We developed a sleep inducing system using an auditory stimulus. Especially, we combined a 6 Hz binaural beat and an autonomous sensory meridian response (ASMR) trigger. The proposed auditory stimulus could induce both theta activity corresponding to non-rapid eye movement sleep 1 and psychological stability required for sleep.
Contact: C.-B. Song (cb_song@korea.ac.kr)
We investigated the difference of change between conscious experience and no conscious experience during non-rapid eye movement sleep. Dream could be detected using periods of conscious experience based on brain network
Contact: M.-J. Lee (minjilee@korea.ac.kr)
We made a?wireless portable fNIRS device which is robust to the motion artifact by detecting motions with a pressure sensor.
Contact: S.-H. Dong (zpfzpf123@korea.ac.kr)
We develop the technology of Neuro-feedback based brain training simulator system for stroke rehabilitation based on fNIRS. Finally, we try to offer accurate rehabilitation state and improvement level to stroke patients and medical team using our technology.
Contact: S.-H. Jin (jinjinsh@dgist.ac.kr)
In this study, we tried to design a novel brain-computer interface (BCI) framework that can decode decisions (movement intentions) and underlying strategies at the same time. We named this novel BCI as a meta-BCI?and classified the learning strategies with high accuracy (98%). Moreover, the movement intentions, which is not separable in a conventional BCI framework, has decoded with high accuracy (84%).
Contact: D.-J. Kim (kim10481@kaist.ac.kr)
This experiment paradigm collects emotional biosignals including EEG, ECG, and FACE during watching twelve selected emotional movie clips. In the experiment, there are?two sessions. Each session includes six movie clips, and the movie clips are arranged in?different orders in two sessions. Researchers can use the paradigm for gathering emotional biosignal states.
Contact: S. Hwang (sunny16@yonsei.ac.kr)
This study designed deep neural networks which extract spatio-temporal features in EEG. Our network predicts the probability of each task about an input, represented with a bar graph in this demo. In our experiments, our spatio-temporal network made good achievements for motor imagery related EEG classification and also gave a neurophysiological insight to spatial patterns.
Contact:?B.-C. Kim (bckim88@korea.ac.kr)
In BCI race game, the subject has to pass the 4 kinds of obstacles to reach the end running course. Subjects performed the 4-class motor imagery task. We decoded EEG signals and identified the subject’s intention using CNN.
Contact: J.-W. Kim (jw_kim@korea.ac.kr)
The aim of this study is to measure EEG-based SSVEPs for a brain-controlled exoskeleton under ambulatory conditions in which numerous artifacts may deteriorate decoding.
Contact: N.-S. Kwak (nskwak@korea.ac.kr)
We proposed a novel feature representation by combining spatial and spectral characteristics of brain signals for brain-controlled wheelchair, one of the major applications of BMIs.
Contact: K.-T. Kim (kim_kt@korea.ac.kr)