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 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.
Portable neuroscience: using devices and apps for diagnosis and treatment of neurological disease
Scientists work in laboratories; comfortable spaces which we equip and configure to be ideal for our needs. The scientific paradigm has been adopted by clinicians, who run diagnostic tests and treatments in fully equipped hospital facilities. Yet advances in technology mean that that increasingly many functions of a laboratory can be compressed into miniature devices, or even into a smartphone app. This has the potential to be transformative for healthcare in developing nations, allowing complex tests and interventions to be made available in every village. In this talk, I will give two examples of this approach from my recent work. In the field of stroke rehabilitation, I will present basic research which we have conducted in animals over the last decade. This reveals new ways to intervene and strengthen surviving pathways, which can be deployed in cheap electronic devices to enhance functional recovery. In degenerative disease, we have used Bayesian statistical methods to improve an algorithm to measure how rapidly a subject can stop an action. We then implemented this on a portable device and on a smartphone app. The measurement obtained can act as a useful screen for Parkinson’s Disease. I conclude with an outlook for the future of this approach, and an invitation to those who would be interesting in collaborating in rolling it out to in African settings.