Hyun-Chool Shin

Quantitative EEG and neurological recovery with therapeutic hypothermia after asphyxial cardiac arrest in rats.

We test the hypothesis that quantitative electroencephalogram (qEEG) can be used to objectively assess functional electrophysiological recovery of brain after hypothermia in an asphyxial cardiac arrest rodent model. Twenty-eight rats were randomly subjected to 7-min (n = 14) and 9-min (n = 14) asphyxia times. One half of each group (n = 7) was randomly subjected to hypothermia (T = 33 degrees C for 12 h) and the other half (n = 7) to normothermia (T = 37 degrees C). Continuous physiologic monitoring of blood pressure, EEG, and core body temperature monitoring and intermittent arterial blood gas (ABG) analysis was undertaken. Neurological recovery after resuscitation was monitored using serial Neurological Deficit Score (NDS) calculation and qEEG analysis. Information Quantity (IQ), a previously validated measure of relative EEG entropy, was employed to monitor electrical recovery. The experiment demonstrated greater recovery of IQ in rats treated with hypothermia compared to normothermic controls in both injury groups (P < 0.05). The 72-h NDS of the hypothermia group was also significantly improved compared to the normothermia group (P < 0.05). IQ values measured at 4 h had a strong correlation with the primary neurological outcome measure, 72-h NDS score (Pearson correlation 0.746, 2-tailed significance <0.001). IQ is sensitive to the acceleration of neurological recovery as measured NDS after asphyxial cardiac arrest known to occur with induced hypothermia. These results demonstrate the potential utility of qEEG-IQ to track the response to neuroprotective hypothermia during the early phase of recovery from cardiac arrest.

Quantitative EEG and effect of hypothermia on brain recovery after cardiac arrest

In this paper, we provide a quantitative electroencephalogram (EEG) analysis to study the effect of hypothermia on the neurological recovery of brain after cardiac arrest. We hypothesize that the brain injury results in a reduction in information of the brain rhythm. To measure the information content of the EEG a new measure called information quantity (IQ), which is the Shannon entropy of decorrelated EEG signals, is developed. For decorrelating EEG signals, we use the discrete wavelet transform (DWT) which is known to have good decorrelating properties and to show a good match to the standard clinical bands in EEG. In measuring the amount of information, IQ shows better tracking capability for dynamic amplitude change and frequency component change than conventional entropy-based measures. Experiments are carried out in rodents (n = 30) to monitor the neurological recovery after cardiac arrest. In addition, EEG signal recovery under normothermic (37/spl deg/C) and hypothermic (33/spl deg/C) resuscitation following 5, 7, and 9 min of cardiac arrest is recorded and analyzed. Experimental results show that the IQ is greater for hypothermic than normothermic rats, with an IQ difference of more than 0.20 (0.20 /spl plusmn/ 0.11 is 95% confidence interval). The results quantitatively support the hypothesis that hypothermia accelerates the electrical recovery from brain injury after cardiac arrest.

Mean-square performance of data-reusing adaptive algorithms

This letter provides a unified mean-square performance analysis of the class of data reusing adaptive algorithms. The derivation relies on energy conservation arguments, and it does not restrict the regression data to being Gaussian. Simulation results show that there is a relatively good match between theory and practice.

Towards a Brain-Computer Interface for Dexterous Control of a Multi-Fingered Prosthetic Hand

Advances in brain-computer interfaces (BCI) have enabled direct neural control of robotic and prosthetic devices. However, it remains unknown whether cortical signals can be decoded in real-time to replicate dexterous movements of individual fingers and the wrist. In this study, single unit activity from 115 task-related neurons in the primary motor cortex (Ml) of a trained rhesus monkey were recorded, as it performed individuated movements of the fingers and wrist of the right hand. Virtual multi-unit ensembles, or voxels, were created by randomly selecting contiguous subpopulations of these neurons. Non-linear hierarchical filters using artificial neural networks (ANNs) were designed to asynchronously decode the activity from multiple virtual ensembles, in real-time. The decoded output was then used to actuate individual fingers of a robotic hand. An average real-time decoding accuracy of greater than 95 % was achieved with all neurons from randomly placed voxels containing 48 neurons, and up to 80% with as few as 25 neurons. These results suggest that dexterous control of individual digits and wrist of a prosthetic hand can be achieved by real-time decoding of neuronal ensembles from the Ml hand area in primates.

Quantitative EEG Assessment of Brain Injury and Hypothermic Neuroprotection after Cardiac Arrest

In this paper we provide a quantitative electroencephalogram (EEG) analysis to study the effect of hypothermia on the neurological recovery of brain after cardiac arrest. We hypothesize that the brain injury results in a reduction in information of the brain rhythm. To measure the information content of the EEG a new measure called information quantity (IQ), which is the Shannon entropy of decorrelated EEG signals, is developed. For decorrelating EEG signals, we use the discrete wavelet transform (DWT) which is known to have good decorrelating properties and to show a good match to the standard clinical bands in EEG. In simulation for measuring the amount of information, the IQ shows better tracking capability for dynamic amplitude change and frequency component change than conventional entropy-based measures. Experiments are carried out in rodents to monitor the neurological recovery after cardiac arrest. In addition, EEG signal recovery under normothermic (37degC) and hypothermic (33degC) resuscitation following 5, 7 and 9 minutes of cardiac arrest is recorded and analyzed. Experimental results show that the IQ is higher for hypothermic than normothermic rats. The results quantitatively support the hypothesis that hypothermia accelerates the recovery of brain injury after cardiac arrest

Cortical Vascular Blood Flow Pattern By Laser Speckle Imaging

The cortical vascular blood flow pattern is associated with the functional response in cerebral cortex. The pattern of the vascular blood flow can be used to study the spatiotemporal activities of the somatosensory center and the diagnosis of the focal stroke or ischemia. In this present study, temporal laser speckle analysis is used to obtain the cortical blood flow information. By centerline modeling method based on ridge tracking, we extracted the cortical vessels. By fusing with the laser speckle contrast results, we obtained the image of the cortical vascular blood flow pattern in which both the vessels and the blood velocity can be visualized

Motor Imagery based Brain-Computer Interface for Cerebellar Ataxia

소뇌 운동실조는 점차 진행되는 신경퇴행질병이며 운동 조절을 위한 기능의 상실을 동반하기에 환자의 삶을 심각하게 저하시킨다. 소뇌 운동실조 환자는 운동제어 과정에서 부적절한 폐회로 소뇌 반응으로 인해 운동 명령이 제한된다. 본 논문에서는 최근 뇌-컴퓨터 인터페이스 기술을 이용하여 소뇌의 이상으로 인한 운동실조 환자들이 외부기기를 제어할 수 있도록 운동상상 기반의 뇌파의 특성을 분석하고 이를 이용한 뇌-컴퓨터 인터페이스 기법을 제안한다. 뇌파 기반의 뇌-컴퓨터 인터페이스의 효용성을 검증하기 위하여 소뇌 운동실조 환자와 정상인 그룹에서 운동상상에 따른 뮤밴드 파워를 조절하는 능력을 비교하였다. 이를 통하여 소뇌 운동실조 환자에의 뇌-컴퓨터 인터페이스의 가능성을 보여준다.