Cure may be difficult, repair may be doable - brain machine interface

February 8, 2019, 2PM

138-415

 

Abstract:

The brain-machine interface (BMI) is a direct communication channel between a brain and an artificial device. It is known to the public as a technology that allows the brain to directly manipulate external devices, for example, a robot arm without connecting other parts of the body. In 1971, when the American scientist Vidal coined the name brain-computer interface (BCI) for the first time, public awareness for this terminology was low. As, however, a series of studies showing patients with limb paralysis manipulating a robot arm according to his or her intention around 2010, BCI received spotlight from the medias. In the early of 2017, the Facebook announced a plan to commercialize non-verbal brain-to-skin computer interface. Furthermore, Tesla's Elon Musk announced a plan to commercialize technology connecting human brain and computer. However, unlike the public expectations, experts point out that there are a number of hurdles that must be overcome in order for the BMI to be commercialized. Perhaps the area where the BMI is most likely to be applied is in the medical field. In the case of diseases, such as degenerative brain diseases, in which the underlying cure is difficult to find, the BMI would play an important role in terms of repair rather than cure. In this seminar, I will introduce the monkey experiment that is going on in my laboratory in this respect. In our experiment set up, monkey learned how to manipulate a robot arm and hand with its neural signal. Since we wanted to translate the result from monkey to human patients with paralysis seamlessly, we tried to mimic the situation where human patients canít move their own arm. For this, while the monkey was not allowed to move its arm, it observed a robot arm reaching to and grasping the target. We recorded single unit activities from the primary motor cortex of the animal. We could successfully apply this observation based neural data to the decoder of BMI.