynamic causal modelling of evoked responses in EEG/MEG with lead field parameterization.

Dynamical causal modeling (DCM) of evoked responses is a new approach to making inferences about connectivity changes in hierarchical networks measured with electro- and magnetoencephalography (EEG and MEG). In a previous paper, we illustrated this concept using a lead field that was specified with infinite prior precision. With this prior, the spatial expression of each source area, in the sen...

Neural Field Dynamics on the Folded Three-Dimensional Cortical Sheet and Its Forward EEG and MEG

Dynamic systems defined on the scale of neural ensembles are well-suited to model the spatiotemporal dynamics of electroencephalographic (EEG) and magnetoencephalographic (MEG) data. We develop a methodological framework, which defines the activity of neural ensembles, the neural field, on a sphere in three dimensions. Using Magnetic Resonance Imaging (MRI) we map the neural field dynamics from...

Difference-Based Causal Models: Bridging the gap between Granger causality and DCMs

In recent years, the study of causality in the brain, or effective connectivity, has become increasingly important in the field of computational neuroscience. Neuroimaging modalities such as EEG, fMRI and MEG provide an immense and rich source of data. Causal analysis has the potential to help make sense of this complex data, and provide important insights about the neural basis of cognition. T...