• Investigative Magnetic Resonance Imaging
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• v.7 no.2
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• pp.108-115
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• 2003

Purpose : To evaluate the effect of the gradient switching noise on the ECD source localization with the EEG data recorded during the MRI scan. Materials and Methods : We have fabricated a spherical EEG phantom that emulates a human head on which multiple electrodes are attached. Inside the phantom, electric current dipole(ECD) sources are located to evaluate the source localization error. The EEG phantom was placed in the center of the whole-body 3.0 Tesla MRI magnet, and a sinusoidal current was fed to the ECD sources. With an MRI-compatible EEG measurement system, we recorded the multi channel electric potential signals during gradient echo single-shot EPI scans. To evaluate the effect of the gradient switching noise on the ECD source localization, we controlled the gradient noise level by changing the FOV of the EPI scan. With the measured potential signals, we have performed the ECD source localization. Results : The source localization error depends on the gradient switching noise level and the ECD source position. The gradient switching noise has much bigger negative effects on the source localization than the Gaussian noise. We have found that the ECD source localization works reasonably when the gradient switching noise power is smaller than $10\%$ of the EEG signal power. Conclusion : We think that the results of the present study can be used as a guideline to determine the degree of gradient switching noise suppression in EEG when the EEG data are to be used to enhance the performance of fMRI.

• Therapeutic Science for Rehabilitation
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• v.7 no.2
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• pp.9-16
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• 2018

Objective : The aim of this research was to provide EEG (electroencephalogram) basic data in clinical areas through identifying measurement tools, measurement methods, and evaluation and analysis method of the EEG which is a neurological change measurement of patients with brain injury. Methods : Previous studies were found in an electronic database (e.g., PubMed, Science Direct). The keyword search terms were 'Electroencephalography', 'stroke', 'intervention OR training'. Results : Utilitizing brain-computer interface, the EEG, which is a tool for measuring the effects of rehabilitation through changes of brain activation state. Also, it could identify functional brain reorganization mechanism. Whenever a research utilized the EEG, which is composed of various channels, different types of electrode, and varied electrode locations. Conclusions : Through this review, we found that Electroencephalography is possible to neurologically verify the effectiveness of intervention and formulate an intervention strategy for efficient occupational therapy.

• Investigative Magnetic Resonance Imaging
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• v.8 no.1
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• pp.1-8
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• 2004

Purpose : Information about electrical activity inside the brain during fMRl scans is very useful in monitoring physiological function of the patient or locating the spatial position of the activated region in the brain. However, many additional noises appear in the EEG signal acquired during the MRI scan. Gradient induced noise is the biggest one among the noises. In this work, we propose a gradient noise reduction method using the independent component analysis (ICA) method. Materials and Methods : We used a 29-channel MR-compatible EEG measurement system and a 3.0 Tesla MRI system. We measured EEG signals on a subject lying inside the magnet during EPI scans. We selectively removed the gradient noise from the measured EEG signal using the ICA method. We compared the results with the ones obtained with conventional averaging method and PCA method. Results : All the noise reduction methods including the averaging and PCA methods were effective in removing the noise in some extent. However, the proposed ICA method was found to be superior to the other methods. Conclusion : Gradient noise in EEG signals acquired during fMRI scans can be effectively reduced by the ICA method. The noise-reduced EEG signal can be used in fMRI studies of epileptic patients or combinatory studies of fMRI and EEG.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.17 no.1
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• pp.38-46
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• 2024

In general, EEG signal analysis has been the subject of several studies due to its ability to provide an objective mode of recording brain stimulation, which is widely used in brain-computer interface research with applications in medical diagnosis and rehabilitation engineering. In this study, we developed EEG reception hardware to measure electroencephalograms and implemented a processing system, classifying it into server and data processing. It was conducted as an intermediate-stage research on the implementation of a brain-computer interface using electroencephalograms, and was implemented in the form of predicting the user's arm movements according to measured electroencephalogram data. Electroencephalogram measurements were performed using input from four electrodes through an analog-to-digital converter. After sending this to the server through a communication process, we designed and implemented a system flow in which the server classifies the electroencephalogram input using a convolutional neural network model and displays the results on the user terminal.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.29 no.7
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• pp.508-514
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• 2018

An electroencephalogram (EEG) evaluates the electrical activity generated by brain cell interactions that occur during brain activity, and an EEG can evaluate the brain activity caused by hand movement. In this study, a 16-channel EEG was used to measure the EEG generated before and after hand movement. The measured data can be classified as a supervised learning model, a support vector machine (SVM). To shorten the learning time of the SVM, a feature extraction and vector dimension reduction by filtering is proposed that minimizes motion-related information loss and compresses EEG information. The classification results showed an average of 72.7% accuracy between the sitting position and the hand movement at the electrodes of the frontal lobe.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2000.04a
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• pp.47-52
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• 2000

단기기억 작업시 집중도를 평가하기 위하여 평가용 task를 설계하고 5단계의 난이도에 대하여 실험을 실시하였다. 난이도에 따른 집중도의 변화를 측정하기 위하여 주관평가를 실시하고 행동지표와 생리신호를 측정하였다. 측정된 생리신호 중 전두엽(Ep1, Ep2)에서 측정한 뇌전도 신호의 $\alpha$-band와 $\beta$-band의 전력의 비와, 심박변화율의 전력 스펙트럼의 MF/(LF+MF+HF)비를 구하였다. 실험 결과 집중도 높게 보고된 단계에서 task를 수행함에 따라 뇌전도 신호의 $\alpha$/$\beta$ 비와 심박변화율의 MF/(LF+MF+HF) 비도 점차로 증가하였다.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.133-153
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• 2023

In this paper, we propose a novel approach to investigating brain-signal measurement technology using Electroencephalography (EEG). Traditionally, researchers have combined EEG signals with bio-signals (BSs) to enhance the classification performance of emotional states. Our objective was to explore the synergistic effects of coupling EEG and BSs, and determine whether the combination of EEG+BS improves the classification accuracy of emotional states compared to using EEG alone or combining EEG with pseudo-random signals (PS) generated arbitrarily by random generators. Employing four feature extraction methods, we examined four combinations: EEG alone, EG+BS, EEG+BS+PS, and EEG+PS, utilizing data from two widely-used open datasets. Emotional states (task versus rest states) were classified using Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) classifiers. Our results revealed that when using the highest accuracy SVM-FFT, the average error rates of EEG+BS were 4.7% and 6.5% higher than those of EEG+PS and EEG alone, respectively. We also conducted a thorough analysis of EEG+BS by combining numerous PSs. The error rate of EEG+BS+PS displayed a V-shaped curve, initially decreasing due to the deep double descent phenomenon, followed by an increase attributed to the curse of dimensionality. Consequently, our findings suggest that the combination of EEG+BS may not always yield promising classification performance.

• Journal of Biomedical Engineering Research
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• v.25 no.3
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• pp.227-233
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• 2004

In this paper, it is proposed that the four channel electrode arrangement at a frontal lobe and the noise reduction method using a JADE for the EEG biofeedback system. The proposed electrode arrangement is based on the retina-cornea dipole model. Using JADE and signals which are acquired by the proposed arrangement, four independent components are separated. To estimate a pure EEG component among four components, it is measured that a ratio of alpha wave to the whole signal and then the component that has a maximum value is considered as a pure EEG which the noise is eliminated. As a result of experiments, the proposed methods are effective in reduction of noises during acquisition of the EEG.

• Journal of Korea Society of Industrial Information Systems
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• v.18 no.5
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• pp.15-18
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• 2013

This paper presents an analog front-end for electroencephalogram(EEG) signal processing. Since EEG signals are typically weak and located at very low frequencies, it is imperative to implement an amplifier with high gain, high common-mode rejection ratio(CMRR) and good noise immunity at very low frequencies. The analog front-end of this paper consists of a programmable-gain instrumentation amplifier and a band-pass filter. A frequency chopping technique is employed to remove the low-frequency noise. The circuits were fabricated in 0.18um CMOS technology and measurements showed that the analog front-end has the maximum gain of 60dB and >100dB CMRR over the programmable gain range.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06a
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• pp.142-145
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• 2011

뇌전도 기반 뇌-컴퓨터 인터페이스 기술은 신체 움직임이 불가능하거나 불편한 사람에게 새로운 의사전달 수단이 될 수 있으며 일반인에게도 상상만으로 컴퓨터 혹은 기계에 명령을 내릴 수 있게 하는 기술이다. 본 논문에서는 뇌-컴퓨터 인터페이스 연구 분야에 잘 알려진 Common Spatial Pattern (CSP), Invariant Common Spatial Pattern (iCSP) 그리고 Common Spatio-Spectral Pattern (CSSP) 알고리즘들의 성능을 비교 분석하였고, CSSP에 불변성(invariant)을 고려한 iCSSP를 제안하였다. 9명의 피험자로부터 상상움직임 실험을 통해 18셋의 뇌전도 데이터를 측정하였고, 4가지 알고리즘들을 성능 면에서 비교하였다. 그 결과 CSSP의 성능과 차이가 크지는 않지만, 본 연구에서 제안한 노이즈를 고려하여 최적의 필터를 구성하는 iCSSP에 대하여 더 나은 성능을 보여주는 결과들을 확인할 수 있었다.