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

• Journal of Internet Computing and Services
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• v.20 no.5
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• pp.27-36
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• 2019

In this study, experiments on the improvement of the emotion classification, analysis and accuracy of EEG data were proceeded, which applied DEAP (a Database for Emotion Analysis using Physiological signals) dataset. In the experiment, total 32 of EEG channel data measured from 32 of subjects were applied. In pre-processing step, 256Hz sampling tasks of the EEG data were conducted, each wave range of the frequency (Hz); Theta, Slow-alpha, Alpha, Beta and Gamma were then extracted by using Finite Impulse Response Filter. After the extracted data were classified through Time-frequency transform, the data were purified through Independent Component Analysis to delete artifacts. The purified data were converted into CSV file format in order to conduct experiments of Machine learning algorithm and Arousal-Valence plane was used in the criteria of the emotion classification. The emotions were categorized into three-sections; 'Positive', 'Negative' and 'Neutral' meaning the tranquil (neutral) emotional condition. Data of 'Neutral' condition were classified by using Cz(Central zero) channel configured as Reference channel. To enhance the accuracy ratio, the experiment was performed by applying the attributes selected by ASC(Attribute Selected Classifier). In "Arousal" sector, the accuracy of this study's experiments was higher at "32.48%" than Koelstra's results. And the result of ASC showed higher accuracy at "8.13%" compare to the Liu's results in "Valence". In the experiment of Random Forest Classifier adapting ASC to improve accuracy, the higher accuracy rate at "2.68%" was confirmed than Total mean as the criterion compare to the existing researches.

• Progress in Medical Physics
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• v.13 no.3
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• pp.156-162
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• 2002

In this study, we attempted in preparing high precision EEG measuring equipment. To measure EEG in high efficiency, pre-amplifier should get high performance common mode rejection ratio. Also, separation amplifier is essential to eliminate common line noise. So, our study were pointed at elevating the efficiency of eliminating noise, user safety and low noise characteristics. Prepared high precision pre-amplifier for EEG was A/D converted to automatically classify $\alpha$ wave, $\beta$ wave and $\theta$ wave. And converted data were Fast Fourier Transformed with real time DSP (Digital Signal Processing). Clinical demonstrations were carried out with healthy students, aged between 20 to 26 who has no histories of illness. To recognize the efficiency of the EEG, prepared EEG were used with MS equipment in low stimulated state and high stimulated state. Then, we studied at the effect of sensitivity on brain wave. From this study, it is known that our EEG equipment is efficient in sensitivity evaluation and suitable stimulations for each psychological state are required.

• Journal of Digital Convergence
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• v.17 no.2
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• pp.155-161
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• 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
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• v.16 no.12
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• pp.343-349
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• 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.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.3D
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• pp.231-240
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• 2010

As majority of the traffic accidents in intersections is caused by human factor, a close examination is required on its contributing factors through measuring the psychological and physiological response according to the driving characteristics of the drivers and the road conditions. In this study, for the safety evaluation of individual intersection considering human factors of the drivers, electroencephalography reaction was measured utilizing cutting-edge measuring equipment and survey on drivers' cognitive characteristics in ordinary times and while driving test was conducted. The relationship between the electroencephalography response when approaching the intersection and cognitive evaluation survey data in driving test was clarified, and individual intersection safety evaluation model was built considering cognitive evaluation factor and the reaction of a bio-response electroencephalography data. As a result, I could find out that cognitive evaluation was made through the reaction of a bio-response (Electroencephalography) process because electroencephalography reaction of a bio-response showed differently by the physical characteristics of the intersection and cognitive evaluation had a difference.

• Journal of IKEEE
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• v.25 no.4
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• pp.679-688
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• 2021

Gait is an evaluation index used in various clinical area including brain nervous system diseases. Signal source localizing and time-frequency analysis are mainly used after extracting independent components for Electroencephalogram data as a method of measuring and analyzing brain activation related to gait. Existing treadmill-based walking EEG analysis performs signal preprocessing, independent component analysis(ICA), and source localizing by merging data after the multiple EEG measurements, and extracts representative component clusters through inter-subject clustering. In this study we propose an analysis method, without merging to single dataset, that performs signal preprocessing, ICA, and source localization on each measurements, and inter-subject clustering is conducted for ICs extracted from all subjects. The effect of data merging on the IC clustering and time-frequency analysis was investigated for the proposed method and two conventional methods. As a result, it was confirmed that a more subdivided gait-related brain signal component was derived from the proposed "non-merging" method (4 clusters) despite the small number of subjects, than conventional method (2 clusters).

• Journal of the Korea Computer Graphics Society
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• v.25 no.1
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• pp.23-32
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• 2019

Cybersickness is a symptom of dizziness that occurs while experiencing Virtual Reality (VR) technology and it is presumed to occur mainly by crosstalk between the sensory and cognitive systems. However, since the sensory and cognitive systems cannot be measured objectively, it is difficult to measure cybersickness. Therefore, methodologies for measuring cybersickness have been studied in various ways. Traditional studies have collected answers to questionnaires or analyzed EEG data using machine learning algorithms. However, the system relying on the questionnaires lacks objectivity, and it is difficult to obtain highly accurate measurements with the machine learning algorithms. In this work, we apply Deep Neural Network (DNN) deep learning algorithm for objective cybersickness measurement from EEG data. We also propose a data preprocessing for learning and network structures allowing us to achieve high performance when learning EEG data with the deep learning algorithms. Our approach provides cybersickness measurement with an accuracy up to 98.88%. Besides, we analyze video characteristics where cybersickness occurs by examining the video segments causing cybersickness in the experiments. We discover that cybersickness happens even in unusually persistent changes in the darkness such as the light in a room keeps switching on and off.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.103-108
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• 2016

In daily life, humans get stress very often. Stress is one of the important factors of healthy life and closely related to the quality of life. Too much stress is known to cause hormone imbalance of our body, and it is observed by the brain and bio signals. Based on this, the relationship between brain signal and stress is explored, and brain signal based stress index is proposed in our work. In this study, an EEG measurement device with 32 channels is adopted. However, only two channels (FP1, FP2) are used to this study considering the applicability of the proposed method in real enveironment, and to compare it with the commercial 2 channel EEG device. Frequency domain features are power of each frequency bands, subtraction, addition, or division by each frequency bands. Features in time domain are hurst exponent, correlation dimension, lyapunov exponent, etc. Total 6 subjects are participated in this experiment with English sentence reading task given. Among several candidate features, ${\frac{{\theta}\;power}{mid\;{\beta}\;power}}$ shows the best test performance (70.8%). For future work, we will confirm the results is consistent in low price EEG device.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1922-1923
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• 2007

현대 사회는 급속한 기술의 발전으로 인하여 공상과학영화에서나 볼 수 있었던 첨단 기술들이 실생활에서 구현되어지고 있다. 이러한 첨단기술 중 하나였던 뇌를 이용하여 각종 인터페이스를 제어하는 기술인 BCI 및 BMI 기술이 각광을 받고 있다. 이러한 기술들은 EEG 신호의 취득 및 분석 기술이 발전하면서 많은 발전을 이루었고 앞으로도 그 발전 가능성은 무궁무진하다. 따라서 본 연구에서는 이러한 기술의 실현을 위해 획득된 뇌파 신호를 분석하여 기계장치를 제어 할 수 있도록 데이터의 처리 방법을 제안하였다. 이러한 데이터 처리 방법으로는 Fir(Finite impulse response)필터링과 ICA알고리즘의 구현, FFT 분석을 통한 주파수별 전력분포 계산의 과정이 있다. 이러한 과정 등을 통해 피검자가 원하는 EEG 데이터를 얻을 수 있게 된다.