The Next Generation of Brain-Computer Interfaces: Responding Implicitly to Learners' Cognitive State
Colloquium | April 30 | 4-5:30 p.m. | 2515 Tolman Hall
Beste Yuksel, University of San Francisco
The human and computer are both complex machines, capable of sophisticated functions, yet there is a very narrow bandwidth of communication between them. A new generation of brain computer interfaces (BCIs) are currently being developed that can increase this communication bandwidth by passively detecting learners' cognitive state and responding appropriately in real-time. In this talk, I present my Best Paper Award work on a brain-computer interface that dynamically increases task difficulty in a musical learning task based on user's cognitive workload. Results showed that users can learn with increased speed and accuracy with the BCI. I will discuss the broader future implications of this next generation of user interfaces, including the addition of user affect in conjunction with cognitive workload.
Beste Filiz Yuksel is an Assistant Professor of Computer Science at the University of San Francisco where she is creating a Human-Computer Interaction teaching and research program. She received her Ph.D. in Computer Science from Tufts University, Boston, working with Prof. Robert Jacob. Her research was on the next generation of brain-computer interfaces (BCIs) that detect and evaluate real-time brain signals using machine learning classification of functional near infrared spectroscopy (fNIRS) to build adaptable user interfaces for the general population. Her work won a Best Paper Award at ACM CHI 2016. She has also worked with Mary Czerwinski at Microsoft Research, and with product teams at Microsoft, investigating user-virtual agent interactions for the next generation of intelligent personal assistants. Beste is currently working on building intelligent, adaptive interfaces that respond to both user cognitive and affective state in conjunction in her new Human-Computer Interaction Lab at USF. She is a great supporter of women and minorities in computer science.