[tt] MIT develops better algorithms for neural prosthetics

[Hughes, James J.]

http://www.scienceblog.com/cms/mit-aids-creation-neural-prosthetic-devic
es-14389.html

MIT aids creation of neural prosthetic devices

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.

Until now, researchers working on brain prosthetics have used different
algorithms depending on what method they were using to measure brain
activity. The new model is applicable no matter what measurement
technique is used, according to Srinivasan. "We don't need to reinvent a
new paradigm for each modality or brain region," he said.

Srinivasan is quick to underscore that many challenges remain in
designing neural prosthetic algorithms before they are available for
people to use. While the algorithm is unifying, it is not universal: the
algorithm consolidates multiple avenues of development in prostheses,
but it isn't the final and only approach these researchers expect to see
in the years to come. Moreover, energy efficiency and robustness are key
considerations for any portable, implantible bio-electronic device.

Through a better quantitative understanding of how the brain normally
controls movement and the mechanisms of disease, he hopes these devices
could one day allow for a level of dexterity depicted in movies, such as
actor Will Smith's mechanical arm in the movie I Robot.

The gap between existing prototypes and that final goal is wide.
Translating an algorithm into a fully functioning clinical device will
require a great deal of work, but also represents an intriguing road of
scientific and engineering development for the years to come.

Other authors on the paper are Uri Eden (Ph.D. '05), assistant professor
in Mathematics and Statistics at Boston University, Sanjoy Mitter,
professor in EECS and MIT's Engineering Systems Division, and Emery
Brown, professor in Brain and Cognitive Sciences, HST, and Anesthesia &
Critical Care at Massachusetts General Hospital. The cover image for the
October issue of Journal of Neurophysiology that depicts this research
was designed by Rene Chen (B.S. '07) and Eric Pesanelli (J.
Neurophysiol.).

This work was sponsored by the National Institutes of Health and the
National Science Foundation.

http://www.lmu.edu