Brain-Machine Interfaces: Beyond Decoding

Wednesday, November 29, 2017
1:30 PM to 2:30 PM
EER 3.646
Free and open to the public

Real-time signal processing and decoding of brain signals are certainly at the heart of a brain-machine interface (BMI). Yet, this does not suffice for subjects to control a BMI. In the first part of my talk I will review a few recent studies, most involving users with severe motor disabilities, that illustrate additional principles of a reliable BMI. In the second part, I will show how BMI are not just opening the door to advanced assistive devices, but can also lead to innovative rehabilitation interventions to recover from brain lesions and augmented interaction. Finally, I will put forward some visions for the future of BMIs and share preliminary results that support them.

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José del R. Millán

Defitech Foundation Chair
École Polytechnique Fédérale de Lausanne

Dr. José del R. Millán joined the École Polytechnique Fédérale de Lausanne (EPFL) in 2009, where he holds the Defitech Foundation Chair and directs the Brain-Machine Interface Laboratory. He received a PhD in computer science from the Technical University of Catalonia, Barcelona, in 1992. 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). He has also been a visiting scholar at the Universities of Berkeley and Stanford as well as at the International Computer Science Institute in Berkeley.

Dr. Millán has made several seminal contributions to the field of BMI, especially based on electroencephalogram (EEG) signals. Most of his achievements revolve around the design of brain-controlled robots. He has received several recognitions for these seminal and pioneering achievements, most recently the IEEE-SMC Nobert Wiener Award in 2011 and elevation to IEEE Fellow in 2017.

He puts a strong emphasis on the use of statistical machine learning and human-machine interaction techniques so as to achieve a seamless coupling between the user and the brain-controlled device. A key element is the design of efficient and robust algorithms for real-time decoding of patterns of brain activity associated to different aspects of voluntary behaviour. He also builds on neuroscience findings to design new interaction protocols to operate complex devices. During the last years he 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. In parallel, he is designing BMI technology to offer new interaction modalities for able-bodied people.