IFML Seminar: Mutual Learning in Brain-Machine Interfaces
José del R. Millán, Professor, Department of Neurology at Dell Medical School, Associate Director of Texas Robotics-
The University of Texas at Austin
A brain-machine interface (BMII) is a system that enables users to interact with computers and robots through the voluntary modulation of their brain activity. Machine learning is certainly at the heart of a BMI, but building decoders need to cope with the non-stationary nature of brain signals and the fact that the user learns to volitionally modulate them. I will argue that a successful ML approach in BMI is the one that supports this user's learning process. In this talk I will review some of our recent studies, most involving participants with severe motor disabilities controlling complex devices, that illustrate mutual learning in BMI --the user and the machine learn from each other. In particular, I will discuss our BMI-based approach to motor rehabilitation, where an exclusive focus on increasing decoding performance is not necessarily the desired aim.
Dr. José del R. Millán is a professor and holds the Carol Cockrell Curran Endowed Chair in the Department of Electrical and Computer Engineering at The University of Texas at He is also a professor in the Department of Neurology at Dell Medical School and faculty of the Mulva Clinic for the Neurosciences. He is co-director of the UT CARE Initiative and associate director of Texas Robotics.
He received a PhD in computer science from the Technical University of Catalonia, Barcelona. Previously, he was a research scientist at the Joint Research Centre of the European Commission in Ispra (Italy) and a senior researcher at the Idiap Research Institute in Martigny (Switzerland). Most recently, he held the Defitech Foundation Chair in Brain-Machine Interface at the École Polytechnique Fédérale de Lausanne in Switzerland (EPFL), where he helped establish the Center for Neuroprosthetics. Dr. Millán has made several seminal contributions to the field of brain-machine interfaces (BMI), especially based on electroencephalogram signals. Most of his achievements revolve around the design of brain-controlled robots. He has received several recognitions for these seminal and pioneering achievements, notably the IEEE-SMC Nobert Wiener Award in 2011, elevation to IEEE Fellow in 2017, and elected Fellow of the International Academy of Medical and Biological Engineering in 2020. In addition to his work on the fundamentals of BMI and design of neuroprosthetics, Dr. Millán is prioritizing the translation of BMI to people who live with motor and cognitive disabilities.