• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2008.04a
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• pp.147-150
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• 2008

사람과 컴퓨터의 인터페이스를 위한 방법에는 여러 가지가 있으나 보다 편리하고 몸이 불편한 사람들도 이용할 수 있도록 하기 위하여 최근에는 사람의 생체신호를 이용하여 Interface하기위한 연구가 활발히 진행되고 있다. 생체신호에는 뇌파, 근전도, 심전도, 등 여러 가지가 있지만 이를 위해 사용자의 가장 많은 정보를 내포하고 있는 뇌파에 대한 연구는 필수적이다. 따라서 세계 여러 나라에서 뇌파에 대한 연구가 진행되고 있지만 아직까지는 뇌파에 대한 정확한 분석이 이루어지지 못하고 있는 실정이다. 이를 위해 본 논문에서는 정확한 뇌파분석을 위한 뇌파 유발 자극 방법 및 측정법을 제안하고 사람이 몸을 움직이고자 하는 상상을 할 때 ERS(Event-Related Synchronization), ERD(Event-Related Desynchronization)를 분석함으로써 사람의 의도를 뇌파를 통해 분석하고자 한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2002.11a
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• pp.801-804
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• 2002

뇌기능 연구 수단으로 널리 사용되고 있는 뇌파의 시각적 분석 및 정량적 분석시 오차를 증가시키는 원인이 되어 왔던 잡파(artifact)를 제거 대상이 아닌 제어 신호로써 활용한다. 본 연구에서는 다양한 잡파 중 뇌파 측정시 가장 잘 포함되고, 시각적으로 쉽게 구별이 가능한 안면근(facial muscle) 신호를 이용한다. 측정된 뇌파에 파워스펙트럼(power spectrum)을 적응하여 뇌파를 분석하고, Backpropagation 알고리즘을 이용하여 전 처리된 뇌파를 인식하는 2 채널 실시간 인식(recognition) 및 분류(classification) 시스템을 구현한다. 이와 같이 구현된 시스템을 이용하여 5 방향(상, 하, 좌, 우, 정지) 제어를 실시함으로써 뇌-컴퓨터간 통신을 통한 방향제어 시스템을 구현하였다.

• 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.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.5
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• pp.96-103
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• 1999

본 논문에서는 sliding-DFT(SDFT)를 계수의 유한 비트 근사구현에 기초하여 실시간 구현하는 기법을 제시하고, 이의 오차영향을 해석하였다. 오차의 영향을 오차전력과 신호전력비율(noise-to-signal power ratio : NSR)로 하여 이를 해석적으로 유도하였다. 가우스 렌덤신호 및 사람의 수면 EEG 신호를 대상으로 수행한 시뮬레이션 결과가 해석식과 잘 일치하는 것을 보임으로써 본 연구에서 얻은 해석식을 확인하였다.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.9 no.6
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• pp.729-738
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• 2019

Recently, we aim to improve the quality of virtual reality contents based on quantitative analysis results of emotions through combination of emotional recognition field and virtual reality field. Emotions are analyzed based on the participant's vital signs. Much research has been done in terms of signal analysis, but the methodology for quantifying emotions has not been fully discussed. In this paper, we propose a normalization function design and expression method to quantify the emotion between various bio - signals. Use the Brute force algorithm to find the optimal parameters of the normalization function and improve the confidence score of the parameters found using the true and false scores defined in this paper. As a result, it is possible to automate the parameter determination of the bio-signal normalization function depending on the experience, and the emotion can be analyzed quantitatively based on this.

• Science of Emotion and Sensibility
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• v.8 no.4
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• pp.403-413
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• 2005

The purpose of the present study was to investigate the responses to positive/negative emotional stimuli for the different sensitivities of behavioral activation system (BAS) and behavioral inhibition system (BIS). We recorded If-channel EEG data for 8 BAS sensitive subjects an48 BIS sensitive subjects. EEGs were analyzed with LORETA (Low-resolution electromagnetic tomography) From scalp-recorded electrical potential distribution, LORETA computes the three-dimensional intracerebral distributions of current density for specified EEG frequency bands. hs results , significant differences between the BAS sensitive group ant the BIS sensitive group appeared LORETA alpha activities over the prefrontal lobe and the cingulate gyrus. Prefrontal regions and limbic system including cingulate gyrus are involved in emotional processing. Moreover, subjects with the high BAS sensitivity. responded more sensitively to the positive stimulation than subjects with the high BIS sensitivity. Therefore, our results suggest the possibility of correlation between BAS/BIS sensitivity and positive/negative emotional stimuli.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.1
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• pp.122-127
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• 2009

In this paper, we measured EEG signals on frontal and Broca's area when subjects imagine to speak A or B or C or D. These signals were analyzed by Event-Related Spectral Perturbation (ERSP), Inter-Trial Coherence (ITC) and Event Related Potential (ERP) methods. As a result, high coherences were showed at 1$\sim$13Hz during 0$\sim$300ms after the stimuli of each character and P300 was seen clearly and there are several differences between the ERP results. However, unlike the motivation of this study to classify the characters, it is impossible that we can classify each intention or each character cause these differences. Nevertheless, this paper suggest an application system using this results so BCI can provide various services.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.7
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• pp.2491-2496
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• 2010

The DFA(detrended fluctuation analysis) which is included the correlation property of the EEG is used to analysis the depth of anesthesia. We studied ASA I or II adult patients supported by the society of anesthesiologists. Patients with history of dementia and neurological disorder are excluded. Average age is $48.9{\pm}10.9$ old, average weight is $57.1{\pm}8.2$ kg and average hight is $158{\pm}6.6$cm of the patients under the operation. Anesthesia medicine is Sevoflurane and the stages of anesthesia are 6 stages, that is pre-operation, induction, right after induction, stop the medicine and post-operation. Among the scaling exponent ${\alpha}1$, ${\alpha}2$, ${\alpha}3$ we know that ${\alpha}1$, ${\alpha}3$, were well appeared to discriminate pre-operation, induction, right after induction, stop the medicine and post-operation. So we confirmed that the parameters is useful to the depth of anesthesia.

• The Journal of the Korea Contents Association
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• v.22 no.10
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• pp.861-869
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• 2022

In patients with Tourette's syndrome, a tic occurs when Mu wave synchronization is broken. Conversely, when Mu wave synchronization is achieved, a tick does not occur. When the tic is suppressed, the cognitive control response process is changed, and if the neurofeedback training that adjusts the EEG frequency power is performed with the changed, the patient will be treated autonomously without artificially suppressing the tic. The results of the research test suggest that if the tic patient does not artificially synchronize mu waves in the premotor cortex (Frontal Cortical 3 site), and if EEG control is performed autonomously like neurofeedback training, as a result, tics do not occur. Cognitive control response processes are altered when a subject is inhibited from tics. By training the altered cognitive control with neurofeedback that modulates EEG frequency power, the patient can be treated autonomously without artificially suppressing the tic.Mu-wave synchronizationcan now be added to existing neurofeedback treatment protocols such as SMR reinforcement, theta-beta-wave imbalance correction, and alpha-wave reinforcement. This study will be used in follow-up studies and clinical trials to more scientifically verify the neurofeedback treatment protocol, a treatment for patients with Tourette's syndrome.

• The Journal of the Acoustical Society of Korea
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• v.41 no.5
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• pp.518-524
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• 2022

In this paper, we propose a method for combining convolutional neural networks and attention mechanism to improve the recognition performance of emotional stress from Electroencephalogram (EGG) signals. In the proposed method, EEG signals are decomposed into five frequency domains, and spatial information of EEG features is obtained by applying a convolutional neural network layer to each frequency domain. As a next step, salient frequency information is learned in each frequency band using a gate transformer-based attention mechanism, and complementary frequency information is further learned through inter-frequency mapping to reflect it in the final attention representation. Through an EEG stress recognition experiment involving a DEAP dataset and six subjects, we show that the proposed method is effective in improving EEG-based stress recognition performance compared to the existing methods.