Dr.-Ing. Aureli Soria-Frisch received the BSc degree from University Ramon Llull (1992), the MSc from the Polytechnic University of Catalonia– UPC (1995), and the PhD from the Technical University Berlin. He is R&D Manager and his research interest is focused on the fields: data and multi-sensory fusion, computational intelligence for data analysis, and machine learning for electrophysiological signal analysis. He was Project Manager of the FP7 HIVE project, and PI of the MJFF grant for the development of Machine Learning PD biomarker discovery. He is coordinator of the H2020 FET Open project LUMINOUS.
EEG for Decision Support in Diagnosis and Prognosis
Electroencephalography (EEG) data can be used to extract useful markers to study the brain in healthy and pathologic conditions, and modern signal processing, machine learning, and artificial intelligence methods offer news opportunities in the area of Digital Health. Our vision is that cost effective and easy to use EEG based analysis systems will soon be used in clinical settings for decision support in the diagnosis, early detection, and prognosis of mental and neurologic diseases. We aim to develop and implement a Decision Support System (DSS) based on non-invasive, low-cost EEG markers integrating data acquisition, algorithmic evaluation of electrophysiological biomarkers, and decision support information based significance level and effect size. This can be combined with modern visualization and reporting tools for biomarker illustration and presentation of test results. We will present results of advanced Machine Learning (ML) techniques for the analysis of in different use cases. First ML allows for the classification of these patients according to their ultimate diagnosis, differentiating those who develop 8 years later on average a neurodegenerative disease (PD or DLB) from those who do not. Other examples of data analysis results in Disorders of Consciousness and ADHD will be shown.
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