• Journal of Communication Design
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• v.68
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• pp.278-288
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• 2019

The purpose of this study is to analyze the effects of risk and convenience on purchase intention in the IOT market, and I want to analyze the moderating effect of emotional consumption value. In this study, two products were selected from three product groups. There are three major methods of research. First, theoretical considerations. Second, survey analysis. Reliability analysis and factor analysis were performed using descriptive statistics using SPSS. Third, we measured changes of EEG according to in - depth interview and indirect experience. As a result of the hypothesis of this study, it was confirmed that convenience of use of IoT product influences purchase intention. Risk was predicted to have a negative effect on purchase intentions, but not significant in this study. This implies that IoT products tend to be neglected in terms of monetary loss such as cost of purchase, cost of use, and disposal cost when purchasing. In-depth interviews and EEG analysis revealed that there is a desire to purchase and try out the IoT product due to the nature of the product, the novelty of new technology, and the vague idea that it will benefit my life. The aesthetic, symbolic, and pleasure factors, which are sub - elements of emotional consumption value, were found to have a great influence. This is consistent with previous research showing that emotional consumption value has a positive effect on purchase intention. In-depth interviews and EEG analyzes also yielded the same results. This study has revealed that emotional consumption value affects the intention to purchase IoT products. It seems that companies producing IoT products need to concentrate on marketing with more emotional consumption value.

• Science of Emotion and Sensibility
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• v.14 no.2
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• pp.269-278
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• 2011

This paper illustrates the inter-relationship between the theta/alpha ratio of the EEG signal and multiple HRV related parameters associated with the cardiovascular system response during event-related stimuli. Both EEG and PPG signals were simultaneously recorded in 21 healthy subjects. All subjects had their attention focused on the CNT program for nine minutes. Time-frequency analysis was applied to the EEG and PPG signals. The theta/alpha ratio was extracted from the EEG results, and the HRV features, including beat interval(1), SDNN(2), RMSSD(3), NN50(4), LF(5), HF(6), and LFIHF(7), were extracted from the PPG. Through multiple linear regression, the relationship ($R^2$) between the multiple combined features and the theta/alpha rhythm was identified. As a result, the combinations of $R^2$($R^2=0.253$; seven dimensions) and the theta/alpha ratio indicated a higher inter-relationship value than those of other combinations. The combinations of features that were greater than three dimensions, based on {SDNN(2), HF(6)}, generally showed higher $R^2$ value. We demonstrate that the high dimensional combinations had a higher correlation than did the low dimensional combinations.

• Journal of KIISE
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• v.44 no.6
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• pp.587-594
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• 2017

Recently, motor imagery electroencephalogram(EEG) based Brain-Computer Interface(BCI) systems have received a significant amount of attention in various fields, including medicine and engineering. The Common Spatial Pattern(CSP) algorithm is the most commonly-used method to extract the features from motor imagery EEG. However, the CSP algorithm has limited applicability in Small-Sample Setting(SSS) situations because these situations rely on a covariance matrix. In addition, large differences in performance depend on the frequency bands that are being used. To address these problems, 4-40Hz band EEG signals are divided using nine filter-banks and Regularized CSP(R-CSP) is applied to individual frequency bands. Then, the Mutual Information-Based Individual Feature(MIBIF) algorithm is applied to the features of R-CSP for selecting discriminative features. Thereafter, selected features are used as inputs of the classifier Least Square Support Vector Machine(LS-SVM). The proposed method yielded a classification accuracy of 87.5%, 100%, 63.78%, 82.14%, and 86.11% in five subjects("aa", "al", "av", "aw", and "ay", respectively) for BCI competition III dataset IVa by using 18 channels in the vicinity of the motor area of the cerebral cortex. The proposed method improved the mean classification accuracy by 16.21%, 10.77% and 3.32% compared to the CSP, R-CSP and FBCSP, respectively The proposed method shows a particularly excellent performance in the SSS situation.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.5
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• pp.197-206
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• 2014

Motor imagery based Brain-computer Interface(BCI), which has recently attracted attention, is the technique for decoding the user's voluntary motor intention using Electroencephalography(EEG). For classifying the motor imagery, event-related desynchronization(ERD), which is the phenomenon of EEG voltage drop at sensorimotor area in ${\mu}$-band(8-13Hz), has been generally used but this method are not free from the performance degradation of the BCI system because EEG has low spatial resolution and shows different ERD-appearing band according to users. Common spatial pattern(CSP) was proposed to solve the low spatial resolution problem but it has a disadvantage of being very sensitive to frequency-band selection. Discriminative filter bank common spatial pattern(DFBCSP) tried to solve the frequency-band selection problem by using the Fisher ratio of the averaged EEG signal power and establishing discriminative filter bank(DFB) which only includes the feature frequency-band. However, we found that DFB might not include the proper filters showing the spatial pattern of ERD. To solve this problem, we apply a band-selection process using CSP feature vectors and linear discriminant analysis to DFBCSP instead of the averaged EEG signal power. The filter selection results and the classification accuracies of the existing and the proposed methods show that the CSP feature is more effective than signal power feature.

• The Korean Journal of Nuclear Medicine
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• v.29 no.1
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• pp.22-30
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• 1995

To predict preoperatively the safety of permanent occlusion of an internal carotid artery with $^{99m}Tc$-HMPAO brain single photon emission computed tomography(SPECT) from an objective point of view, Twenty-four patients underwent balloon test occlusion (BTO) of the internal carotid arteries because of neck and skull base tumors. The authors assessed the uptake of both middle cerebral artery territories before and during BTO with $^{99m}Tc$-HMPAO brain SPECT using semiquantitative analysis method and compared the results with other factors(neurologic examination, arterial stump pressure and electroenceph-alogram). Nineteen patients had not experienced neurological deteriorating or any problem during BTO. Their comparative uptakes of the middle cerebral artery territories were 95 to 101% of the pre-BTO state. The remaining five patients showed severe neurologic symptoms such as transient hemiplegia and unconsciousness. Their comparative uptake of the middle cerebral artery territories were 77 to 85% of the pre-BTO state, and were well matched with other factors. $^{99m}Tc$-HMPAO brain SPECT before and during BTO seems to be a simple and objective method for prediction of permanent neurologic deficits when the comparative uptake of middle cerebral artery territories during BTO is lower than 85% of that before BTO.

• The Journal of the Korea Contents Association
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• v.12 no.7
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• pp.261-272
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• 2012

This study was conducted so as to examine which change tDCS (Transcranial Direct Current Stimulation) for improving working memory can make on the EEC of stroke patients. Among the patients who suffered for more than 6 months by hemiparalysis caused by stroke, 20 patients selected by MMSE and DST were randomly divided into I group (10 patients) fulfilled by only CCT and II group (10 patients) fulfilled by both tDCS and CCT for total 4 weeks, 30 minutes per a day, three times per a week. For examining EEC variation, the absolute spectrum power was calculated by three bands (${\theta}$; 4~8 Hz, lower ${\alpha}$; 8~10.5 Hz, upper ${\alpha}$;10.5~13 Hz) during the task of words, photos and mental calculation with EEC test, before the arbitration, after 2 weeks and after 4 weeks, so the rate of increase and decrease (%) for the reference EEC was obtained. As the results, the first, particular aspects different one another in three bands were detected according to the measuring period and task. The second, in the forth week, there was only a significant difference in lower ${\alpha}$-power of all tasks. Therefore, through the procedure measuring EEC of this study, the degree of working memory's damage can be expressed by numerical value and tDCS should be additionally helpful for brain damaged patients' perception rehabilitation.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.7 no.1
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• pp.51-58
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• 2013

As it is changing into aging society, rehabilitation, welfare and sports industry markets are being expanded fast. Especially, the field of vital signals interface to control welfare instruments like wheelchair, rehabilitation ones like an artificial arm and leg and general electronic ones is a new technology field in the future. Also, this technology can help not only the handicapped, the old and the weak and the rehabilitation patients but also the general public in various application field. The commercial bio-signal measurement instruments and interface systems are complicated, expensive and large-scaled. So, there are a lot of limitations for using in real life with ease. this thesis proposes a wireless transmission interface system that uses EMG(electromyogram) signals and a control module to manipulate hardware systems with portable size. We have designed a hardware module that receives the EMG signals occurring at the time of wrist movement and eliminated noises with filter and amplified the signals effectively. DSP(Digital Signal Processor) chip of TMS320F2808 which was supplied from TI company was used for converting into digital signals from measured EMG signals and digital filtering. We also have used PCA(Principal Component Analysis) technique and classified into four motions which have right, left, up and down direction. This data was transmitted by wireless module in order to display at PC monitor. As a result, the developed system obtains recognition success ratio above 85% for four different motions. If the recognition ratio will be increased with more experiments. this implemented system using EMG wrist direction signals could be used to control various hardware systems.

• Journal of IKEEE
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• v.23 no.2
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• pp.727-734
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• 2019

This paper confirms whether the movement or specific operation of the muscles in the process of transferring a person from the brain can find a signal showing an essential feature of a certain part of the brain. As a rule, the occurrence of EEG(Electroencephalogram) changes when a signal is received from a specific action or from an induced action. These signals are very vague and difficult to distinguish from the naked eye. Therefore, it is necessary to define a signal for analysis before classification. The EEG form can be divided into the alpha, beta, delta, theta and gamma regions in the frequency ranges. The specific size of these signals does not reflect the exact behavior or intention, since the band or energy difference of the activated frequencies varies depending on the EEG measurement domain. However, if different actions are performed in a specific method, it is possible to classify the movement based on EEG activity and to determine the EEG tendency affecting the movement. Therefore, in this article, we first study the EEG expression pattern based on the activation of the left and right biceps EMG, and then we determine whether there is a significant difference between the EEG due to the activation of the left and right muscles through EEG. If we can find the EEG classification criteria in accordance with the EMG activation, it can help to understand the form of the transmitted signal in the process of transmitting signals from the brain to each muscle. In addition, we can use a lot of unknown EEG information through more complex types of brain signal generation in the future.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.9
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• pp.127-132
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• 2019

This study examined the change in the attention of University students after being given Transcranial Direct Current Stimulation (tDCS). The participants were divided randomly into two group (tDCS vs. Control). tDCS was applied to 37 university students ($23.08{\pm}3.33years$). The tDCS group was applied 2 mA, for 13 minutes twice over a 26 minute period ($n_1=19$). The control ($n_2=18$) was not applied after padding and was applied twice for 13 minutes over a 26 minute period. This study was conducted from September 3 to 28, 2018 and three times a week for a total of four weeks. The electroencephalogram was confirmed to affect attention. tDCS showed significant improvement in the results in the sensory motor rhythm wave (p<0.01, 95% CI: -1.955, -0.459), middle beta wave (p<0.05; 95% CI: 0.027, 0.943), and power ratio (p<0.01, 95% CI: -1.764, -0.315). The results showed that tDCS application increased the attention ability significantly. These results can be applied to attention deficit disorder (ADHD) patients and college students.

• Science of Emotion and Sensibility
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• v.6 no.4
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• pp.9-14
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• 2003

Sleep stages have been useful indicator to check a person's comfortableness in a sleep, But the traditional method of scoring sleep stages with polysomnography based on the integrated analysis of the electroencephalogram(EEG), electrooculogram(EOG), electrocardiogram(ECG), and electromyogram(EMG) is too restrictive to take a comfortable sleep for the participants, While the sympathetic nervous system is predominant during a wakefulness, the parasympathetic nervous system is more active during a sleep, Cardiovascular function is controlled by this autonomic nervous system, So, we have interpreted the heart rate variability(HRV) among sleep stages to find a simple method of classifying sleep stages, Six healthy male college students participated, and 12 night sleeps were recorded in this research, Sleep stages based on the "Standard scoring system for sleep stage" were automatically classified with polysomnograph by measuring EEG, EOG, ECG, and EMG(chin and leg) for the six participants during sleeping, To extract only the ECG signals from the polysomnograph and to interpret the HRV, a Sleep Data Acquisition/Analysis System was devised in this research, The power spectrum of HRV was divided into three ranges; low frequency(LF), medium frequency(MF), and high frequency(HF), It showed that, the LF/HF ratio of the Stage W(Wakefulness) was 325% higher than that of the Stage 2(p＜.05), 628% higher than that of the Stage 3(p＜.001), and 800% higher than that of the Stage 4(p＜.001), Moreover, this ratio of the Stage 4 was 427% lower than that of the Stage REM (rapid eye movement) (p＜.05) and 418% lower than that of the Stage l(p＜.05), respectively, It was observed that the LF/HF ratio decreased monotonously as the sleep stage changes from the Stage W, Stage REM, Stage 1, Stage 2, Stage 3, to Stage 4, While the difference of the MF/(LF+HF) ratio among sleep Stages was not significant, it was higher in the Stage REM and Stage 3 than that of in the other sleep stages in view of descriptive statistic analysis for the sample group.