Brain-Machine Interfaces: The Perception-Action Closed Loop

Monday, March 27, 2017
11:00 AM to 12:00 PM
POB 2.302
Free and open to the public

Future neuroprosthetics will be tightly coupled with the user in such a way that the resulting system can replace and restore impaired upper limb functions because controlled by the same neural signals than their natural counterparts. However, robust and natural interaction of subjects with sophisticated prostheses over long periods of time remains a major challenge. To tackle this challenge we can get inspiration from natural motor control, where goal-directed behavior is dynamically modulated by perceptual feedback resulting from executed actions.

Current brain-machine interfaces (BMI) partly emulate human motor control as they decode cortical correlates of movement parameters --from onset of a movement to directions to instantaneous velocity-- in order to generate the sequence of movements for the neuroprosthesis. A closer look, though, shows that motor control results from the combined activity of the cerebral cortex, subcortical areas and spinal cord. This hierarchical organization supports the hypothesis that complex behaviours can be controlled using the low-dimensional output of a BMI in conjunction with intelligent devices in charge to perform low-level commands.

A further component that will facilitate intuitive and natural control of motor neuroprosthetics is the incorporation of rich multimodal feedback and neural correlates of perceptual processes resulting from this feedback. As in natural motor control, these sources of information can dynamically modulate interaction.

I will illustrate these principles and approach with a variety of brain-controlled robots and devices that have been extensively tested by users, many of them with severe motor disabilities.

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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.