Brain-Machine Interfaces: Beyond Decoding
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 the talk, Dr. José del R. Millán will review 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, Dr. Millán 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, Dr. Millán will discuss how to enhance subject learning and BMI performance through appropriate feedback modalities. Finally, Dr. Millán 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.
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Instructor
Dr. José del R. Millán
Dr. José del R. Millán is Carol Cockrell Curran Endowed Chair Professor, Dept. of Electrical and Computer Engineering & Dept. of Neurology at The University of Texas at Austin.
Publication Year: 2021