A brain-machine interface (BMI) is a system that enables users to interact with computers and robots through the voluntary modulation of their brain activity. Such a BMI is particularly relevant as an aid for patients with severe neuromuscular disabilities, although it also opens up new possibilities in human-machine interaction for able-bodied people.
Real-time signal processing and decoding of brain signals are certainly at the heart of a BMI. Yet, this does not suffice for subjects to operate a brain-controlled device. In the first part of my talk I will review some basic machine learning components that facilitate user learning as well. I will also discuss studies, most involving users with severe motor disabilities, showing that BMI is more than just decoding. A central theme is the need for a comprehensive mutual learning methodology that reinstates the three learning pillars of a brain-controlled device (at the machine, subject, and application level) as equally significant. Finally, I will discuss recent work in our laboratory illustrating how to enhance subject learning and BMI performance through appropriate feedback modalities.
Dr. José del R. Millán joined École Polytechnique Fédérale de Lausanne (EPFL) in 2009 to help establish the Center for Neuroprosthetics. He holds the Defitech Foundation Chair and directs the Brain-Machine Interface Laboratory. Dr. Millán has made seminal contributions to the field of brain-machine interfaces, especially based on electroencephalogram (EEG) signals. Most of his achievements revolve around the design of brain-controlled robots. During the last years Dr. Millán is prioritizing the translation of BMI to end-users suffering from motor disabilities. As an example of this endeavour, his team won the first Cybathlon BMI race in October 2016. Together with his team, he is also designing BMI technology to offer new interaction modalities for able-bodied people.