prof. Lars Kai Hansen heads DTU Compute's Section for Cognitive Systems at Technical University of Denmark. His research focuses on machine learning with applications in bio-medicine, digital media and cognitive systems - he has published more than 300 papers on these subjects. The major contribution include introduction and analysis of neural network ensembles (1990-96); the first application of predictive machine learning for mind reading in PET (1994-) and fMRI(1997-); the concept of cognitive components(2005-); the cure for variance inflation (2001,2011-). His recent work concerns real-time attention monitoring using EEG.
His lecture is titled "Machine learning for brain state monitoring based on EEG.".
Title: Machine learning for brain state monitoring based on EEG
Abstract: Electroencephalography (EEG) is a widely used non-invasive technology for brain state monitoring. The EEG signal is complex and confounded by artifacts, hence modelling and decision making based on EEG is a serious challenge. I will review progress in machine learning for EEG analysis and applications including attention monitoring, sleep scoring, microstate inference for biomarkers in computational psychiatry.