Machine Learning for Brain Health and Understanding at Starlab Neuroscience

This conference is part of the AI4Health Lecture Series organised by the Department of Computer Science and the Department of Life Sciences and Medicine of the University of Luxembourg. The talk takes place online via this link on Webex

Abstract

The convergent development of different technologies is bringing the understanding of the brain, both on healthy and pathological condition, further than ever before. The confluence of Wearables, Neurotechnologies, Augmented and Virtual Reality, Serious Gaming, Data Science, Machine Learning and Artificial Intelligence are a game changer in the way we study brain functionality, make use of it for interacting with the environment, and treat mental and neurological disease. The talk will deal with the combination of Neurotechnologies, Machine Learning and Artificial Intelligence in different Digital Brain Health applications developed at Starlab Neuroscience. Digital markers of brain function will lead in the near future to improved diagnostic, drug discovery, risk analysis, and interactivity. We will show developed methodologies for: stratified performance evaluation of classifiers in operational conditions for Parkinsons’ risk assessment, differential diagnosis in ADHD based on Reservoir Computing, and new treatment outcome prediction in Coma patients. I will go over the technical challenges we faced to develop these applications, but also over some insights that influence the applicability of pure academic data science in the real world.

Speaker

Dr.-Ing. Aureli Soria-Frisch received the MSc from the Polytechnic University of Catalonia– UPC (1995), and the PhD from the Technical University Berlin. Between 1996 and 2005 he worked at the Fraunhofer IPK (Berlin), as research scientist and project leader. After 3 years as Visiting Professor at the Universitat Pompeu Fabra, he joined Starlab in 2008. He is the Director of the Neuroscience Business Unit since beginning 2017. His research interest is focused on the fields: computational intelligence for data analysis, and machine learning for electrophysiological signal analysis. He has authored 20 journal papers, seven book chapters, and over 60 conference papers. He has been Project Manager of the FP7 HIVE project, where the Starstim early prototype for transcranial electrical stimulation was developed, Project Coordinator of the H2020 FET Open LUMINOUS project on the clinical study of consciousness, and PI of the 2 MJFF grants for the development of Machine Learning PD biomarkers.