• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.3
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• pp.329-334
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• 2014

Recently, various electronic functions are developed for car drivers as the advent of electrical automobile. Especially, there are functions to examine for preventing drowsy or healthcare through monitoring brainwave(EEG) of drivers in real time. This function can be provided by transmitting driver's EEG, and the network function for transmission among cars or between car and road side infrastructure is a vital issue. Therefore, in this paper, to provide efficient routing protocol for transmitting EEG data at a cross road in an urban area, 5 different wireless communication network applied each routing protocol such as AODV, DSR, GRP, OLSR, and TORA is designed and simulated in the OPNet network simulator, then it is evaluated for the result.

• JSTS:Journal of Semiconductor Technology and Science
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• v.16 no.4
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• pp.436-442
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• 2016

An electroencephalogram (EEG)-connectome processor to monitor and diagnose mental health is proposed. From 19-channel EEG signals, the proposed processor determines whether the mental state is healthy or unhealthy by extracting significant features from EEG signals and classifying them. Connectome approach is adopted for the best diagnosis accuracy, and synchronization likelihood (SL) is chosen as the connectome feature. Before computing SL, reconstruction optimizer (ReOpt) block compensates some parameters, resulting in improved accuracy. During SL calculation, a sparse matrix inscription (SMI) scheme is proposed to reduce the memory size to 1/24. From the calculated SL information, a small world feature extractor (SWFE) reduces the memory size to 1/29. Finally, using SLs or small word features, radial basis function (RBF) kernel-based support vector machine (SVM) diagnoses user's mental health condition. For RBF kernels, look-up-tables (LUTs) are used to replace the floating-point operations, decreasing the required operation by 54%. Consequently, The EEG-connectome processor improves the diagnosis accuracy from 89% to 95% in Alzheimer's disease case. The proposed processor occupies $3.8mm^2$ and consumes 1.71 mW with $0.18{\mu}m$ CMOS technology.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1910-1911
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• 2007

A new method for estimating and eliminating electrocardiogram (ECG) artifacts from single channel scalp electroencephalogram (EEG) is proposed. The proposed method consists of emphasis of QRS complex from EEG using least squares acceleration (LSA) filter, generation of synchronized pulse with R-peak and ECG artifacts estimation and elimination using adaptive filter. The performance of the proposed method was evaluated using simulated and real EEG recordings, we found that the ECG artifacts were successfully estimated and eliminated in comparison with the conventional multi-channel techniques, which are independent component analysis (ICA) and ensemble average (EA) method. In conclusion, we can conclude that the proposed method is useful for the detecting and eliminating the ECG artifacts from single channel EEG and simple to use for ambulatory/portable EEG monitoring system.

• The Korean Journal of Physiology and Pharmacology
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• v.23 no.2
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• pp.131-139
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• 2019

Manually reviewing electroencephalograms (EEGs) is labor-intensive and demands automated seizure detection systems. To construct an efficient and robust event detector for experimental seizures from continuous EEG monitoring, we combined spectral analysis and deep neural networks. A deep neural network was trained to discriminate periodograms of 5-sec EEG segments from annotated convulsive seizures and the pre- and post-EEG segments. To use the entire EEG for training, a second network was trained with non-seizure EEGs that were misclassified as seizures by the first network. By sequentially applying the dual deep neural networks and simple pre- and post-processing, our autodetector identified all seizure events in 4,272 h of test EEG traces, with only 6 false positive events, corresponding to 100% sensitivity and 98% positive predictive value. Moreover, with pre-processing to reduce the computational burden, scanning and classifying 8,977 h of training and test EEG datasets took only 2.28 h with a personal computer. These results demonstrate that combining a basic feature extractor with dual deep neural networks and rule-based pre- and post-processing can detect convulsive seizures with great accuracy and low computational burden, highlighting the feasibility of our automated seizure detection algorithm.

• International Journal of Computer Science & Network Security
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• v.24 no.6
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• pp.59-66
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• 2024

Measurable electrophysiological changes in the scalp are frequently linked to brain activities. These progressions are called related evoked potentials (ERP), which are transient electrical responses recorded by electroencephalography (EEG) in light of tactile, mental, or motor enhancements. This painless strategy is gradually being used as a conclusion and clinical help. In this article, we will talk about the main ways to monitor brain activities in people with neurological diseases like Alzheimer's disease by analyzing EEG signals using ERP. We will also talk about how this method helps to detect the disease at an early stage.

• Annals of Clinical Neurophysiology
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• v.19 no.2
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• pp.118-124
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• 2017

Electroencephalography (EEG) is frequently used to assist the diagnosis of brain death. However, to date there have been no guidelines in terms of EEG criteria for determining brain death in Korea, despite EEG being mandatory. The purpose of this review is to provide an update on the evidence and controversies with regarding to the utilization of EEG for determining brain death and to serve as a cornerstone for the development of future guidelines. To determine brain death, electrocerebral inactivity (ECI) should be demonstrated on EEG at a sensitivity of $2{\mu}V/mm$ using double-distance electrodes spaced 10 centimeters or more apart from each other for at least 30 minutes, with intense somatosensory or audiovisual stimuli. ECI should be also verified by checking the integrity of the system. Additional monitoring is needed if extracerebral potentials cannot be eliminated. Interpreting EEG at high sensitivities, which is required for the diagnosis of brain death, can pose a diagnostic challenge. Furthermore, EEG is affected by physiologic variables and drugs. However, no consensus exists as to the minimal requirements for blood pressure, oxygen saturation, and body temperature during the EEG recording itself, the minimal time for observation after the brain injury or rewarming from hypothermia, and how to determine brain death when the findings of ECI is equivocal. Therefore, there is a strong need to establish detailed guidelines for performing EEG to determine brain death.

• Journal of the Korea Society of Computer and Information
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• v.23 no.11
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• pp.129-135
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• 2018

In this study, we designed a model that can measure the level of user's concentration by measuring and analyzing EEG data of the subjects who are performing Continuous Performance Test based on visual stimulus. This study focused on alpha and beta waves, which are closely related to concentration in various brain waves. There are a lot of research and services to enhance not only concentration but also brain activity. However, there are formidable barriers to ordinary people for using routinely because of high cost and complex procedures. Therefore, this study designed the model using the portable EEG measurement device with reasonable cost and Visual Continuous Performance Test which we developed as a simplified version of the existing CPT. This study aims to measure the concentration level of the subject objectively through simple and affordable way, EEG analysis. Concentration is also closely related to various brain diseases such as dementia, depression, and ADHD. Therefore, we believe that our proposed model can be useful not only for improving concentration but also brain disease prediction and monitoring research. In addition, the combination of this model and the Brain Computer Interface technology can create greater synergy in various fields.

• Biomedical Engineering Letters
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• v.8 no.4
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• pp.373-382
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• 2018

Since epileptic seizure is unpredictable and paroxysmal, an automatic system for seizure detecting could be of great significance and assistance to patients and medical staff. In this paper, a novel method is proposed for multichannel patient-specific seizure detection applying the earth mover's distance (EMD) in scalp EEG. Firstly, the wavelet decomposition is executed to the original EEGs with five scales, the scale 3, 4 and 5 are selected and transformed into histograms and afterwards the distances between histograms in pairs are computed applying the earth mover's distance as effective features. Then, the EMD features are sent to the classifier based on the Bayesian linear discriminant analysis (BLDA) for classification, and an efficient postprocessing procedure is applied to improve the detection system precision, finally. To evaluate the performance of the proposed method, the CHB-MIT scalp EEG database with 958 h EEG recordings from 23 epileptic patients is used and a relatively satisfactory detection rate is achieved with the average sensitivity of 95.65% and false detection rate of 0.68/h. The good performance of this algorithm indicates the potential application for seizure monitoring in clinical practice.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.625-626
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• 2017

장시간 앉아 생활을 하는 직장인이나 학생들은 시간 경과에 따른 집중력 저하는 필수적으로 동반되며, 이를 모니터링하여 집중력을 향상시키기 위한 다양한 시도들이 연구되고 있다. 본 연구에서는 간편하게 착용이 가능한 무선뇌파계측 시스템기반이 실시간 집중력 모니터링 시스템을 구현하고자 하였으며, 이를 위하여 블루투스기반의 무선뇌파 측정시스템과 스마트폰 기반의 뇌파분석 어플리케이션을 개발하였다. 어플리케이션에서는 실시간 스펙트럼분석을 통해 집중력 파라미터를 추출하고 집중력의 저하를 인지하면 자동으로 피드백할 수 있도록 시스템을 구성하였다. 구현된 시스템의 평가를 위해 집중상태를 유발하고 각각의 상태별 뇌파스펙트럼 파라미터의 상관관계를 분석하였으며, 실험결과 본 연구에서 제시한 기법을 통해 실시간 집중상태 모니터링이 가능함을 확인하였다.

• Annals of Clinical Neurophysiology
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• v.1 no.1
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• pp.1-9
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• 1999

Since Hans Berger reported the first paper on the human electroencephalogram in 1920s, huge technological advance have made it possible to use a number of electrophysiological approaches to neurological diagnosis in clinical neurology. In majority of the neurology training hospitals they have facilities of electroencephalography(EEG), electromyography(EMG), evoked potentials(EP), polysomnography(PSG), electronystagmography(ENG) and, transcranial doppler(TCD) ete. Clinicials and electrophysiologists should understand the technologic characteristics and general applications of each electrophysiological studies to get useful informations with using them in clinics. It is generally agreed that items of these tests are selected under the clinical examination, the tests are performed by the experts, and the test results are interpretated under the clinical background. Otherwise these tests are sometimes useless and lead clinicians to misunderstand the lesion site, the nature of disease, or the disease course. In this sense the clinical utility of neurophysiological tests could be summerized in the followings. First, the abnormal functioning of the nervous system and its environments can be demonstrated when the history and neurological examinations are equivocal. Second, the presence of clinically unsuspected malfunction in the nervous system can be revealed by those tests. Finally the objective changes can be monitored over time in the patient's status. Also intraoperative monitoring technique becomes one of the important procedures when the major operations in the posterior fossa or in the spinal cord are performed. In 1996, the Korean Society for Clinical Neurophysiology(KSCN) was founded with the hope that it will provide the members with the comfortable place for discussing their clinical and academic experience, exchanging new informations, and learning new techniques of the neurophysiological tests. The KSCN could collaborate with the International Federation of Clinical Neurophysiology(IFCN) to improve the level of the clinical neurophysiologic field in Korea as will as in Asian region.1 In this paper the clinical neurophysiological tests which are commonly used in clinical neurology and which will be delt with and educated by the KSCN in the future will be discussed briefly in order of EEG, EMG, EP, PSG, TCD, ENG, and Intraoperative monitoring.