## 3 ESI-Clinical and Cognitive Research Implementations
### Clinical Applications
Although high-density EEG is not always available, low-density EEG source localization has been consistently effective in localizing epileptic brain activity, achieving over 90% accuracy in some cases, while indicating that performance could be further enhanced by including connectivity analysis [59, 60, 58]. ESI has also been employed in pediatric epilepsy. In a recent study [61], using a visual working-memory task as a stimulus and a source localization framework, critical biomarkers (that could not be detected by sensor-level analysis alone) were identified, leading to a more localized and specified treatment policy. The fine temporal communication of the different brain regions in source space has also been applied in neurodegenerative diseases [62]. Frequently, it is used in the evaluation of Alzheimer's disease progress or even in its prognosis, since the brain activation related to cognitive performance is commonly altered in the disease's progression [63]. As such, eLORETA and connectivity analysis has been adopted for Alzheimer's disease diagnosis [64, 65]. Another interesting approach is the use of ESI in localizing the origin of epileptic spikes using the differences created by non-Rapid Eye Movement (NREM) and Rapid Eye Movement (REM) sleep [66]. In view of the fact that EEG has been consistently employed to study sleep-related cycles and disorders, source localization can efficiently identify the main areas that are involved in the sleep onset transition and the activity alterations in the brain regions affected by sleep deprivation recovery [67, 68]. Furthermore, ESI is effective in detecting the areas and the associated activity during different NREM sleep stages with the use of MEG and EEG recordings [69], the areas responsible for generating sleep spindles [70] and slow wave generation [71]. Interestingly, source localization has been applied for sleepwalking and sleep terror origin decoding, used not only to estimate the brain areas that were affected by the sleep arousal disorder, but also for computing the decrement of slow wave activity power [72]. Despite the prognostic and diagnostic capabilities of ESI, rehabilitation processes can also be substantially supported. In this context, distinguishing direction by decoding movement intensions on people suffering from motor deficits (such as stroke patients) is of utmost importance in the optimal management and evaluation of rehabilitation planning. Multiple approaches have contributed in classifying multi-direction hand movement, using weighted MNE source localization [73], or Overlapping Averaging weighted MNE (OA-wMNE) [74]. Moreover, detecting the brain source locations of complex imaginary movements, like flexion, extension, supination and pronation, is also feasible with source localization methods and feature selection algorithms [75, 76]. On this premise, ESI has been employed to assess the allocation of brain functions with regard to the attempts made by stroke patients in limb movement [77]. Recent approaches have employed high-density EEG and beamformer techniques to facilitate functional connectivity analysis, thus measuring the connection between alpha and beta band coherence with motor learning and cortical plasticity of post-stroke impairments [78, 79, 80].