Seminar

Brain-Machine Interfaces: Beyond Decoding
Brain-Machine Interfaces: Beyond Decoding

15/Jun/2021
15/Jun/2021

Speaker:

José del R. Millán
José del R. Millán

Institution:

University of Texas at Austin
University of Texas at Austin

Language :

EN
EN

Type :

Webinar
Webinar

Description:

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 of our recent studies, most involving participants with severe motor disabilities, that illustrate additional principles of a reliable BMI that enable users to operate different devices. In particular, I will show how an exclusive focus on machine learning is not necessarily the solution as it may not promote subject learning. This highlights the need for a comprehensive mutual learning methodology that foster learning at the three critical levels of the machine, subject and application. To further illustrate that BMI is more than just decoding, I will discuss how to enhance subject learning and BMI performance through appropriate feedback modalities. Finally, I will show how these principles translate to motor rehabilitation, where in a controlled trial chronic stroke patients achieved a significant functional recovery after the intervention, which was retained 6-12 months after the end of therapy.

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 Austin. He is also a professor in the Department of Neurology of the Dell Medical School.
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). 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 end-users suffering from motor and cognitive disabilities. In parallel, he is designing BMI technology to offer new interaction modalities for able-bodied people.

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 of our recent studies, most involving participants with severe motor disabilities, that illustrate additional principles of a reliable BMI that enable users to operate different devices. In particular, I will show how an exclusive focus on machine learning is not necessarily the solution as it may not promote subject learning. This highlights the need for a comprehensive mutual learning methodology that foster learning at the three critical levels of the machine, subject and application. To further illustrate that BMI is more than just decoding, I will discuss how to enhance subject learning and BMI performance through appropriate feedback modalities. Finally, I will show how these principles translate to motor rehabilitation, where in a controlled trial chronic stroke patients achieved a significant functional recovery after the intervention, which was retained 6-12 months after the end of therapy.

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 Austin. He is also a professor in the Department of Neurology of the Dell Medical School.
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). 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 end-users suffering from motor and cognitive disabilities. In parallel, he is designing BMI technology to offer new interaction modalities for able-bodied people.