• Journal of the Korean Society of Safety
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• v.25 no.3
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• pp.123-130
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• 2010

Electroencephalogram(EEG) would be the most objective psychophysiological research technique on human errors though few research has been taken yet. This study aimed to get characteristics of human error while committing simple Odd-Ball tasks by utilizing the power spectrum technique of EEG data. Each experiment was composed of 3 tasks with different rules, and 8 young undergraduate students participated in this study as paid subjects. The result showed in the affirmative that subject and the interaction of subject and task factors were statistically significant on variation of $\alpha$ band power $P_{\alpha/(\alpha+\beta+\theta)}$ and $\beta$ band power $P_{\beta/(\alpha+\beta+\theta)}$, and that the former increasing in backward direction to Pz reflects compatibility whereas the latter increasing in forward direction to Fz reflects familiarity. Therefore it was coucluded that, since task 2 carried out in the present research requiring decoding process would be more difficult to human beings than the task merely requiring psychological recall process, task 1 and task 3 were classified into a homogenious group excluding task 2, and the ratio $\alpha$ band power to $\beta$ band power indicated enormous increase of $\alpha$ band power relative to $\beta$ band power in the cases of contra-lateral errors, especially in task 2.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.23 no.57
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• pp.51-62
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• 2000

In this study, the psycho-physiological response of drivers was investigated in terms of EEG(Electroencephalogram), especially with the fractal dimensions computed by Chaotic algorithm. The Chaotic algorithm Is well Known to sensitively analyze the non-linear information such as brain waves. An automobile with a fully equipped data acquisition system was used to collect the data. Ten healthy subjects participated in the experiment. EEG data were collected while subjects were driving the car between Won-ju and Shin-gal J.C. on Young-Dong highway The results were presented in terms of 3-Dimensional attractor to confirm the chaotic nature of the EEG data. The correlation dimension and fractal dimension were calculated to evaluate the complexity of the brain activity as the driving duration changes. In particular, the fractal dimension indicated a difference between the driving condition and non-driving condition while other spectral variables showed inconsistent results. Based upon the fractal dimension, drivers processed the most information at the beginning of the highway driving and the amount of brain activity gradually decreased and stabilized. No particular decrease of brain activity was observed even after 100 km driving. Considering the sensitivity and consistency of the analysis by Chaotic algorithm, the fractal dimension can be a useful parameter to evaluate the psycho-physiological responses of human brain at various driving conditions.

• ETRI Journal
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• v.35 no.6
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• pp.1105-1114
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• 2013

Owing to the large number of video programs available, a method for accessing preferred videos efficiently through personalized video summaries and clips is needed. The automatic recognition of user states when viewing a video is essential for extracting meaningful video segments. Although there have been many studies on emotion recognition using various user responses, electroencephalogram (EEG)-based research on preference recognition of videos is at its very early stages. This paper proposes classification models based on linear and nonlinear classifiers using EEG features of band power (BP) values and asymmetry scores for four preference classes. As a result, the quadratic-discriminant-analysis-based model using BP features achieves a classification accuracy of 97.39% (${\pm}0.73%$), and the models based on the other nonlinear classifiers using the BP features achieve an accuracy of over 96%, which is superior to that of previous work only for binary preference classification. The result proves that the proposed approach is sufficient for employment in personalized video segmentation with high accuracy and classification power.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06b
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• pp.414-416
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• 2012

고차 주성분 방법으로는 텐서 분석이 있었다. Electroencephalography(EEG) 데이터나 Social Network 데이터에 텐서 분석이 적용되어 주요한 성분들을 찾는 연구들이 있었다. 그러나 텐서 분석은 직관적으로 이해하기에 어려움이 있으며 중요한 노드를 찾는데에는 다소 어려움이 있다. 본 논문에서는 고차 하이퍼에지로 이차원 행렬을 만들고 주성분분석법을 이용하여 중요한 노드를 찾는 새로운 방법론을 제시한다. 데이터로는 Multimodal Memory Game(MMG) 수행시 촬영한 EEG 데이터를 사용하였다. MMG는 TV 드라마 기반의 기억인출게임이다. 베타파의 Power Spectrum Density(PSD)는 각 위치의 채널들의 활성도를 나타내는 지표이다. 우리는 Random Sampling을 바탕으로 PSD 상위 50%의 채널들간의 전이행렬을 구하였다. 그 후 고유치와 고유벡터를 구하였다. 가장 큰 고유치의 고유벡터는 주성분을 나타내며 고유벡터의 각 원소들은 중요도를 나타내는 centrality 이다. 세 명의 피험자에 대한 centrality 상위 30개의 중요한 채널들을 구하였고 세명에 공통적으로 포함되는 채널을 확인하였다.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.4
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• pp.773-778
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• 2020

In this paper, we analyzed the effects of musical stimuli on humans in performing mathematical tasks through EEG measurements. The musical stimuli were divided into preferred music and non-preferred music, and mathematical tasks were divided into memorization task and procedure task. The data measured in the EEG experiments was divided into frequency bands of Theta, SMR, and Mid-beta because of the concentration. In our results, preferred music causes more positive emotional response than no music and non-preferred music regardless of the type of mathematical task.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.04a
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• pp.992-994
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• 2014

Hemispheric asymmetry in prefrontal activation have been proposed in two decades ago, as measured by electroencephalographic (EEG) power in the theta band (4-8Hz), is related to reactivity to affectively pleasure audio stimuli. In this study, we designed an emotional audio stimulus experiment in order to verify frontal EEG asymmetry by analyzing ERSP results. Thirty healthy college students volunteered the stimulus experiment with the standard IADS affective sounds. These affective sound clips are classified in three emotion states, happy, neutral and fear. ERSP image results revealed that there are the stronger responses of high arousal (fear and happy) in the left prefrontal lobe, while the stronger responses of low arousal (neutral) in the right pre-frontal lobe. However, the high pleasure emotions (happy) can elicit greater relative right EEG activity, while the low and middle pleasure emotions (fear and neutral) can elicit the greater relative left EEG activity. Additionally, the most response differences of theta band have been found out in the medial frontal lobe, which is proved as the frontal midline theta.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.3
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• pp.607-612
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• 2019

In this paper, we measured and analyzed the EEG signals related to the relativity between musical stimuli and human concentration for memorization. In our experiments, the subjects carried out the tasks related to human memorization exposing to musical stimuli and the tasks are to memorize the english words. We used two kinds of musical stimuli, one is a sedative tendency music and the other is a stimulative tendency music. We presented the results that are analyzed as the EEG signals by frequency bands, respectively.

• ETRI Journal
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• v.44 no.4
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• pp.672-685
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• 2022

This article proposes a subject-independent application of brain-computer interfacing (BCI). A 32-channel Electroencephalography (EEG) device is used to measure imagined speech (SI) of four words (sos, stop, medicine, washroom) and one phrase (come-here) across 13 subjects. A deep long short-term memory (LSTM) network has been adopted to recognize the above signals in seven EEG frequency bands individually in nine major regions of the brain. The results show a maximum accuracy of 73.56% and a network prediction time (NPT) of 0.14 s which are superior to other state-of-the-art techniques in the literature. Our analysis reveals that the alpha band can recognize SI better than other EEG frequencies. To reinforce our findings, the above work has been compared by models based on the gated recurrent unit (GRU), convolutional neural network (CNN), and six conventional classifiers. The results show that the LSTM model has 46.86% more average accuracy in the alpha band and 74.54% less average NPT than CNN. The maximum accuracy of GRU was 8.34% less than the LSTM network. Deep networks performed better than traditional classifiers.

• 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.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.4
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• pp.478-485
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• 1999

The purpose of this paper was the determination of the relationship between the chaotic charateristics and various levels of brain activities. Assuming that EEG(eletroencephalogram), which is generated by a nonlinear electiecal behavior of billions of neurons in the brain, has chaotic characteristics, it was confirmed by frequency spectrum analysis, log frequency spectrum analysis, correlation dimension analysis and Lyapunov exponents analysis. Chaotic characteristics are related to the degree of brain activity. The slope of log frequency spectrum increased and the correlation dimension decreased with respect to the brain activities, while the lagrest Lyapunov exponent has some rough correlation.