• Journal of Biomedical Engineering Research
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• v.24 no.2
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• pp.99-106
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• 2003

In this paper, a method of discriminating EEG for diagnoses of mental activity is proposed. The proposed method for classification of schizophrenia and normal EEG is based on the wavelet transform and the artificial neural network. The wavelet coefficients of $\alpha$ band, $\beta$ band, $\theta$ band, and $\delta$ band are obtained using the wavelet transform. The magnitude, mean, and variance of wavelet coefficients for each EEG band are applied to the input data of the system's ANN. The architecture of the ANN s a four layered feedforward network with two hidden layer which implements the error back propagation learning algorithm. Through the classification of schizophrenia composed of 19 ANNs corresponding to 19 channels, the classifying system show that it can classify the 100% of the normal EEG group and the 86.67% of the schizophrenia EEG group.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.4
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• pp.351-360
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• 2016

The purpose of this study is to predict visual-spatial working memory performance through the characteristics of an electroencephalogram (EEG) in the resting state. The 31 study participants, middle school students with various to academic performance, were underwent visual-spatial working memory test in the Comprehensive Attention Test (CAT) on December in 2014. Each 7 and 6 participants were divided into an Excellent Working Memory (EWM) group and Poor Working Memory (PWM) group depending on the forward/backward working memory scores. The EEG measurements and analysis of the data from a Brain Function Tester were performed by the two groups. A Mann-Whitney Test was used to examine the statistical differences between them. The activation of high beta (${\beta}H$) at the Fp1 and Fp2 sites in the left and right hemisphere, and that of the low beta (${\beta}L$) in the right hemisphere in the EWM group was significantly higher than that in the PWM group. In conclusion, there is a correlation between the visual-spatial working memory performance and the activation of ${\beta}H$ and ${\beta}L$ in the resting state and a close correlation that of ${\beta}L$ in the right hemisphere in terms of mental activity and faculty. Therefore, the visual-spatial working memory performance can be predicted by the activation of ${\beta}H$ and ${\beta}L$ in the resting state. The activation of EEG can be applied as an assessment tool and provide basis data for visual-spatial working memory performance.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.1
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• pp.462-470
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• 2015

This study examined for which socio-economic difference effects on brain function and Problem behavior syndrome in children. About a children with no disorders, diseases or cognitive dysfunction-30 were from LIC children and another 30, from MC ones, the study was conducted by measuring and analyzing the data using brain function analysis and K-CBCL from January to April, 2013. The results of the study are as follows. First, it was found that the ratio of LIC's theta(${\Theta}$) and SMR waves and that of delta(${\delta}$), high beta(${\beta}h$), alpha(${\alpha}$) and low beta(${\beta}l$) waves showed significantly higher values than MC children. Second, concerning the symptoms of child behavioral problems, LIC showed significantly higher values than MC children in symptoms of the body, depression and anxiety, social immaturity, thinking problems, attention problems, aggression, internalization, externalization, overall behavioral problems, and emotional instability. MC children showed significantly higher values than LIC chidren in symptoms of social, academic-performance, total social skills. In conclusion, the significant difference of the brain functions and the symptoms of child behavioral problems between LIC and MC children showed that the socio-ecnomic difference has an influence on the same functions and symptoms above.

• 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 rehabilitation welfare engineering & assistive technology
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• v.11 no.1
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• pp.45-52
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• 2017

EEG signals that are the same as those that have the same disabled people. So, the EEG signals are becoming the next generation. In this paper, we propose an internet of things system that controls the indoor environment using EEG signal. The proposed system consists EEG measurement device, EEG simulation software and indoor environment control device. We use data as EEG signal data on emotional imagination condition in a comfortable state and logical imagination condition in concentrated state. The noise of measured signal is removed by the ICA algorithm and beta waves are extracted from it. then, it goes through learning and test process using SVM. The subjects were trained to improve the EEG signal accuracy through the EEG simulation software and the average accuracy were 87.69%. The EEG signal from the EEG measurement device is transmitted to the EEG simulation software through the serial communication. then the control command is generated by classifying emotional imagination condition and logical imagination condition. The generated control command is transmitted to the indoor environment control device through the Zigbee communication. In case of the emotional imagination condition, the soft lighting and classical music are outputted. In the logical imagination condition, the learning white noise and bright lighting are outputted. The proposed system can be applied to software and device control based BCI.

• Korean Journal of Psychosomatic Medicine
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• v.25 no.1
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• pp.63-72
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• 2017

Objectives : Attention-deficit hyperactivity disorder(ADHD) is characterized by significant impairments in executive functions, with a prevalence of approximately 3-5% of all children worldwide. The goal of this study was to examine the relationship between executive functions and electrophysiological activities in children and adolescents with ADHD. Methods : In 31 patients with ADHD, resting-state EEG was recorded, and Comprehensive Attention Test(CAT), Stroop Color-Word Inference Test(Stroop CWIT), Trail Making Test(TMT), and Wisconsin Card Sorting Test(CST) were administered. Korean version of the ADHD Rating Scale(K-ARS) was assessed. Results : Alpha and beta power positively correlated with the Attention Quotient(AQ), while delta power negatively correlated with AQ from CAT. In the Stroop CWIT, decreased delta power and increased beta power were related to higher performance. Power of the alpha band increased with higher TMT performance. Moreover, delta power negatively correlated with good performance on the CST, while alpha and high gamma band showed a positive correlation. Correlation with the parent-rating of ADHD symptoms showed a negative correlation between alpha power and higher scores on the K-ARS. Conclusions : These findings indicate that relative power in higher frequency bands of EEG is related to the higher executive function in children and adolescents with ADHD, while the association with the relative power in lower frequency bands of EEG seem to be vice versa. Furthermore, the findings suggest that QEEG may be a useful adjunctive tool in assessing patients with ADHD.

• Journal of Bio-Environment Control
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• v.23 no.1
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• pp.39-42
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• 2014

The objective of this study was to estimate cardinal temperatures for germination of lettuce (Lactuca sativar L.) using bilinear, parabolic, and beta distribution functions. Seeds of lettuce were germinated in a growth chamber at 7 constant temperatures: 10, 14, 16, 20, 24, 28, and $32^{\circ}C$. Four replicates of 100 seeds were placed on two layers of filter paper in a 9 cm petri-dish. Radicle emergence of 1 mm was scored as germination. The time course of germination was modeled using a logistic function. These minimum, optimum, and maximum temperatures were estimated by regression of the inverse of time to 50% germination rate against the temperature gradient. In bilinear function, minimum, optimum, and maximum temperatures were $7.9^{\circ}C$, $23.3^{\circ}C$, and $28.0^{\circ}C$, respectively. In parabolic function, minimum, optimum, and maximum temperatures were $9.7^{\circ}C$, $19.5^{\circ}C$, and $29.4^{\circ}C$, respectively. In beta distribution function, minimum, optimum, and maximum temperatures were $3.7^{\circ}C$, $20.7^{\circ}C$ and $32.0^{\circ}C$, respectively. Minimum, optimum, and maximum ranges of temperatures were $3.7{\sim}9.7^{\circ}C$, $19.5{\sim}23.3^{\circ}C$, and $28.0{\sim}32.0^{\circ}C$, respectively.

• Science of Emotion and Sensibility
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• v.13 no.4
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• pp.721-732
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• 2010

As increasing of electrical device usage, social concerns about the possible effects of 60Hz electromagnetic fields (EMF) on human health have increased. The number of people with self-attributed electromagnetic hypersensitivity (EHS) who complain of subjective symptoms such as headache, insomnia etc. also increased. However, we don't know whether the EHS results from psychological factor or real perception to the electromagnetic field. In this study, we simultaneously investigated physiological changes(heart rate, respiration rate, heart rate variability, alpha and beta waves in EEG), subjective symptoms and perception accuracy to assess origins of subjective symptoms according to the EMF exposure. Experiment consists of real and sham sessions and 60Hz 12.5uT magnetic field was on(real) or off(sham) to 15 EHS and 16 nonEHS. As the results, EMF exposure did not have any effects on physiological parameters or subjective symptoms for both groups. There was also no evidence that EHS group perceived the EMFs correctly than the control group. Therefore, the origins of subjective symptoms is not the 60Hz magnetic field but psychological factors.

• Journal of Korean Society of Transportation
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• v.27 no.1
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• pp.17-25
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• 2009

This research analyzed the changes of drivers' visual workload with beta wave of bionic signals from brain's occipital lobe from entrance to exit section of the Yuksimnyeong tunnel which is 3.1km long in Daejeon-Tongyeong section of Jungbu expressway. There are 10 subjects who Participate our experiments and the results are as follows. First. the drivers' visual workload is higher about 41% in entrance section of the tunnel than 200m in advance of the section of the tunnel. Second, it is higher than 200m in advance of the section of the tunnel by 34.5% that the value of the drivers' visual workload who is driving in the tunnel. Third, as a result of analyzing the tunnel into 200m unit section, it is statistically different from other sections' at the 1.0km and 2.4km section from the entrance of the tunnel. If drivers sustain average visual workload for safe driving in very long tunnel, it's not desirable environment that drivers' visual workload goes under the average workload. Therefore, it would be the section where drivers who is driving in tunnel could cause a traffic accident.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.9
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• pp.387-393
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• 2016

The aim of this study is to analyze the effectiveness of QEEG-based neurofeedback clinical tests on patients diagnosed with anxiety disorder. Researchers tested six patients with anxiety disorder using 32-channel QEEG(Quantitative electroencephalograpy) and neurofeedback training equipment. The study measured anxiety levels of patients using QEEG and BAI physiological psychology tests. Test results found hyperactive beta waves present in all six patients' temporal lobe. The intensity of the wave of the right hemisphere temporal lobe T4(M=31.07) was the higher than that of the left hemisphere temporal lobe 3(M=29.11). Following Neurofeedback training, the beta wave of the right hemisphere lobe was reduced significantly in all patients. The average anxiety level decreased from 23.57 to 12.14 after the neurofeedback training. In addition, among the two patients who were taking medication, one patient reduced his dosage while the other patient discontinued taking medication. This result implies that the QEEG neurofeedback training technique can be effectively applied to patients with anxiety disorder.