Neuromatic Game Art

Critical Play With Neurointerfaces


Ludic Lunchtalk: Moritz Grosse-Wentrup – Brain-Artificial Intelligence Interfaces

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.