Measurement in Transition of Consciousness under Sedation

We examined network changes using graph theoretical analysis of high-density EEG during patient-titrated propofol-induced sedation. Our findings provide novel insights into the neural correlates of these behavioural transitions and EEG signatures for monitoring the levels of consciousness under sedation.

Contact: M. Lee (minjilee@korea.ac.kr)

 

Self-Regulated Neurofeedback Training for BCI Illiteracy

The aim of this study is to predict BCI illiteracy. BCI illiteracy is estimated in the resting state and then we measure the accuracy by performing the RSVP task. If the accuracy is low, the subjects would perform the brain signal expression training based on neuro feedback.

Contact: K. Won (kyunghowon0712@gist.ac.kr)

 

Estimation of Context-dependent Feature based on CNN-CAM

This study aims to design EEG meta-BCI system that estimates a learning strategy of the subject during performing two-stage MDP. This model utilizes context data extracted by arbitration model to train CNN-CAM deep learning model. Well trained meta-BCI is able to estimate not only subject’s learning strategy but also visualize context dependent EEG features.

Contact: Dongjae Kim (kim10481@kaist.ac.kr)

 

Brain/Bio Signal Measurement System of Natural Conscious

The aim of this study is to construct multimodal-based simultaneous brain/bio signal measurement system with the natural conscious states. This experiment simultaneously measured two brain signals (EEG, NIRS) and two biological signals (ECG, PPG). Triggers were generated by pressing the buttons (right button, left button) according to the auditory stimuli (‘right’, ‘left’) to distinguish the natural state of consciousness. 

Contact: S.-T. Yang (hilton99@dgist.ac.kr)

 

Multimodal BCI System based on EEG and fNIRS

Twenty-six healthy participants performed three cognitive tasks: 1) n-back (0-, 2- and 3-back), 2) Discrimination/Selection Response task (DSR) and 3) Word Generation (WG) tasks. The potential merit of hybrid EEG-NIRS BCIs was validated with respect to classification accuracy.

Contact: H.-J. Hwang (h2j@kumoh.ac.kr)

 

Detection of Emergency Situations based on BCI during Driving

The aim of this study is to develop a simulated driving environment for studying neural correlates of emergency braking in diversified driving situations. We investigated to what extent these neural correlates can be used to detect a participantʼs braking intention prior to the behavioral response.

Contact: J.-W. Kim (jw_kim@korea.ac.kr)

 

Wireless Portable fNIRS System Measured Prefrontal Cortex

This study investigated the possibility of wireless and portable functional Near-Infrared Spectroscopy (fNIRS) in a real world with optimal customization. Our device shows fast and stable hemodynamic change in a prefrontal cortex without any uncomfortability or large noise caused by motion artifact.

Contact: S. Dong (zpfzpf123@korea.ac.kr)

 

P300 BCI Experiment Paradigm for Open Database

This experiment paradigm collects bio signals, including EEG, EOG, ECG, and electrodes coordinate during resting state, RSVP task, and P300 BCI. Researchers can use the paradigm for investigating P300 BCI performance predictor.

Contact: K. Won (kyunghowon0712@gist.ac.kr)

 

BCI-based Speller using Visual Stimulation

This study investigated the effect of stimulation frequency and duty-cycle on the usability of a SSVEP-based BCI system. These results suggest that the use of higher frequency visual stimuli is more beneficial for performance improvement and stability as time passes when developing practical SSVEP-based BCI applications.

Contact: D.-O. Won (wondongok@korea.ac.kr)