MIT researchers have developed a new algorithm to help create prosthetic devices that convert brain signals into action in patients who have been paralyzed or had limbs amputated.
The technique, described in a paper published as the cover article in the October edition of the Journal of Neurophysiology, unifies seemingly disparate approaches taken by experimental groups that prototype these neural prosthetic devices in animals or humans.
“The work represents an important advance in our understanding of how to construct algorithms in neural prosthetic devices for people who cannot move to act or speak,” said Lakshminarayan “Ram” Srinivasan (MIT S.M., Ph.D. '06), lead author of the paper.
Srinivasan, currently a postdoctoral researcher at the Center for Nervous System Repair at Massachusetts General Hospital and a medical student in the Harvard-MIT Division of Health Sciences and Technology (HST), began working on the algorithm while a graduate student in MIT's Department of Electrical Engineering and Computer Science (EECS).
Both trauma and disease can lead to paralysis or amputation, reducing the ability to move or talk despite the capacity to think and form intentions. In spinal cord injuries, strokes, and diseases such as amyotrophic lateral sclerosis (Lou Gehrig's disease), the neurons that carry commands from the brain to muscle can be injured. In amputation, both nerves and muscle are lost.
Neural prosthetic devices represent an engineer's approach to treating paralysis and amputation. Here, electronics are used to monitor the neural signals that reflect an individual's intentions for the prosthesis or computer they are trying to use. Algorithms form the link between neural signals that are recorded, and the user's intentions that are decoded to drive the prosthetic device.
Over the past decade, efforts at prototyping these devices have divided along various boundaries related to brain regions, recording modalities, and applications. The MIT technique provides a common framework that underlies all these various efforts.
The research uses a method called graphical models that has been widely applied to problems in computer science like speech-to-text or automated video analysis. The graphical models used by the MIT team are diagrams composed of circles and arrows that represent how neural activity results from a person's intentions for the prosthetic device they are using.
The diagrams represent the mathematical relationship between the person's intentions and the neural manifestation of that intention, whether the intention is measured by an electoencephalography (EEG), intracranial electrode arrays or optical imaging. These signals could come from a number of brain regions, including cortical or subcortical structures.