Brain-Computer Interface Lab
뇌-컴퓨터 인터페이스 연구실관련기사 바로가기
As a neuroengineering lab, we are conducting researches on how to establish interfaces to the brain to read out neural information for understanding brain mechanisms and developing technologies used for humans. Our research activities largely cover a number of specific topics. The research of intracortical brain-computer interfaces (BCIs) aims to understand how neuronal activities encode information and to develop a system that harnesses intracortical neuronal signals to actuate external systems. Specifically, we are working on developing a bi-directional BCI to control a robotic arm by reading motor cortical activities and at the same time, delivering somatosensory senses back to the brain by writing the code of senses directly to the somatosensory neurons. The non-invasive BCI research builds a similar BCI system, but using non-invasive brain signals such as electroencephalography (EEG) to control home appliances of daily use. The non-invasive BCI system targets a wider range of populations to allow people to gain brain control over a variety of devices such as a TV set, a refrigerator, a smart LED system, and so on. We aim to achieve this goal by combining BCIs with augmented reality (AR) technology. The neuromarketing research pursues to establish a set of tools to help understand the cognitive and affective states of consumers by reading and analyzing brain activities. The tactile intelligence research is concerned with building a model to learn tactile percepts from artificial tactile sensor signals, by mimicking the way the human somatosensory system works. Taking all these together, our lab ultimately aims to understand the brain better and utilize the brain information more effectively for human life.
brain-computer interface, neuromarketing, tactile neuroscience, neural decoding
neuron-inspired AI, brain-AI interface, autism, neural encoding, tactile intelligence
Research Keywords and Topics
brain-computer interface, neuromarketing, tactile neuroscience, neural decoding, neural encoding
Scientific Reports, "Shared neural representations of tactile roughness intensities by somatosensation and touch observation using an associative learning method," J. Kim, I. Buelthoff, S.-P. Kim, H.H. Buelthoff, 2019.
Frontiers in Neuroscience, "Group-level neural responses to service-to-service brand extension," T. Yang and S.-P. Kim, 2019.
Frontiers in Computational Neuroscience, "A spike train distance robust to firing rate changes based on the Earth Mover’s Distance," D. Sihn and S.-P. Kim, 2019.
신경 발화 패턴을 이용한 촉감의 모델링 방법, 촉감 모델 및 신경 발화 패턴을 이용한 촉감의 생성방법, 김성필, 박지성, 정승준, 장동표, 2018.09
영상컨텐츠의 몰입도 측정 방법 및 장치, 김성필, 강다윤, 조양석, 박완주, 2017.03
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