Three major projects launched during 2016, with the collaboration of several researchers globally, concerning the Alzheimer’s Disease (AD), the Autism Spectrum Disorders (ASD), and the application of Point cloud Techniques in Endoscopic Visualization.
In the first case, a new method was designed for the early diagnosis of AD based on a complex Bayesian model and the current classification of the disease. In the second case, a new software was presented for a digitalized Art Therapy and statistical analysis in EEG measurements of patients with ASD. Finally, the R&D department implemented a new set of algorithms for the application of the PCL in endoscopic products, offering 3D visibility and pattern recognition in biomedical applications.


The real etiology of Alzheimer’s disease (AD) is still unclear while several risk factors have been recognized to catalytically affect the early onset and the progression of the disease. According to latest studies, AD can be categorized according to risk factors, symptoms, and pathophysiological lesions into 8 different categories. Furthermore, these 8 categories can be analyzed in depth, by adding potential biomarkers in each category which have been proved to affect the severity of the disease.

How it works

The proposed Bayesian Network has been designed according to the latest ‘Research criteria for the diagnosis of Alzheimer’s disease: revising the NINCDS-ADRDA criteria’ and the model exports for every AD category the maximum probability value given the biomarkers evidence. The proposed statistical model is multi-parametric, relating several heterogeneous data like plasma and CSF tests, behavioral or imaging tests as categorical variables through prior categorical distributions.

NASESE software

The inclusion of NASESE software in a therapeutic session involves a neurologist, a therapist and the patient who continuously exchange data within networking electronic devices and an EEG portable device


The Neurocognitive Assessment Software for Enrichment Sensory Environments (NASESE) is based on the JAVA and Processing programming languages and detects, displays and analyzes EEG signals for individual or a group of patients. This neuroinformatics software offers a digital painting environment and a real-time transformation of EEG signals into adjustable music volume and octave configuration per electrode, for the real-time observation and evaluation of therapeutic procedures. The EEG acquisition is wireless, therefore, brain data can also be collected from the application of other sensory or sensorimotor therapeutic sessions as well. NASESE includes two main functionalities, organized in five different modules. The first functionality includes recording, filtering and visualization of the EEG signals exported to a rotating 3D brain model and a real-time transformation of brain activity to sound sculptures, while the second functionality generates statistical tests and coherence calculation in a fully customizable computerized environment.