• Journal of IKEEE
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• v.22 no.4
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• pp.1109-1114
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• 2018

Electroencephalogram (EEG) is a tool used to study brain activity caused by external stimuli. In this process, artifacts are mixed and it is easy to distort the signal, so post-processing is necessary to remove it. Independent Component Analysis (ICA) is a widely used method for removing artifact. This method has a disadvantage in that it has excellent performance but some loss of brain wave information. In this paper, we propose a method to reduce EEG information loss by restricting the filter coverage using eye blink information obtained from Eyetracker. We then compared the results of the proposed method with the conventional method using quantization methods such as Signal to Noise Ratio (SNR) and Spectral Coherence (SC).

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2002.11a
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• pp.250-254
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• 2002

컴퓨터의 발달과 더불어 인간과 컴퓨터 인터페이스에 있어서도 많은 발전을 하고 있다. 본 연구는 두뇌-컴퓨터 인터페이스(brain-computer interface : BCI)를 위해서 인간에 있어서 가장 간단한 의사문제라고 여겨지는 긍정이나 부정을 선택할 때 나타나는 뇌파를 AR 모델을 이용하여 시간-주파수 분석을 한 후 topographical map을 그렸다. 그 결과 문제에 대답하는 시점 전후에서 파워스펙트럼이 유사하였고, 피험자가 문제를 읽고 판단하고, 동작하는 시점(reaction time : RT) 전으로 1초 ~ 0.5초 사이에 전두엽과 두정엽 부위에서 16Hz ~ 24Hz, 80 ∼ 88Hz의 주파수 대역에서 유의미한 차이를 보였다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.170-174
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• 2007

Although research efforts for brain waves have prospered in medicine and engineering, acupuncture still has a long way to go regarding researches on brain waves analysis. Thus this study set out to analyze brain waves stimulated by indirect mugwort moxibustion, which was part of acupuncture techniques, and to investigate their correlations with the automatic nervous system. For the experiments, stimulation was given to Jungwan, Shingwol and Gwanwon, which were some of the spots on the body suitable for acupuncture, through indirect mugwort moxibustion. The subjects' brain waves were measured before the stimulation, during the stimulation, and one hour and two hours after the stimulation. The measurements were analyzed with Matlab 7.0 for FFT and frequency power spectrum. Then the ${\alpha}$, ${\beta}$, ${\delta}$, and ${\theta}$ waves were analyzed and examined for changes to the percentage of each frequency and to the amplitude of vibration according to the stages of stimulation. The EEG data of the entire brain were translated into FFT to analyze the percentage of the ${\alpha}$, ${\beta}$, ${\delta}$, and ${\theta}$ waves. As a result, the ${\alpha}$ waves recorded a double increase after the stimulation. The power spectrum analysis results of the entire brain decreased the ${\alpha}$ and ${\beta}$ waves dropping in the energy level, which suggested that the parasympathetic nerves were activated. When the results of the study were compared with those of the previous study, it's confirmed that indirect moxibustion stimulation could cause changes to the automatic nervous system and bring stability to those who were nervous or under stress due to the proportionate increase of the ${\alpha}$ waves.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.947-950
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• 2015

In the cause of car accidents in Korea, drowsy driving has shown that it is larger fctors than drunk driving. Therefore, in order to prevent drowsy driving accidents, drowsiness detection and warning system for drivers has recently become a very important issue. Furthermore, Many researches have been published that measuring alpha wave of EEG signals is the effective way in order to be aware of drowsiness of drivers. In this study, we have developed EEG measuring device that applies a signal processing algorithm using the LabView program for detecting drowsiness. According to results of drowsiness inducement experiments for small test subjects, it was able to detect the pattern of EEG, which means drowsy state based on the changing of power spectrum, counterpart of alpha wave. After all, Comparing to the results of drowsiness pattern between commercial equipments and developed device, we could confirm acquiring similar pattern to drowsiness pattern. With this results, the driver's drowsiness prevention system expect that it will be able to contribute to lowering the death rate caused by drowsy driving accidents.

• Korean Journal of Biological Psychiatry
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• v.3 no.2
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• pp.216-221
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• 1996

The quantitative EEGs of obsessive-compulsive disorder patients were analyzed using spectral analysis and compared to age and sex-matched controls. The subjects were 19 patients(men=15, women=4) suffering from obsessive-compulsive disorder(DSM-III-R). Absolute power, relative power and interhemispheric asymmetry of EEG were used to compare obsessive-compulsive disorder patients with controls. In order to fit the EEG data to a normal distribution, a log transformation of power values of every bandwidth in each deviation was calculated prior statistical analysis. The Wilcoxon rank test was performed to compare obsessive-compulsive group to the control group. In obsessive-compulsive disorder, abnormalities of quantitative EEGs are prominent in fronto-central. These results ore compatible with other brain imaging studies of obsessive-compulsive disorder and suggested that fronto-central area plays an important role in the pathophysiology of obsessive-compulsive disorder.

• Science of Emotion and Sensibility
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• v.16 no.3
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• pp.409-416
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• 2013

Attention is one of important cognitive functions in human affecting on the selectional concentration of relevant events and ignorance of irrelevant events. The discrimination of attentional and inattentional status is the first step to manage human's attentional capability using computer assisted device. In this paper, we newly combine the non-linear recurrence pattern analysis and spectrum analysis to effectively extract features(total number of 13) from the electroencephalographic signal used in the input to classifiers. The performance of diverse types of attention-inattention classifiers, including supporting vector machine, back-propagation algorithm, linear discrimination, gradient decent, and logistic regression classifiers were evaluated. Among them, the support vector machine classifier shows the best performance with the classification accuracy of 81 %. The use of spectral band feature set alone(accuracy of 76 %) shows better performance than that of non-linear recurrence pattern feature set alone(accuracy of 67 %). The support vector machine classifier with hybrid combination of non-linear and spectral analysis can be used in later designing attention-related devices.

• Sleep Medicine and Psychophysiology
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• v.8 no.2
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• pp.121-128
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• 2001

Objectives: In narcoleptic patients diagnosed with ICSD (international classification of sleep disorders, 1990) criteria, nocturnal polysomnography, and MSLT (multiple sleep latency test), we tried to find characteristic features of quantitative electroencephalography (QEEG) in a wakeful state. Methods: We compared eight drug-free narcoleptic patients with sex- and age-matched normal controls, using computerized electroencephalographic mapping technique and spectral analysis. Absolute power, relative power, interhemispheric asymmetry, interhemispheric and intrahemispheric coherence, and mean frequency in each frequency band (delta, theta, alpha and beta) were measured and analyzed. Results: Compared with normal controls, narcoleptic patients showed decrease in monopolar interhemispheric coherence of alpha frequency bands in occipital ($O_1/O_2$), parietal ($P_3/P_4$), and temporal ($T_5/T_6$) areas and beta frequency band in the occipital ($O_1/O_2$) area. Monopolar intrahemispheric coherences of alpha frequency bands in left hemispheric areas ($T_3/T_5$, $C_3/P_3$ & $F_3/O_1$) decreased. Decrease of monopolar interhemispheric asymmetry of delta frequency band in the occipital ($O_1/O_2$) area was also noted. The monopolar absolute powers of beta frequency bands decreased in occipital ($O_2,\;O_z$) areas. Conclusion: Decreases in coherences of narcoleptic patients compared with normal controls may indicate fewer posterior neocortical interhemispheric neuronal connections, and fewer left intrahemispheric neuronal connections than normal controls in a wakeful state. Therefore, we suggest that abnormal neurophysiological sites of narcolepsy may involve complex areas such as neocortex and subcortex as well as the brainstem.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2002.05a
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• pp.210-214
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• 2002

집중도 변화와 정신피로는 정의 방향의 평가와 부의 방향의 평가가 있으며, 작업 수행시의 집중변화와 정신피로의 평가는 정의 방향보다 부의 방향으로의 평가가 용이하다. 따라서, 저자들은 먼저 피험자에게 부가할 정신작업부하로 난이도 조정이 용이한 연산작업을 사용하여 작업수행시 가장 높은 집중을 요구하는 하나의 난이도를 선정하고 피험자에게 반복 수행시키면서 생리량과 심리량을 동시에 측정하였다. 측정된 생리량은 뇌파, 심전도, 호흡, 맥파, 말초피부온이며 심리량은 저자들이 개발한 정신피로설문지를 사용하였다. 분석결과 작업수행시 심박변화율의 전력스펙트럼 MF/(LF+HF)비가 감소하였으며, 작업이 반복됨에 따라 집중이 저하되고 정신피로가 증가하였다. 특히, 뇌파의 beta파와 호흡간격이 집중도의 변화를 말초피부온이 정신피로를 잘 반영하였다.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.9
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• pp.361-366
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• 2018

Previous studies on fear of crime used survey and interview to measure fear of crime. However, though these methods can measure fear of crime in past events, they cannot measure real time fear of crime. In this paper, we use EEG to measure fear of crime in real time. We measure and analyze the EEG of subjects watching the video and confirm the difference between three groups classified according to the degree of fear of crime. As a result, about two times more beta waves are shown when a group of subjects with a high degree of fear of crime watches the images of fear of crime and 1.5 times more beta waves are shown among the other groups. Although watching videos related to the crime increased the beta waves, the police video showed little increase in beta waves because the subjects can sense safety in the video even if it is related to crime.

• Proceedings of the KOSOMBE Conference
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• v.1994 no.12
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• pp.138-141
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• 1994