Electroencephalography (EEG) is a non-invasive diagnostic technique for recording brain electric activity. The EEG source localization has been an area of research widely explored during the last decades because it provides helpful information about brain physiology and abnormalities. Source localization consists in solving the so-called EEG inverse problem. Over the years, one of the most employed method for solving it has been LORETA (Low Resolution Electromagnetic Tomography). In particular, in this review, we focused on the findings about the LORETA family algorithms applied to high-density EEGs (HD-EEGs), used for improving the low spatial resolution deriving from the traditional EEG systems. The results were classified according to their clinical application and some aspects arisen from the analyzed papers were discussed. Finally, suggestions were provided for future improvement. In this way, the combination of LORETA with HD-EEGs could become an even more valuable tool for noninvasive clinical evaluation in the field of applied neuroscience.

Alzheimer’s Disease (AD) is a neurological disorder characterized by a progressive deterioration of brain functions that affects, above all, older adults. It can be difficult to make an early diagnosis because its first symptoms are often associated with normal aging. Electroencephalography (EEG) can be used for evaluating the loss of brain functional connectivity in AD patients. The purpose of this paper is to study the brain network parameters through the estimation of Lagged Linear Connectivity (LLC), computed by eLORETA software, applied to High-Density EEG (HD-EEG) for 84 regions of interest (ROIs). The analysis involved three groups of subjects: 10 controls (CNT), 21 Mild Cognitive Impairment patients (MCI) and 9 AD patients. In particular, the purpose is to compare the results obtained using a 256-channel EEG, the corresponding 10–10 system 64-channel EEG and the corresponding 10–20 system 18-channel EEG, both of which are extracted from the 256-electrode configuration. The computation of the Characteristic Path Length, the Clustering Coefficient, and the Connection Density from HD-EEG configuration reveals a weakening of small-world properties of MCI and AD patients in comparison to healthy subjects. On the contrary, the variation of the network parameters was not detected correctly when we employed the standard 10–20 configuration. Only the results from HD-EEG are consistent with the expected behavior of the AD brain network.

Alzheimer’s Disease (AD) is an incurable neurodegenerative disorder which mainly affects older adults. An early diagnosis is essential because medical treatments can slow down the progression of the disease only if provided during the first stage, called Mild Cognitive Impairment (MCI). Starting from the study of electroencephalografic signals, brain functional connectivity analyses can be performed with the support of the graph theory. In particular, the purpose of this work is to verify the performances of three indexes, typically adopted to evaluate the graph robustness, in order to estimate the functional connectivity for three groups of subjects: healthy controls and people affected by dementia at two different stages (MCI and AD). The results obtained by the Connection Density Index, the Randić Index, and a normalized version of the Kirchhoff Index revealed a higher robustness in the brain networks of healthy people, followed by MCI and, finally, by AD patients, consistent with the hallmarks of Alzheimer’s disease. The statistical analysis showed that there is a significant difference between controls and AD for all three indexes. Finally, all three indexes were compared, revealing that the the Randić Index outperformed the other two indexes. These preliminary outcomes will be exploited to address further in-depth and time-expensive analyses for improving the diagnosis of Alzheimer’s disease.

Tremor is one of the most common symptoms of Parkinson’s disease (PD), assessed using clinician-assigned clinical scales, which can be subjective and prone to variability. This study evaluates the potential of unsupervised learning for the classification and assessment of tremor severity from wearable sensor data. We analyzed 25 resting tremor signals from 24 participants (13 PD patients and 11 controls), focusing on motion intensities derived from accelerometer recordings. The k-means clustering algorithm was employed, achieving a classification accuracy of 76% for tremor versus non-tremor states. However, performance decreased for multiclass tremor severity classification (57.1%) and binary classification of severe versus mild tremor (71.4%), highlighting challenges in detecting subtle intensity variations. The findings underscore the utility of unsupervised learning in enabling scalable, objective tremor analysis. Integration of such models into wearable systems could improve continuous monitoring, enhance rehabilitation strategies, and support standardized clinical assessments. Future work should explore advanced algorithms, enriched feature sets, and larger datasets to improve robustness and generalizability.

Conduction aphasia is a language disorder occurred after a left-brain injury. It is characterized by fluent speech production, reading, writing and normal comprehension, while speech repetition is impaired. The aim of this study is to investigate the cortical responses, induced by language activities, in a sub-acute stroke patient affected by conduction aphasia before and after an intensive speech therapy training. The patient was examined by using High-Density Electroencephalogram (HD-EEG) examination, while was performing language tasks. the patient was evaluated at baseline and after two months after rehabilitative treatment. Our results showed that an intensive rehabilitative process, in sub-acute stroke, could be useful for a good outcome of language deficits. HD-EEG results showed that left parieto-temporol-frontal areas were more activated after 2 months of rehabilitation training compared with baseline. Our results provided evidence that an intensive rehabilitation process could contribute to an inter- and intra-hemispheric reorganization.