Digitale Kunst, Zentrum Fokus Forschung
Research Group
Neuroinformatics, Faculty of Computer Science, University of Vienna
Hosted by Univ.-Prof. Margarete Jahrmann,
Artistic research PhD program, PI NEUROMATIC GAME ART: Critical Play with Neurointerfaces
(AR581 FWF/PEEK) and Univ.-Prof. Dr.-Ing. Stefan Glasauer, Computational Neuroscience, Brandenburg Technical University Cottbus-Senftenberg. After registration You will receive a confirmation via E-Mail with information for the meeting.
The introduction of machine learning into the field of brain-computer interfaces (BCIs),
which began almost two decades ago, enabled unprecedented performance. Today, machine learning algorithms have become an indispensable
component of a BCI. Machine learning, however, has undergone a radical transformation in the past two decades, resulting in
artificial intelligence (AI) systems that surpass human performance in many real-world tasks. I argue that it is time for
the BCI community to embrace these developments and build Brain-AI Interfaces (BAIs), i.e., systems that leverage the power
of modern AI systems to enable natural human-computer interaction. In particular, I argue that to realize BAIs we will have
to move beyond our dominant decoding paradigm, in which we determine a priori the labels we intend to decode from neural signals,
and let the AI system decide the level of granularity at which cognitive processes are represented in neural signals.
Univ.-Prof.
Dr.-Ing. Moritz Grosse-Wentrup
Bio:
Moritz Grosse-Wentrup
is full professor and head of the Research Group Neuroinformatics at the University of Vienna, Austria. He develops machine
learning algorithms that provide insights into how large-scale neural activity gives rise to (disorders of) cognition, and
applies these algorithms in the domain of cognitive neural engineering, e.g., to build brain-computer interfaces for communication
with severely paralyzed patients, design closed-loop neural interfaces for stroke rehabilitation, and develop personalized
brain stimulation paradigms. He has received numerous awards for his work, including the 2011 Annual Brain-Computer Interface
Research Award, the 2014 Teaching Award of the Graduate School of Neural Information Processing at the University of Tübingen,
and the 2016 IEEE Brain Initiative Best Paper Award.