“Machine Learning Approaches for EEG-derived Early-onset Dementia Neuro-biomarker Development”
September 8, 2022 | Time 13:30 CET
Brain-computer interface (BCI) and efficient machine learning (ML) algorithms belonging to the so-called `AI for social good’ domain contribute to the well-being improvement of patients with limited mobility or communication skills. We will review our recent results from a project focusing on developing a dementia digital neuro-biomarker for early-onset prognosis of a possible cognitive decline utilizing a passive BCI approach. We will report findings from elderly volunteer pilot study groups analyzing EEG responses in a classical short-term memory evaluating oddball paradigms, reminiscent images, and emotional evaluation implicit learning tasks. Results using feature engineering approaches using signal complexity/criticality and information geometry employing Riemannian geometry tools, as well as end-to-end training machine models, will be discussed. The reported pilot studies showcase the vital application of artificial intelligence (AI) for an early-onset mild cognitive impairment (MCI) prediction in the elderly.
Tomasz (Tomek) M. RUTKOWSKI is an applied AI/ML research scientist at the RIKEN Center for Advanced Intelligence Project (AIP), a research fellow at The University of Tokyo, Japan and Nicolaus Copernicus University in Torun, Poland. Tomasz’s research interests include computational neuroscience, primarily passive brain-computer interfacing (BCI) dementia biomarkers elucidation, computational modeling of brain processes, and AI for social good applications. More information and publications are available at http://tomek.bci-lab.info/