By Jose M. Carmena
NOTE: This is an overview of the article, which appeared in the March 2012 issue of the IEEE Spectrum magazine.
Click here to read the entire article.
Imagine a piece of technology that would let you control an apparatus simply by thinking about it. Lots of people, it turns out, have dreamed of just such a system, which for decades has fired the imaginations of scientists, engineers, and science fiction authors. It’s easy to see why: By transforming thought into action, a Brain-Machine Interface (BMI) could let paralyzed people control devices like wheelchairs, prosthetic limbs, or computers. Farther out in the future, in the realm of sci-fi writers, it’s possible to envision truly remarkable things, like brain implants that would allow people to augment their sensory, motor, and cognitive abilities.
This article reports on a fresh approach to the BMI. It takes advantage of the plasticity of the brain which allows it to develop a dedicated neural circuit, called a motor memory, for controlling a virtual device or robotic arm in a manner similar to the way it creates such memories for countless other movements and activities in life. The experiments described here demonstrated that learning to control a disembodied device is, for your brain, not much different from learning to ski or to swing a tennis racket. It’s this extraordinary plasticity of the brain, the author believes, that researchers should exploit to usher in a new wave of BMI discoveries that will finally deliver on the promises of this technology.
The melding of mind and machine took a jump forward in 1999, when John Chapin, Miguel Nicolelis, and their colleagues at the MCP Hahnemann School of Medicine, in Philadelphia, and Duke University, in Durham, N.C., reported that rats in their laboratory had controlled a simple robotic device using brain activity alone.After receiving a brain implant that recorded and interpreted activity in their motor cortices, the animals could just think about pressing the lever and the robotic arm would instantly give them a sip of water.
Researchers began unveiling proof-of-concept systems that demonstrated how rats, monkeys, and humans could control computer cursors and robotic prosthe- ses in real time using brain signals. BMI systems have also revealed new ways of studying how the brain learns and adapts, which in turn have helped improve BMI design. But despite all the advances, we are still a long way from a really dependable, sophisticated, and long-lasting BMI that could radically improve the lives of the physically disabled, let alone one that could let you see the infrared spectrum or download Wikipedia entries directly into your cerebral cortex.
The traditional approach to controlling a prosthetic limb is to create a decoder by monitoring the neural activity of the areas of the brain responsible for control of the natural limb. The decoder transforms neural activity into a small number of putputs (e.g., position, velocity, gripping force of the prosthetic device) similar to the function of the spinal cord.
By contrast, the author’s group at Berkeley set out to investigate an intriguing hypothesis based on a simple question: If we’re trying to control a prosthetic device that’s completely different from a natural arm, why are we relying on brain signals related to a natural arm? If we want to control an artificial arm, we’d ideally use brain activity tailored to that specific arm. But could the brain learn to produce such activity for something that’s not even part of the body? The answer is a resounding yes, according to a series of experiments and trials they’ve done over the past five years. In this new view of BMI design, the focus isn’t on using the existing nervous system to control an artificial device but rather on creating a new, hybrid nervous system that spans the biological and artificial components.
And in their most recent study, they went one step further. Their results showed that other brain areas that had not been explored in a BMI context – including neural circuits between the cortex and deep-brain structures such as the basal ganglia – are actually key to the learning of prosthetic skills. This means that in principle, learning how to control a prosthetic device using a BMI may feel completely natural to a person, because this learning uses the brain’s existing built-in circuits for natural motor control.
There are still many challenges and obstacles on the road to a truly practical BMI system. For instance, the implants need to be tiny, use very little power, and work wirelessly. The entire system needs to be reliable and work for the user’s entire lifetime.
Even after those challenges are met, there is a whole category of other obstacles involved with making the BMI system more sophisticated and capable. Ultimately, we want to build a BMI that can control not only primitive systems but also complex bionic prostheses with multiple degrees of freedom to perform dexterous tasks. And we want the BMI to be able to transmit signals from the brain to the prosthesis as well as from the prosthesis to the brain. That’s BMI’s holy grail: a system that’s part of your body in the sense that you not only control it but also feel it.
BMI research is entering a new phase – call it BMI 2.0 – thanks to work at universities, companies, and medical centers all over the world. There is a palpable sense that we are quite close to cracking several of the fundamental problems standing in the way of clinical and commercial use of BMI systems.
The initial applications of BMI, in helping patients suffering from paralysis due to spinal cord injury or other neurological disorders, including amyotrophic lateral sclerosis and stroke, are still probably a decade or two away. But after this technology becomes mainstream in health care, other realms await in the augmentation of sensory, motor, and cognitive capabilities in healthy subjects – a fascinating possibility for sure, but one that promises to unleash a big ethical debate. The world where we’re able to do a Google search or drive a car just by thinking will be a very different place. But that’s a BMI 3.0 story.
ABOUT THE AUTHOR
Jose M. Carmena is a professor of electrical engineering, cognitive science, and neuroscience at the University of California, Berkeley. While waiting for a flight at the Columbus, Ohio, airport in 1999, he came across a magazine article about the possibilities of brain-machine interfaces. “I decided I had to get into that field,” he recalls. He’s now codirector of the Center for Neural Engineering and Prostheses, a joint effort by Berkeley and the University of California, San Francisco.