## 3 ESI-Clinical and Cognitive Research Implementations
### Clinical Applications  
The most important employment of ESI is in clinical situations. The high temporal resolution provided by EEG, combined with the spatial estimation of the sources given by the ESI, provides non-invasive monitoring of specific brain areas that single modality analysis cannot. As such, ESI is commonly used in locating the epileptic foci, vital for epilepsy treatment and/or for subsequent surgery [50]. In a recent long-term study [51], a synchronously recorded high-density EEG and MEG source localization of ictal and interictal activity provided more accurate results when the analysis was conducted for each modality alone than the multi-modal approach. It is also suggested that less emphasis should be given to mid-phase ictal discharge analysis and include the late-peak phase in source solutions. EEG source localization as a single modality technique is also an effective localization technique for focal epilepsy, minimizing the cost of recording different modalities concurrently, requiring additional expensive equipment [52, 53]. Apart from the obvious economic and scientific benefits of ESI applications, the non-invasiveness of source localization holds significant social and healthcare advantages since (multiple) intracranial invasive recordings can be avoided in certain types of epilepsy, thus relieving preoperative anxiety from patients as well as risk of surgical site infections. On \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Authors** & **Advantages** & **Disadvantages** \\ \hline Recurrent Neural & & Extremely fast computation of source estimates, once the training has completed. Can harness the spatio-temporal information of EEG, resulting in more robust solution regarding noise. Great expandability and room for improvement. & Trained for single sources. Requires a lot of time for the training session, even if the model is simple. Worse accuracy than other models presented. \\ \hline Convolutional Neural & [11] & Simplicity and expandability. Once trained, produces results extremely fast. & Trained on single time points and does not incorporate the temporal information of EEG creating low noise tolerance. Lower accuracy on multisource scenarios. \\ \hline Denoising AutoEncoder & [46] & Very high noise tolerance, producing accurate results even with low SNR. Do not require mathematical priors. & Requires a lot of time for training and offline computation of the Leadfield matrix. Susceptible to overfitting due to vanishing gradient for complex scenarios. \\ \hline Bayesian & [12] & Near-zero mean localization error. Great recovery and accuracy of dipole locations in low SNR. Sparser solutions. Correct estimation of the amplitude of source currents. & Very high computational cost. Requires accurate head model for high accuracy. \\ \hline Method—Kalman Filters & [49] & Near-zero mean localization error. Lower computational cost than other spatio-temporal dynamic algorithms, faster than other KF approaches. & Under-estimation of the amplitude of source currents. Requires a priori information for the source covariance matrix. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Current trends in state-of the-art methods used in ESI. this account, the prevalent utilization of EEG source localization is in presurgical evaluation of drug-resisting epileptic patients [13]. During this procedure, the information provided by the ESI about epileptiform discharges can have significant impact in the management plan, in terms of placing more accurately the intracranial electrodes [54, 55]. The identification of the Seizure Onset Zones can not only define the exact cortical structures for surgical extraction but also provide important information concerning the neurodynamic state of the epileptic brain activity, especially in generalized (e.g., absence epilepsy) [56] or focal epilepsies (e.g., structural, non-lesional epilepsy) [57, 52, 58].