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. http://neural.engineering/