[Seminar] Cure may be difficult, repair may be doable - brain machine interface

가톨릭관동대학교 국제성모병원 의학과
Friday, February 8th 2019, 2:00pm - Friday, February 8th 2019, 3:00pm
138동 415호

호스트: 장병탁 교수(x1833,880-1833)


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 can’t 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.

Speaker Bio

<학력> 로체스터 대학 (University of Rochester, Rochester, NY) 이학박사, 뇌인지과학 (Brain and Cognitive Sciences) 07.03 서울대학교 공학석사, 인지과학 99.02 서울대학교 공학사, 원자핵공학 97.02

<경력> 대구경북첨단의료산업진흥재단 책임연구원 (첨단의료기기개발지원센터 의료영상팀장) 12.12~18.07 피츠버그대학 (University of Pittsburgh, PA) Research Associate at System Neuroscience Institute 11.07~12.10 Postdoctoral associate at Dept. of Neurobiology 07.02~11.06 서울대학교 병원 보조 연구원 (신경과) 99.03~00.05