• Proceedings of the KOSOMBE Conference
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• v.1995 no.11
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• pp.278-281
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• 1995

In this paper, we have developed EEG (electroencephalography) analyzer for monitoring the condition of brain in neurological surgery. This system is composed of EEG amplifier. personal-computer and BSP (Digital Signal Processor). By parallel processing of DSP, this system can analysis the power spectral density change of EEG in real-time and display the CSA(Compressed Spectral Array) and CDSA(Color Density Spectral array) of EEG. This system was tested by real EEG and showed the change of EEG.

• Nuclear Engineering and Technology
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• v.51 no.3
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• pp.683-691
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• 2019

In this paper, we propose a novel method to quantify the neural synchronization between subjects in the collaborative process through electroencephalogram (EEG) hyperscanning. We hypothesized that the neural synchronization in EEGs will increase when the communication of the operators is smooth and the teamwork is better. We quantified the EEG signal for multiple subjects using a representative EEG quantification method, and studied the changes in brain activity occurring during collaboration. The proposed method quantifies neural synchronization between subjects through bispectral analysis. We found that phase synchronization between EEGs of multi subjects increased significantly during the periods of collaborative work. Traditional methods for a human error analysis used a retrospective analysis, and most of them were analyzed for an unspecified majority. However, the proposed method is able to perform the real-time monitoring of human error and can directly analyze and evaluate specific groups.

• Journal of Biomedical Engineering Research
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• v.28 no.2
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• pp.258-264
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• 2007

Spatiotemporal changes of brain rhythmic activity at a certain frequency have been usually monitored in real time using scalp potential maps of multi-channel electroencephalography(EEG) or magnetic field maps of magnetoencephalography(MEG). In the present study, we investigate if it is possible to implement a real-time brain activity monitoring system which can monitor spatiotemporal changes of cortical rhythmic activity on a subject's cortical surface, neither on a sensor plane nor on a standard brain model, with a high temporal resolution. In the suggested system, a frequency domain inverse operator is preliminarily constructed, considering the individual subject's anatomical information, noise level, and sensor configurations. Spectral current power at each cortical vertex is then calculated for the Fourier transforms of successive sections of continuous data, when a single frequency or particular frequency band is given. An offline study which perfectly simulated the suggested system demonstrates that cortical rhythmic source changes can be monitored at the cortical level with a maximal delay time of about 200 ms, when 18 channel EEG data are analyzed under Pentium4 3.4GHz environment. Two sets of artifact-free, eye closed, resting EEG data acquired from a dementia patient and a normal male subject were used to show the feasibility of the suggested system. Factors influencing the computational delay are investigated and possible applications of the system are discussed as well.

• Journal of Biomedical Engineering Research
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• v.44 no.6
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• pp.392-403
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• 2023

This study presents a new hybrid fNIRS-EEG system to meet the demand for a lightweight and low-cost sleep pattern monitoring device. For multiple-channel configuration, a six-channel electroencephalogram (EEG) and a functional near-infrared spectroscopy (fNIRS) system with eight photodiodes (PD) and four dual-wavelength LEDs are designed. To enhance the convenience of signal measurement, the device is miniaturized into a patch-like form, enabling simultaneous measurement on the forehead. Due to its fully integrated functionality, the developed system is advantageous for performing sleep stage classification with high-temporal and spatial resolution data. This can be realized by utilizing a two-dimensional (2D) brain activation map based on the concentration changes in oxyhemoglobin and deoxyhemoglobin during sleep stage transitions. For the system verification, the phantom model with known optical properties was tested at first, and then the sleep experiment for a human subject was conducted. The experimental results show that the developed system qualifies as a portable hybrid fNIRS-EEG sleep pattern monitoring device.

• Proceedings of the IEEK Conference
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• 2002.06e
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• pp.289-292
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• 2002

In this study, we measure and analyzed variation of EEG signal by anesthesiologist progress step. In an experiment, the EEG signal was acquired and analyzed as 5 steps(prior surgical operation, during induction, surgical operation, awakening, posterior surgical operation). As a result, we confirm the anesthesiologist progress phase, concluded the possibility of anesthesia depth because using SEF and MF, and Delta ratio confirmed that can presume operating patient's consciousness state.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.2
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• pp.70-78
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• 1998

Spike detection in long-term EEG monitoring forepilepsy by wavelet transform(WT), artificial neural network(ANN) and the expert system is presented. First, a small set of wavelet coefficients is used to represent the characteristics of a singlechannel epileptic spikes and normal activities. In this stage, two parameters are also extracted from the relation between EEG activities before the spike event and EEG activities with the spike. then, three-layer feed-forward network employing the error back propagation algorithm is trained and tested using parameters obtained from the first stage. Spikes are identified in individual EEG channels by 16 identical neural networks. Finally, 16-channel expert system based on the context information of adjacent channels is introducedto yield more reliable results and reject artifacts. In this study, epileptic spikes and normal activities are selected from 32 patient's EEG in consensus among experts. The result showed that the WT reduced data input size and the preprocessed ANN had more accuracy than that of ANN with the same input size of raw data. Ina clinical test, our expert rule system was capable of rejecting artifacts commonly found in EEG recodings.

• The Journal of the Korea Contents Association
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• v.11 no.6
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• pp.26-32
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• 2011

Recently, in the U-health area, there are research related on monitoring brainwaves in real-time for coping with emergent situations like the fatigue driving, cerebral infarction or the heart attack of not only the patients but also the normal elderly folks by transmitting of the EEG(Electroencephalograph). This system could be applied to hospitals or sanatoriums. In this paper, it is applied for the vehicular ad-hoc network to prevent the car accident in advance by monitoring the brainwaves of a driver in real-time. In order to do this, I used mobile ad-hoc nodes supported in the Opnet simulator for the efficient EEG brainwave transmission in the VANET environment. The vehicular ad-hoc networks transmitting the brainwaves to the nearest road-side unit are designed and simulated to draw an efficient and proper vehicular ad-hoc network environment.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.5
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• pp.521-528
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• 2019

Epilepsy is one of the most prevalent neurological diseases. Electroencephalogram (EEG) signals are widely used for monitoring and diagnosis tool for epileptic seizure. Typically, a huge amount of EEG signals is needed, where they are visually examined by experienced clinicians. In this study, we propose a simple automatic seizure detection framework using intracranial EEG signals. We suggest a sparse approximation based classification (SAC) scheme by solving overdetermined system. L1-norm minimization algorithms are utilized for efficient sparse signal recovery. For evaluation of the proposed scheme, the public EEG dataset obtained by five healthy subjects and five epileptic patients is utilized. The results show that the proposed fast L1-norm minimization based SAC methods achieve the 99.5% classification accuracy which is 1% improved result than the conventional L2 norm based method with negligibly increased execution time (42msec).

• Journal of Dental Anesthesia and Pain Medicine
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• v.17 no.4
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• pp.241-251
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• 2017

Devices that monitor the depth of hypnosis based on the electroencephalogram (EEG) have long been commercialized, and clinicians use these to titrate the dosage of hypnotic agents. However, these have not yet been accepted as standard monitoring devices for anesthesiology. The primary reason is that the use of these monitoring devices does not completely prevent awareness during surgery, and the development of these devices has not taken into account the neurophysiological mechanisms of hypnotic agents, thus making it possible to show different levels of unconsciousness in the same brain status. An alternative is to monitor EEGs that are not signal processed with numerical values presented by these monitoring devices. Several studies have reported that power spectral analysis alone can distinguish the effects of different hypnotic agents on consciousness changes. This paper introduces the basic concept of power spectral analysis and introduces the EEG characteristics of various hypnotic agents that are used in sedation.

• Clinical and Experimental Pediatrics
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• v.51 no.1
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• pp.62-66
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• 2008

Purpose : In addition to epileptic seizures (ES), a variety of physiologic, organic and psychogenic disorders can manifest as paroxysmal behavioral events. Paroxysmal nonepileptic events (PNEs) are quite encountered in infants, young children, and adolescents. In a substantial proportion of cases, a careful history and examination will elucidate their nature. However, in other cases, it is necessary to differentiate PNEs from ES by video-electroencephalographic (EEG) monitoring. We report our experiences with PNEs in a group of children and adolescents who underwent video-EEG monitoring. Methods : From September, 2004 to June, 2006, one hundred thirty patients were monitored in the Pediatric Epilepsy Monitoring Units of Korea University Guro and Ansan hospitals. Their hospital charts were reviewed and video records of these events were analyzed. We observed all patients after video-EEG monitoring for more than 3 months. Results : Typical spells occurred during monitoring in 33 patients, not associated with a seizure pattern on EEG recordings. Two patients were diagnosed as frontal lobe epilepsy on basis of typical semiology and clinical characteristics, so 31 patients were documented to have PNEs finally. The mean age of patients was $7.2{\pm}5.8\;years$. The male to female ratio was 15 (48.4%) to 16 (51.6%). Among 31 patients, fifteen patients had associated disorders such as epilepsy, developmental delay, cerebral palsy, gastric ulcer, attention deficit hyperactivity disorder or depressive disorder. Somatoform disorder and factitious disorder was frequently seen in children more than 5 years old (P<0.05). Psychogenic disorder was more frequent in female (n=6) than in male (n=2) but there was no statistical significance (P>0.05). Conclusion : Our study suggests that video-EEG monitoring is an important diagnostic tool in the evaluation of paroxysmal behavioral events. With correct diagnosis of the PNEs, several unnecessary treatment could be avoided.