• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2002.05a
• /
• pp.87-90
• /
• 2002

뇌파(Electroencephalogram, EEG)는 뇌 신경세포가 정보를 처리하는 과정에서 발생하는 전기적인 신호를 두피 표면에서 측정한 것이다. 이러한 뇌파는 비침습적인 방법으로 전기적인 신호를 측정하며 측정시 여러 잡파(artifact)가 섞이기 쉽다. 이러한 잡파는 뇌의 정보처리과정에 대한 유용한 정보를 담고 있는 뇌파를 분석하는데 방해가 되므로 이를 제거하기 위한 노력이 계속되어 왔다. 그러나 본 연구에서는 보다 적극적인 방향으로 잡파가 섞인 뇌파의 특성을 분석하여 이를 통해 제어 시스템 등과 같은 시스템에 적용할 수 있는 가능성을 알아보았다. 대표적인 잡파인 eye_blinking, eye_rolling, muscle 등이 각각 포함된 뇌파에 대해서 선형 및 비선형 분석을 실시함으로써 유의미한 특성 차이를 나타내었다.

• Journal of Korea Game Society
• /
• v.15 no.2
• /
• pp.105-114
• /
• 2015

We propose a real time user interface that utilizes emotion recognition by physiological signals. To improve the problem that was low accuracy of emotion recognition through the traditional EEG(ElectroEncephaloGram), We developed a physiological signals-based emotion recognition system mixing relative power spectrum values of theta/alpha/beta/gamma EEG waves and autonomic nerve signal ratio of ECG (ElectroCardioGram). We propose both a data map and weight value modification algorithm to recognize six emotions of happy, fear, sad, joy, anger, and hatred. The datamap that stores the user-specific probability value is created and the algorithm updates the weighting to improve the accuracy of emotion recognition corresponding to each EEG channel. Also, as we compared the results of the EEG/ECG bio-singal complex data and single data consisting of EEG, the accuracy went up 23.77%. The proposed interface system with high accuracy will be utillized as a useful interface for controlling the game spaces and smart spaces.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2015.05a
• /
• pp.958-961
• /
• 2015

Emotional expression is universal and emotional state impacts important areas in our life. Until now, analyzing the acquired EEG signals under circumstances caused by invoked feelings and efforts to define their emotional state have been made mainly by psychologists based on the results. But, recently emotion-related information was released by research results that it is possible to identify mental activity through measuring and analyzing the brain EEG signals. So, this study has compared and analyzed emotional expressions of human by using brain waves. To get EEG difference for a particular emotion, we showed specific subject images to the people for changing emotions that peace, joy, sadness and stress, etc. After measured EEG signals were converged into frequence domain by FFT signal process, we have showed EEG changes in emotion as a result of the performance analyzing each respective power spectrum of delta, theta, alpha, beta and gamma waves.

• The Journal of the Korea Contents Association
• /
• v.21 no.2
• /
• pp.140-147
• /
• 2021

EEG(electroencephalogram) are measured to accurately determine the level of sleep in various sleep examinations. In general, measurements are more accurate as the number of sensor channels increases. EEG can interfere with sleep by attaching electrodes to the skin when measuring. It is necessary for self sleep care to select the minimum number of EEG channels that take into account both the user's discomfort and the accuracy of the measurement data. In this paper, we proposed a sleep stage analysis model based on machine learning and conducted experiments for using from one channel to four channels. We obtained estimation accuracy for sleep stage as following 82.28% for one channel, 85.77% for two channels, 80.33% for three channels and 68.87% for four channels. Although the measurement location is limited, the results of this study compare the accuracy according to the number of channels and provide information on the selection of channel numbers in the EEG sleep analysis.

• Proceedings of the KIEE Conference
• /
• 2007.10a
• /
• pp.343-344
• /
• 2007

본 논문은 의사표현이 어려운 전신마비환자의 뇌파(EEG)를 이용하여 긍정과 부정을 표현할 수 있는 방법에 대해서 소개한다. 더 나아가 인간의 감정에 따라 긍정과 부정을 민감하게 반응하는 뇌 영역을 분석하였다. 해당영역의 뇌파(EEG)변화를 측정하기 위해 컴퓨터 시스템과 접목시키는 목적도 포함하고 있다. 이를 위해서 미약한 뇌파를 증폭 시키는 전치 증폭기를 구현하였고 인공산물과 뇌파 주파수영역만을 통과시키는 아날로그 전자회로를 구현하였다. 또한 인간의 두뇌피질로부터 측정된 신호는 컴퓨터 시스템에 전송된다. 수신된 신호를 실시간 Fast Fourier Transform(FFT) 신호처리과정을 거쳐 뇌파의 주파수 영역을 분류하게 된다. 이때 분류된 뇌파를 바탕으로 인간의 긍정과 부정을 표현할 수 있는 방법을 제시한다.

• Clinical and Experimental Pediatrics
• /
• v.46 no.2
• /
• pp.137-142
• /
• 2003

Purpose : This study was performed to determine the sensitivity of neonatal electroencephalography (EEG) in detecting underlying brain disease, to compare the sensitivity and specificity of EEG with those of brain ultrasonography and to determine the prognostic value of EEG for neonatal neurologic diseases. Methods : Eighty-seven newborn babies were subjected to a electroencephalographic examination for the evaluation of underlying neurological diseases and EEGs were recorded at least before three days of life. The findings of early ultrasonography performed within three days after birth were compared with those of magnetic resonance imaging(MRI) or ultrasonography after seven days of life. Results : The EEG results were more sensitive and specific than ultrasonography for the detection of neonatal brain damage. The EEG results showed 91.7% sensitivity for mild grade neurological sequelae and 100.0% sensitivity for moderate and severe-grade neurological sequelae in predicting the neurological outcome. However, early ultrasonography results showed 20.8% and 18.8% of sensitivity and specificity, respectively. Conclusion : EEG is a highly sensitive diagnostic tool for detecting neonatal brain disease and is valuable for predicting the long-term outcome of neurologic sequelae.

• Journal of Digital Convergence
• /
• v.17 no.2
• /
• pp.155-161
• /
• 2019

Electroencephalography (EEG), a representative method of identifying temporal and spatial changes in brain activity, is a voluntary electrical activity measurable in the human scalp. Various interface technologies have been provided to control EEG activity, and it is possible to operate a machine such as a wheelchair or a robot through brainwaves. The characteristics of EEG data are collected in various types of channels in real time, and a server system for analyzing them is required to have an independent and lightweight system for the platform. In these days, the Spring platform is used as a large business server as an independent, lightweight server system. In this paper, we propose an EEG analysis system using the Spring server system. Using the proposed system, the reliability of EEG control can be enhanced, and analysis and control interface expansion can be provided in various aspects such as game and medical areas.

• Journal of Digital Convergence
• /
• v.16 no.12
• /
• pp.343-349
• /
• 2018

Electroencephalograph(EEG) information, which is an important data of brain science, reflects various levels of information from the molecular level to the behavior and cognitive stages, and the explosively amplified information is provided at each stage. Therefore, EEG information is an intrinsic privacy area of an individual, which is important information to be protected. In this paper, we apply spring security to web based system of spring MVC (Model, View, Control) framework to build independent and lightweight server system with powerful security system. Through the proposal of the platform type EEG analysis system which enhances the security function, the web service security of the EEG information is enhanced and the privacy of the EEG information can be protected.

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

• KIPS Transactions on Software and Data Engineering
• /
• v.10 no.12
• /
• pp.587-594
• /
• 2021

As the diagnosis using encephalography(EEG) has been expanded, various studies have been actively performed for classifying EEG automatically. This paper proposes a CNN model that can effectively classify EEG signals acquired from healthy persons and patients with epilepsy. We segment the EEG signals into sub-signals with smaller dimension to augment the EEG data that is necessary to train the CNN model. Then the sub-signals are segmented again with overlap and they are used for training the CNN model. We also propose ensemble strategy in order to improve the classification accuracy. Experimental result using public Bonn dataset shows that the CNN can detect the epileptic seizure with the accuracy above 99.0%. It also shows that the ensemble method improves the accuracy of 3-class and 5-class EEG classification.