# DIVA Meets EEG: Model Validation Using Formant-Shift Reflex
## 4 Discussion
### Diva_EEG  
Other modifications of the DIVA model preceded the development of DIVA_EEG. For instance, DIVA has been extended to incorporate physiologically based laryngeal motor control [63] or simplified for assessing the relative contribution of feedback and feedforward control mechanisms to sensorimotor adaptation [64]. Furthermore, DIVA has been translated to open-source codes, thereby facilitating their integration with freely available machine learning tools [65]. The DIVA environment, which also comprises the gradient order DIVA (GODIVA) for the analysis of speech sequencing [66], is now enriched with a new neuroimaging modality (EEG).  
Several aspects need to be considered when interpreting the synthetic EEG that resulted from the activations of the different DIVA maps. First, DIVA_EEG comprise anatomical priors since the locations of seeds for the EEG generation are the same as for the nodes in the original DIVA model [3], which in turn were obtained from fMRI feedback perturbations protocols [18; 21]. Noteworthy, since brain activity reflected in the EEG is mainly restricted to the cerebral cortex [18; 36], DIVA_EEG does not include subcortical regions, which are already considered in DIVA. Second, the brain activity of DIVA_EEG seeds are simulated as Gaussian functions that extend 2 mm from the centroid. Therefore, seeds in the model can be considered as a point source for the EEG generation since the seed size is lower than the voxel size of the head model used in this study for solving the EEG inverse problem [50]. Third, the main outcome of the study is presenting the first version of DIVA_EEG. The scalp topography and the cortical source of the synthetic EEG obtained with DIVA_EEG (Figures 4 and 5) are highly dependent on the head model and the theoretical considerations selected for constructing the generative EEG model and solving the EEG inverse problem. Further refinement of the DIVA_EEG can result from including individual head models [67; 68], generating brain activity maps that combine the EEG obtained from DIVA_EEG and the BOLD signal obtained with DIVA [3], and testing the replicability of the results as a function of the EEG generative model [69] and the source estimation method [70]. Noteworthy, future developments can use the computational load of the nodes (the instantaneous neural activity) as input of mean field models (e.g., neural mass models) to generate oscillatory EEG-like signals for assessing the EEG oscillatory  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Brain Lobe** & **AAL Region** & **Hemisphere** \\ \hline \multirow{6}{*}{Parietal} & Postcentral & (bilateral) \\  & Parietal\_Sup & (bilateral) \\  & Parietal\_Inf & (right) \\  & SupraMarginal & (bilateral) \\  & Parcentral & (right) \\  & Lingual & (bilateral) \\  & Fusiform & (bilateral) \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._dynamic [71], including cross-frequency coupling. This aspect is relevant since accurate speech encoding has been associated with the coupling of theta oscillations that tracks slow speech fluctuations and gamma-spiking activity related to phoneme-level responses [72].