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Neurological diagnosis research
Based on the electroencephalogram (EEG) in this laboratory, research has been conducted to help diagnose brain diseases, or to help with rehabilitation and prevention.
EEG-based active stroke exercise rehabilitation
Brain wave and biosignal analysis of game addiction group
Early diagnosis of MCI and dementia based on EEG
epilepsy diagnosis
EEG-based active stroke exercise rehabilitation study
Existing stroke exercise rehabilitation has a limitation in that it concentrates on upper and lower extremity rehabilitation exercises. Therefore, this laboratory conducted a study to develop a BCI-based self-directed exercise rehabilitation system that directly induces brain plasticity. To create a new sensory motor feedback loop after a stroke injury, we created a BCI-based real-time upper extremity motor assistance system.
< Existing rehabilitation system >
< BCI-based upper extremity exercise assistance system >
A study was started for the diagnosis, treatment, and prediction of stroke patients, and upper extremity rehabilitation (Grasping, supination, MI / An EEG measurement experiment was performed during active and passive tasks). This study was conducted jointly with the Department of Rehabilitation Medicine, Samsung Medical Center.
By measuring the degree of cognitive engagement during motor rehabilitation, it was proved that active rehabilitation involving the patient's intention is more effective than passive rehabilitation through a rehabilitation device. (IEEE TNSRE 2015)
[ERD of active movement (with motor intention) is significantly stronger than that of passive movement (without motor intention) in beta frequency, Detect active engagement with the accuracy of single trial classification > 80% ]
We also reported the relationship between brain network characteristics and the degree of damage in stroke patients.
[Phase-locking synchronization to see connectivity between electrodes]
[Meaningful correlation between node degree & local efficiency of ipsilesional M1 (C3) with motor function score in initial execution (PLOS ONE 2015) ]
[The betweenness centrality in the contralesional motor area (FC4) indicated high reliable prognostic predictors for the stroke motor function (Frontiers in Neuroscience- revision) ]
A study on the difference in EEG according to the lesion caused by stroke (JNER 2016)
[Patient subgroups classified by their lesion locations showed distinctly different patterns of cortical neural activation, Supratentorial lesions with M1, supratentorial lesions without M1, and infratentorial lesions, EEG spectral analyses should be implemented for patients with stroke after considering the lesion location]
A Study on the Prediction of Exercise Rehabilitation Results through EEG Analysis of Subacute Patients (10 patients, average age 58 years, within 3 weeks of onset) (EMBC, 2016)
[ Brain changes from overactivation during the subacute phase to ipsilesional, SM1-centered, moderated activation in the chronic phase , The overactivation-related EEG features observed in the subacute phase may be useful prognostic indicators to predict functional motor outcome (NNR submitted) ]
Brain wave and biosignal analysis of game addiction group
We conducted a project to develop a program to prevent and treat Internet game addiction based on virtual reality and biosignals. Through this project, real-time biosignal (central and autonomic nervous system) monitoring and feedback technology was developed, and as a result, a total of 6 game addiction treatment scenarios including motivational reinforcement training and high-risk coping training were developed. This project was carried out jointly with Gangnam Severance Hospital.
A biosignal acquisition experiment was performed on a total of 62 adolescents. Based on the Y-IAT (Young's Internet Addiction Test), normal and risk groups were divided. In addition, basic personal information and game-related surveys were conducted. EEG indices of risk groups related to addiction/craving were acquired using data such as EEG, PPG, and GSR, acquired while resting before/after the experiment, watching game videos and nature videos.
[ Relative delta, Theta/alpha increase (game stimulus), Relative alpha power decrease (game stimulus), Parieto-occipital gamma and craving degree showed significant correlations (Resting state) (SCAN submitted) ]
< VR-based game addiction brain wave measurement experiment protocol and experiment photos >
By conducting an experiment with three games as subjects (FIFA Online, Sudden Attack, and League of Legends), the preferred game showed a significant increase in Parieto-occipital theta power over the regular game. In addition, by confirming that the index showed a significant difference in the risk group compared to the normal group, it was confirmed that the preferred game induced craving more than the general game in the risk group. (SCAN, 2020)
EEG-based MCI and early diagnosis of dementia
We are conducting research to find new EEG biomarkers for early diagnosis of MCI and Alzheimer's disease by cognitive domain. We have constructed an experiment to measure EEG at the exact point in time according to task stimuli and responses for each of 8 cognitive areas, and through this, we want to predict the transition to dementia early by measuring changes in local brain activity according to brain area-specific cognitive stimulation. . This study is being conducted jointly with the Department of Neurology, Asan Medical Center, Korea University Anam Hospital.
We tried to explore an analysis method suitable for cognitive task characteristics and to detect indicators of ability for each cognitive domain.
[PSD, ERP, Source, Connectivity, Microstates, Phase-Amplitude coupling, etc.]
Epilepsy Diagnosis
We are conducting research to diagnose or predict the prognosis of Benign Rolandic Epilepsy (BRE) and Temporal Lobe Epilepsy (TLE). We are developing tools to help specialists diagnose and predict by recognizing abnormal brain signal patterns in sleep or non-sleep and performing statistical analysis. [Intericatal EEG: focal slowing, spikes (centro-temporal areas) detection algorithm and visualization] This study is being conducted in collaboration with the Department of Pediatric Neurology at Severance Hospital.
< EEG Abnormal Pattern Analysis Tool for Epilepsy Patients >