Affilliation: Northwestern University Feinberg School of Medicine
Marc Slutzky, MD, PhD, is an Associate Professor of Neurology, Physiology, and Physical Medicine & Rehabilitation at Northwestern University Feinberg School of Medicine. Dr. Slutzky earned his BS with Highest Honors in Electrical Engineering from the University of Illinois from Urbana-Champaign. He then earned his MD and PhD in Biomedical Engineering from Northwestern University. He completed a Neurology residency at Northwestern University, and joined the faculty there in 2006, where he directs the Slutzky Neuroprosthetics Laboratory. His laboratory’s research centers around the use of neural prostheses to improve function in patients with motor impairments from neurological disorders including stroke, traumatic brain injury, spinal cord injury, and amyotrophic lateral sclerosis. In particular, he is interested in translating brain machine interfaces and myoelectric computer interfaces into viable solutions that can improve communication and motor function in such patients. He is a Fellow of the American Neurological Association, and takes care of general neurology patients at Northwestern. He is an Associate Editor at Frontiers in Neuroprosthetics and served as Guest Editor of Neurobiology of Disease.
Talk Title: Developing clinically viable brain machine interfaces: progress and challenges in selecting optimal input signals
Brain machine interfaces (BMIs), also called brain computer interfaces, have the potential to revolutionize care for patients with motor impairments from disorders including stroke, traumatic brain injury, spinal cord injury, and amyotrophic lateral sclerosis. Motor BMIs translate brain signals into commands for an external effector, such as a cursor or prosthetic limb. However, it is still not clear what input signal source is optimal to use for BMIs—whether to use noninvasive signals like EEG or more informative signals that can be recorded invasively. For a BMI to be clinically viable, we must consider not just signal quality and invasiveness, but also stability and longevity. The most informative signals, intracortical action potentials, are also the most difficult to record for long periods of time with current technologies. We have shown that intracortical local field potentials (LFPs) are highly stable for at least a year at a time, and thus may represent a more viable signal source for BMIs, at least in the short term. This talk will describe our investigations of the capabilities of multiple different electrical signal sources for BMIs. It will also examine challenges in the coming years to developing clinically viable BMIs. Ultimately, BMI device design, and corresponding choice of input signal source, should be tailored to the desired function of the BMI.