• Journal of Korean Society of Industrial and Systems Engineering
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• v.35 no.2
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• pp.64-70
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• 2012

This paper presents a statistical analysis method for the selection of electroencephalogram (EEG) electrode positions and spectral features to recognize emotion, where emotional valence and arousal are classified into three and two levels, respectively. Ten experiments for a subject were performed under three categorized IAPS (International Affective Picture System) pictures, i.e., high valence and high arousal, medium valence and low arousal, and low valence and high arousal. The electroencephalogram was recorded from 12 sites according to the international 10~20 system referenced to Cz. The statistical analysis approach using ANOVA with Tukey's HSD is employed to identify statistically significant EEG electrode positions and spectral features in the emotion recognition.

• Proceedings of the KIEE Conference
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• 1987.07b
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• pp.1271-1275
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• 1987

With the stochastic process which consists of the harmonic sinusoid and the white nosie, the power spectrum of background EEG is estimated by the Pisarenko Harmonic Decomposition. The estimating results are examined and compared with the results from the maximum entropy spectral estimation, and the optimal order of this model can be determined from the eigen value's fluctuation of autocorrelation of background EEG. From the comparing results, this paper ensures that this method is possible to analyze the power spectrum of background EEG.

• Journal of Biomedical Engineering Research
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• v.30 no.4
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• pp.327-332
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• 2009

In this paper, new index was developed to estimate the depth of anesthesia during general anesthesia using EEG. Analysis of the power spectral density(PSD) of EEG was used to develop new parameters because EEG signal tends to have slow wave during anesthesia. Classifier for index creator was developed by using SEF, BDR and BTR parameters, which are calculated by power spectral density. EEG data were obtained from 7 patients (ASA I, II) during general anesthesia with Sevoflurane. The anesthetic depth evaluation indexes ranged from 0 to 100. The average were $86.05{\pm}10.1$, $36.98{\pm}20.2$, $15.33{\pm}13.6$, $50.87{\pm}16.5$ and $87.72{\pm}11.7$ for the states of pre-operation, induction of anesthesia, operation, awaked and post-operation, respectively. The results show that while the depth of anesthesia was evaluated, more accurate information can be provided for anesthetician.

• 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).

• Journal of Dental Anesthesia and Pain Medicine
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• v.17 no.4
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• pp.241-251
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• 2017

Devices that monitor the depth of hypnosis based on the electroencephalogram (EEG) have long been commercialized, and clinicians use these to titrate the dosage of hypnotic agents. However, these have not yet been accepted as standard monitoring devices for anesthesiology. The primary reason is that the use of these monitoring devices does not completely prevent awareness during surgery, and the development of these devices has not taken into account the neurophysiological mechanisms of hypnotic agents, thus making it possible to show different levels of unconsciousness in the same brain status. An alternative is to monitor EEGs that are not signal processed with numerical values presented by these monitoring devices. Several studies have reported that power spectral analysis alone can distinguish the effects of different hypnotic agents on consciousness changes. This paper introduces the basic concept of power spectral analysis and introduces the EEG characteristics of various hypnotic agents that are used in sedation.

• Journal of Oriental Neuropsychiatry
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• v.18 no.1
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• pp.37-61
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• 2007

Objective : By examining EEG status in Korean Traditional Medicine (KTM) from the viewpoint of 'form-qi theory(形氣論)', We wish to prepare for the fundamentals of applicability of KTM diagnoses to EEG. In addition, through reinterpretation of existing Western Medicine reports from the viewpoint of KTM, We tried to find out interrelationship between them. Method : In this paper, a methodology applicable to KTM diagnoses of EEG is presented from the EEG features in waveform characteristics, personalized diversity, and cognitive activity reflection. Results : Frequency bands are assigned to corresponding one of the eight trigrams in terms of yin/yang balance, which is analogous with EEG spectrum analysis mostly used in EEG quantification. The amplitude ratio of each EEG for each frequency band gives meaningful index numbers which can be used in EEG data interpretation, and every index number is named after the sixty four hexagrams. These approaches are adopted through both '4-band classification system and '6-band classification system', and applied to pre-existing reported EEG data obtained from normal adults. These analyses show that changes and distribution pattern in the index numbers are observed as a whole on both left-right line and front-back line connecting EEG measurement cephalic electrodes. And differences in distribution pattern of three index numbers deduced from '6-band classification system' are discussed according to constitution. Conclusion : The index numbers introduced here, which are the spectral power ratio for each EEG, are based on KTM yin/yang balance. These index numbers vary according to cephalic location, so its application in terms of traditional meridian theory is strongly expected. The index number distribution also shows different patterns according to constitution.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.11
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• pp.265-272
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• 2020

In the research of brain computer interface (BCI) technology, one of the big problems encountered is how to deal with some people as called the BCI-illiteracy group who could not control the BCI system. To approach this problem efficiently, we investigated a kind of spectral EEG characteristics in the prior resting state in association with BCI performance in the following BCI tasks. First, spectral powers of EEG signals in the resting state with both eyes-open and eyes-closed conditions were respectively extracted. Second, a convolution neural network (CNN) based binary classifier discriminated the binary motor imagery intention in the BCI task. Both the linear correlation and binary prediction methods confirmed that the spectral EEG characteristics in the prior resting state were highly related to the BCI performance in the following BCI task. Linear regression analysis demonstrated that the relative ratio of the 13 Hz below and above the spectral power in the resting state with only eyes-open, not eyes-closed condition, were significantly correlated with the quantified metrics of the BCI performance (r=0.544). A binary classifier based on the linear regression with L1 regularization method was able to discriminate the high-performance group and low-performance group in the following BCI task by using the spectral-based EEG features in the precedent resting state (AUC=0.817). These results strongly support that the spectral EEG characteristics in the frontal regions during the resting state with eyes-open condition should be used as a good predictor of the following BCI task performance.

• Sleep Medicine and Psychophysiology
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• v.15 no.1
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• pp.33-38
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• 2008

Introduction: It has been proposed that narcolepsy and REM sleep behavior disorder (RBD) have overlapped symptom profile and pathophysiology. This study was aimed at measuring and comparing changes in EEG frequency band of REM sleep in narcolepsy and RBD, applying EEG spectral analysis method. Methods: Nine patients diagnosed as narcolepsy and the same number of RBD patients were studied. Spectral analysis of the REM sleep EEG was performed in each patient on 9 epochs selected evenly from the first, second, and third REM periods. Then, we compared frequency band percentages of REM sleep EEG in narcolepsy and RBD. Results: Narcolepsy patients had significantly higher delta frequency ratio than RBD ones (p=0.00). In alpha and beta2 frequency bands, RBD patients showed higher percentage than narcolepsy ones. Slow wave sleep was more prevalent in narcolepsy patients. But, no difference of REM sleep percentage was found between the two groups (p=0.93). Conclusion: Higher delta frequency ratio in REM sleep of narcolepsy patients than RBD ones reflects that sleep-promoting mechanism is more dominant in narcolepsy than in RBD.

• Journal of Biomedical Engineering Research
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• v.22 no.5
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• pp.425-430
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• 2001

The Purpose of this study was to find the way of discriminating EEG for some mental activity. which are not characterized within linear spectral analysis but with non-linear analysis . We lave investigated the way of characterizing EEG changes during emotional and cognitive states in healthy volunteered subjects who responded to three designed status. in which the subjects were relaxing with ease and eyes closed. listening to music and computing a simple subtraction with eyes closed. Especially, we estimated EEG dimensional complexity by Skinner s Point-wise correlation dimension(PD2) method for each mental states. As a result it has been found that the subjects, who responded that the\ulcorner had concentrated well during the arithmetic task. show higher PD2 in their non-linear EEG measures. in comparison with the subjects who responded that they had not concentrated during the task This highness of PD2 is also significant in statistical analysis. A subject who had the highest score in evaluating the intensity of induced emotion during emotional task shows significantly lower PD2 in statistical analysis than other subjects who had lower scores. Linear spectral analysis was also performed on these data. However, they did not show and significant difference. Only non-linear dynamical analysis shows the significant different result on these mental status.

• The Korean Journal of Physiology and Pharmacology
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• v.9 no.1
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• pp.17-22
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• 2005

We examined whether the abnormal EEG state by NMDA receptor blocker MK-801 can be reversed by typical and atypical antipsychotics differentially by comparing their spectral profiles after drug treatment in rats. The spectral profiles produced by typical antipsychotics chlorpromazine (5 mg/kg, i.p.) and haloperidol (0.5 mg/kg, i.p.) were differ from that by atypical antipsychotic clozapine (5 mg/kg, i.p.) in the rats treated with or without MK-801 treatment (0.2 mg/kg, i.p.) which produce behavioral abnormalities like hyperlocomotion and stereotypy. The dissimilarity between the states produced by antipsychotics and the control state was examined with the distance of the location of the canonical variables calculated by stepwise discriminant analysis with the relative band powers as input variables. Although clozapine produced more different state from normal state than typical antipsychotics, clozapine could reverse the abnormal schizophrenic state induced by MK-801 to the state closer to the normal state than the typical antipsychotics. The results suggest that atypical anesthetic can reverse the abnormal schizophrenic state with negative symptom to the normal state better than typical antipsychotic. The results indicate that the multivariate discriminant analysis using the spectral parameters can help differentiate the antipsychotics with different actions.