The decoding of higher order cognition directly from recordings of neural activity in the brain could enable a new generation of prosthetic devices. Accurate information about memory processes, reward perception and attempted speech and motor activity will allow targeted interventions and next-level human-computer interaction. In this presentation, I will present work with neurological patients that have electrodes implanted deep into their brains for clinical procedures. By piggybacking on these clinical routines, we are able to record high-fidelity neural activity across a variety of brain areas and align them to cognitive tasks. Through the application of machine learning, we are able to decode higher-order cognition from these recordings and process the output in real-time.
About the speaker:
Christian Herff is an Assistant Professor in the Department of Neurosurgery at Maastricht University where he heads the Neural Interfacing Lab. His research interests lay in the decoding of higher-order cognition from neural signals to combine natural with artificial intelligence. Christian has a background in Computer Science, which he studied at the Karlsruhe Institute of Technology, IIT Delhi and NTU Singapore. He obtained his PhD in computer science from the University of Bremen. His research attracted national and international funding including an NOW VENI in 2019.