[Hplusroadmap] Fwd: [tt] Florida BCI update

[Bryan Bishop]

Hm. How about an open source software project or two for these BCIs?

http://heybryan.org/docs/neuro/
http://heybryan.org/mediawiki/index.php/Brain_implants

On Wednesday 25 June 2008, KurzweilAI.net wrote:
> *************************
> Researchers develop neural implant
> that learns with the brain
> KurzweilAI.net June 25, 2008
> *************************
> University of Florida researchers
> have designed a brain-computer
> interface (BCI) that can adapt to a
> person's behavior over time, helping
> complete a task more efficiently.
> When they tested the BCI on rats
> (allowing the rat to move a robotic
> arm using thought alone), the system
> helped the rats become increasingly
> efficient at moving the robot...
> http://www.kurzweilai.net/email/newsRedirect.html?newsID=8932&m=40924

http://www.news.health.ufl.edu/news/story.aspx?ID=5103

Researchers develop neural implant that learns with the brain 


April Frawley Birdwell 
06/24/2008   

University of Florida researchers Jack DiGiovanna, left, and Justin C. 
Sanchez 

Photo available in the Image Gallery

Devices known as brain-machine interfaces could someday be used 
routinely to help paralyzed patients and amputees control prosthetic 
limbs with just their thoughts. Now, University of Florida researchers 
have taken the concept a step further, devising a way for computerized 
devices not only to translate brain signals into movement but also to 
evolve with the brain as it learns.

Instead of simply interpreting brain signals and routing them to a 
robotic hand or leg, this type of brain-machine interface would adapt 
to a person's behavior over time and use the knowledge to help complete 
a task more efficiently, sort of like an assistant, say UF College of 
Medicine and College of Engineering researchers who developed a model 
system and tested it in rats. 

Until now, brain-machine interfaces have been designed as one-way 
conversations between the brain and a computer, with the brain doing 
all the talking and the computer following commands. The system UF 
engineers created actually allows the computer to have a say in that 
conversation, too, according to findings published this month online in 
the Institute of Electrical and Electronics Engineers journal IEEE 
Transactions on Biomedical Engineering.

"In the grand scheme of brain-machine interfaces, this is a complete 
paradigm change," said Justin C. Sanchez, Ph.D., a UF assistant 
professor of pediatric neurology and the study's senior author. "This 
idea opens up all kinds of possibilities for how we interact with 
devices. It's not just about giving instructions but about those 
devices assisting us in a common goal. You know the goal, the computer 
knows the goal and you work together to solve the task."

Scientists at UF and other institutions have been studying and refining 
brain-machine interfaces for years, developing and testing numerous 
variations of the technology with the goal of creating implantable, 
computer-chip-sized devices capable of controlling limbs or treating 
diseases. 

The devices are programmed with complex algorithms that interpret 
thoughts. But the algorithms, or code, used in current brain-machine 
interfaces don't adapt to change, Sanchez said. 

"The status quo of brain-machine interfaces that are out there have 
static and fixed decoding algorithms, which assume a person thinks one 
way for all time," he said. "We learn throughout our lives and come 
into different scenarios, so you need to develop a paradigm that allows 
interaction and growth."

To create this type of brain-machine interface, Sanchez and his 
colleagues developed a system based on setting goals and giving 
rewards.

Fitted with tiny electrodes in their brains to capture signals for the 
computer to unravel, three rats were taught to move a robotic arm 
toward a target with just their thoughts. Each time they succeeded, the 
rats were rewarded with a drop of water. 

The computer's goal, on the other hand, was to earn as many points as 
possible, Sanchez said. The closer a rat moved the arm to the target, 
the more points the computer received, giving it incentive to determine 
which brain signals lead to the most rewards, making the process more 
efficient for the rat. The researchers conducted several tests with the 
rats, requiring them to hit targets that were farther and farther away. 
Despite this increasing difficulty, the rats completed the tasks more 
efficiently over time and did so at a significantly higher rate than if 
they had just aimed correctly by chance, Sanchez said. 

"We think this dialogue with a goal is how we can make these systems 
evolve over time," Sanchez said. "We want these devices to grow with 
the user. (Also) we want users to be able to experience new scenarios 
and be able to control the device."

Dawn Taylor, Ph.D., an assistant professor of biomedical engineering at 
Case Western Reserve University, said the results of the study add a 
new dimension to brain-machine interface research. That UF researchers 
were able to train rats to use the robotic arm and then obtain 
significant results from animals lacking the mental prowess of primates 
or humans is also impressive, she said. 

"It's a clear demonstration of a methodology that will work in 
situations when other implementations would fall apart," Taylor said. 

To develop and test this brain-machine interface system, Sanchez 
collaborated with engineering professors Jose Principe, Ph.D., and Jose 
Fortes, Ph.D., and engineering doctoral students Jack DiGiovanna and 
Babak Mahmoudi. 

The researchers received funding for the study from the National Science 
Foundation, the Children's Miracle Network and the UF Alumni 
Association.

- Bryan
________________________________________
http://heybryan.org/