## 3 ESI-Clinical and Cognitive Research Implementations
### Cognitive Applications  
Apart from the clinical applications, brain source localization helps in gradually elucidating the underlying neural substrates in various conditions and mental states. As such, ESI has been consistently utilized in the decryption of cognitive functions in various tasks, such as word interpretation and fatigue analysis.  
Multiple paradigms include source localization in Event-Related Potentials (ERPs) to illuminate the brain activity related to certain stimuli or events. For instance, event related responses in Go-Nogo tasks combined with ESI have been employed to identify and interpret somatosensory response inhibition [81, 82]. Specifically, ERP analysis and standardized LORETA (sLORETA) were used to link the differences of amplitude of Nogo-N2 ERP-component with the changes of activation in the right inferior gyrus, showing that response inhibition is correlated with sensory gain modulation [82]. Another recent study [83] employed beamformers on ERP analysis to provide evidence of the regions responsible for generating the P300 signal related to attention and memory operations of the brain.  
The interpretation of facial expressions and emotions in source space has also been consistently investigated in various studies. As such, taking into account that cognitive face processing is evolving during the early stages of life, the employment of LORETA has been able to provide insight into the underlying process [84]. In a similar way, using an MNE algorithm, ESI was able to further advance the brain state decoding by deciphering the neural operations of facial recognition [85], while the utilization of the LAURA method and harnessing of the \"Kuleshov effect\" has effectively highlighted the facial expression cognitive functions [86]. Furthermore, combining ESI with a self-organizing feature map (SOMF) resulted in accurate categorization of emotions perceived from physiognomic components, (up to 91% of classification accuracy) [14]. Similar source localization studies have also been conducted for identifying and analyzing the processing of facial features in autism, distinguishing the regions activated in different cases [87].  
More recently, ESI has been applied in visual and auditory evoked data, in an effort to understand the underlying brain connections and the related inhibitory networks [5, 88]. In fact, since source imaging is able to analyze the paths of brain activity through time, connectivity analysis presents a novel prospective in identifying the transmission of information between cortical areas. In this regard, language understanding and the impact on the brain has been studied using connectivity and sLORETA, revealing individual networks active during lexico-semantic processing of single words [89]. Additionally, pattern recognition frameworks combined with ESI have effectively identified common and distinct functional connections (representing a link between the various brain structures) in different workload levels in two working memory tasks [90]. Moreover, in simulated 2D and 3D flight simulation experiments, eLORETA and graph theory was utilized to assess the dynamic rewiring of functional connections among brain areas as a result of mental workload, establishing the basis for potential real-world applications [91].