{"coupon":"","discount":"","doc_name":"Research Statement","doc_id":68109,"editorId":"65","email":"stahn@gist.ac.kr","message":"\uf06c Research Accomplishments<br \/>My research focused on the development of novel signal processing technique, particularly from various biomedical imaging data. First of all, I was participated in analyzing EEG data analysis from human motor imagery task in order to establish closed-loop brain-computer interface (BCI) system. Sensorimotor rhythm (SMR) derived from motor imagery task (imagine a hand movement) is the key feature for patients who have a severe disorder such as paralysis. However, I was faced with critical challenge which is having low reliability thus some users fail to generate the control signal in the brain (Blankertz et al., 2010). My novel approach to solve this problem is incorporating a tactile stimulation (Ahn et al., 2014) with motor imagery because kinesthetic motor imagery has a strong relationship with somatosensory excitability, and somatosensory spatial attention modulates SMR like motor imagery. As a result, my proposed approach outperformed the others and I developed new feature extraction method from closed-loop tactile-based BCI system. Furthermore, I thoroughly surveyed existing approaches and newly suggest for future direction of closed-loop tactile stimulation-based EEG BCI system in terms of experimental paradigms and feature extractions (Ahn et al., 2016a). In addition to EEG data analysis, I attempted to combine hemodynamic changes from functional near-infrared spectroscopy (fNIRS) with EEG features to explore neuro-physiological correlates of driving performance caused by sleep deprivation. It is known that sleep deprivation yields a reduction of sigma power density and I hypothesize that driving performance will be changed according to sleep conditions. I found out clear differences in EEG spectral power and hemodynamic changes from fNIRS and then I proposed a new index which clearly classify in two different sleep conditions by combining features from EEG and fNIRS (Ahn et al., 2016b).<br \/><br \/>\uf06c Current Works<br \/>Recently, I am working on investigations of effects of external stimulations during sleep using EEG data. Sleep disorders are serious enough to interfere with normal physical, mental, social and emotional functioning. Polysomnography is well-known measurement but more detailed EEG analysis is need to for monitoring and treating the sleeping subjects. Final goal of current project is developing a closed-loop monitoring system that extracts EEG patterns according to sleep stage and effectively stimulates tactile or auditory stimulations for generating slow wave sleep or other sleep spindles. Whole head EEG electrodes were used to record the electrical activities during sleep and its significant results were obtained by temporal- spectral- spatial-analysis. Some researchers found out an auditory stimulation such as white noise generated slow wave sleep more compared to normal sleep. Further, I am investigating optimal external stimulations depending on sleep stage since influences of stimulations differ from stage to stage. Sleep is also closely related to memory consolidation and I am trying to find a relationship between sleep spindles and long-term memory. Evaluation for performance of memory consolidation during different sleep conditions can be a key clue to investigate the mechanism of sleep.<br \/><br \/>","price_select":"2","temp_doc_name":"upload_576b6ecd9f84d4.12893277.docx","word_count":"481","subtotal":9.62,"total_amt":9.62,"transactionReference":"PAY-7TW87200CV9269123K5VW5VY"}