• Journal of the Ergonomics Society of Korea
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• v.32 no.5
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• pp.421-427
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• 2013

Objective: This paper is to analyze the impact of musical training to the fast ${\alpha}$ wave activation of the EEG. Background: EEG is neurological research method that can observe the brain function in real time. EEG can be used to determine the nervousness and relaxedness of a person who receives stimuli in a structured environment. Therefore, it is possible to interpret the functional state of human brain by the analysis of EEG. Method: The brain activities of two groups of university students in the point of RFA(Relative Fast Alpha) caused by different music are analyzed in this paper. One is the group of music majors and the other is the group of non-majors. Results: Music major and non-major groups show meaningful differences in RFA during exposed to classic and metal music. Conclusion: Learning experience on music affects RFA increment of music majors. Application: The result of this study will be used as basic data to evaluate the learning effects of students who want to study music.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1910-1911
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• 2007

A new method for estimating and eliminating electrocardiogram (ECG) artifacts from single channel scalp electroencephalogram (EEG) is proposed. The proposed method consists of emphasis of QRS complex from EEG using least squares acceleration (LSA) filter, generation of synchronized pulse with R-peak and ECG artifacts estimation and elimination using adaptive filter. The performance of the proposed method was evaluated using simulated and real EEG recordings, we found that the ECG artifacts were successfully estimated and eliminated in comparison with the conventional multi-channel techniques, which are independent component analysis (ICA) and ensemble average (EA) method. In conclusion, we can conclude that the proposed method is useful for the detecting and eliminating the ECG artifacts from single channel EEG and simple to use for ambulatory/portable EEG monitoring system.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.24 no.65
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• pp.65-73
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• 2001

In this study, psychophysiological stress was quantitatively evaluated at various car interior noise levels by using Electroencephalogram(EEG). An experiment was performed to investigate the most comfortable range of noise level during simulated driving condition. Twelve healthy volunteers participated in the experiment. They were asked to operate the driving simulator while six levels of interior noise were given, such as 45dB(A), 50dB(A), 55dB(A), 60dB(A), 70dB(A), 80dB(A), and maximal subjective noise level. EEG signals were recorded for 60 seconds in each noise level. The power spectral analysis was performed to analyze EEG signal. At the same time, psychological stress was also measured subjectively by using a magnitude estimation method. The results showed that subjective stress and EEG spectrum indicated a statistically significant difference between noise levels. In particular, high level noise produced an increase in beta power at temporal(T3, T4) areas. It was also found that beta activity was highly correlated with subjective perception of discomfort, and subjects responded to car interior noise as arousing or negative stimuli. Moreover, beta power remained stable above 70dB(A), whereas subjective discomfort continued to increase even above 70dB(A) We concluded that brain waves could provide psychophysiological information of drivers emotional reaction to car interior noise. Thus, EEG parameters could be a new measure to determine optimal noise level in ergonomic workplace design after further verification in various experimental conditions.

• Journal of the Korean Society of Clothing and Textiles
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• v.44 no.5
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• pp.824-836
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• 2020

This study investigated the effects of textile attachments on electroencephalogram using 20 persons (10 males and 10 females). Four types of attachment cushions were manufactured by changing the shell fabric (cotton and microfiber) and interlining (synthetic loose fiber and buckwheat). This was done using BIOS-S8 (BioBrain Inc., Korea), an 8-channel polygraph for multi-body signal measurement, to measure EEG. Data were analyzed using the SPSS 24.0 statistical program. EEG values were significantly activated according to gender, particularly when the subjects' eyes were open. For the male cases, 'RT', 'RAHB' values were highly activated and for the female cases, 'RA', 'RB', 'RG', 'RFA', 'RST', 'RLB', 'RMB', 'RST', 'RMT' values were highly activated. Examining the differences in EEG according to type of attachment indicated no significant difference in both sexes. However, in cases of females with their eyes closed, the 'RSA' index was quite different in the left occipital lobe (O1), and when their eyes were open, the 'RFA' in the right frontal lobe (F4) showed a significant difference. However, there was no obvious correlation between the activation of EEG and the subjective preference of textile attachments.

• Journal of Korea Multimedia Society
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• v.24 no.8
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• pp.1149-1160
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• 2021

Lack of sleep time increases risks of fatigue, hypomnesis, decreased emotional stability, indigestion, and dementia. The risks can be reduced by providing eyelid-warming, inducing sleep and improving sleep quality. However, effective warming temperature to an person varies depending on physical condition and the individual. The various types of frequencies can be identified in brain wave from a person and amount of frequencies is also changed continuously before and after sleep. Therefore we can identify the user's sleep stage with brain wave, namely EEG. Effective sleep induction is possible if warming temperature to a person is controlled based on EEG. In this paper, we propose customized warming control techniques based on EEG for a efficient and effective sleep induction. As an experiment, sleep induction effects of standard sleep mask and customized temperature control techniques sleep mask are compared. EEG data and warming temperature were measured in 100 experiments. At customized warming control techniques, experiments showed that the ratio of alpha and theta waves increased by 3.21%p and the time to sleep decreased by 85 seconds. It will contribute to effective sleep induction and performance verification methods in customized sleep mask systems.

• Journal of IKEEE
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• v.22 no.3
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• pp.816-821
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• 2018

Recently, machine learning algorithms based on artificial neural networks started to be used widely as classifiers in the field of EEG research for emotion analysis and disease diagnosis. When a machine learning model is used to classify EEG data, if training data is composed of only data having similar characteristics, classification performance may be deteriorated when applied to data of another group. In this paper, we propose a method to construct training data set by selecting several groups of data using semi-supervised learning algorithm to improve these problems. We then compared the performance of the two models by training the model with a training data set consisting of data with similar characteristics to the training data set constructed using the proposed method.

• Annals of Clinical Neurophysiology
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• v.21 no.1
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• pp.16-29
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• 2019

Background: Sex influences is important to understand behavioral manifestations in a large number of neuropsychiatric disorders. We found electrophysiological differences specifically related to the influence of sex on psychopathic traits. Methods: The resting electroencephalography (EEG) activity and low-resolution brain electromagnetic tomography (LORETA) for the EEG spectral bands were evaluated in 38 teenagers with conduct disorder (CD). The 25 male and 13 female subjects had psychopathic traits as diagnosed using the Antisocial Process Screening Device. All of the included adolescents were assessed using the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR) criteria. The visually inspected EEG characteristics and the use of frequency-domain quantitative analysis techniques are described. Results: Quantitative EEG (QEEG) analysis showed that the slow-wave activities in the right frontal and left central regions were higher and the alpha-band powers in the left central and bitemporal regions were lower in the male than the female psychopathic traits group. The current source density showed increases in paralimbic areas at 2.73 Hz and decreases in the frontoparietal area at 9.37 Hz in male psychopathics relative to female psychopathics. Conclusions: These findings indicate that QEEG analysis and techniques of source localization can reveal sex differences in brain electrical activity between teenagers with CD and psychopathic traits that are not obvious in visual inspections.

• Science of Emotion and Sensibility
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• v.13 no.1
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• pp.121-128
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• 2010

This study is to analyze change of connectivity between brain positions caused by relaxation through EEG coherence. EEG spectrum analysis method has been used to analyze brain activity when relaxation was experienced. However, the spectrum analysis method has a limit that could not observe interactive reaction between brain-functional positions. Therefore, coherence between positions was analyzed to observe connectivity between the measurement positions in this study. Through the method, the reaction of the central nervous system caused by the emotion change was observed. Twenty-four undergraduates of both genders(12 males and 12 females) were asked to close their eyes and listen to the sound. During experiment, EEG was measured at eight positions. The eight positions were F3, F4, T3, T4, P3, P4, O1, and O2 in accordance with International 10-20 system. The sounds with white noise and without were used for relaxation experience. Subjective emotion was measured to verify whether or not they felt relaxation. Subjective emotion of participants were analyzed by ANOVA method(Analysis of Variance). In the result, it was proved that relaxation was subjectively evoked when participants heard sound. Accordingly, it was proved that relaxation could be enhanced by the mixed white noise. EEG coherence between the measurement positions was analyzed. T-test was performed to find its significant difference between relaxation and not-relaxation. In the results of EEG coherence, connectivity with occipital lobes has been increased with relaxation, and connectivity with parietal lobes has been increased with non-relaxed state. Therefore, brain connectivity has shown different pattern between relaxed emotion and non-relaxed emotion.

• Korean Journal of Biological Psychiatry
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• v.2 no.2
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• pp.257-265
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• 1995

The object of this study is to apply a chaotic signal analysis method to the EEG research, especially in the aspect of neuropsychiatry, and to get some inspection of the chaotic phenomena according to the brain sites and subjects. We have acquired 21 channel EEG data and one EOG according to the international 10-20 system and calculated the correlation dimension. The subject groups are schizophrenics, bipolar disorder, major depression and normal control. They were all awoke and eye-closed. We have found no distinctive features from our experiments except temporal regions have slightly higher correlation dimension. There is also no specific distinctions between groups. We conjecture that these results are mainly because the subjects were not well controlled. EEG dimension may change in accordance with to the age, sex, medication and the time data were selected to calculate. We have also considered some conditions for a better and more objective research of chaotic analysis to EEG research. Better conditioning and standardizing the calculation of correlation dimension is necessary for the application of the chaotic analysis to neuropsychiatry.

• Journal of the Korean Society for information Management
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• v.32 no.3
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• pp.377-396
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• 2015

This study proposed a key-frame automatic extraction method for video storyboard surrogates based on users' cognitive responses, EEG signals and discriminant analysis. Using twenty participants, we examined which ERP pattern is suitable for each step, assuming that there are five image recognition and process steps (stimuli attention, stimuli perception, memory retrieval, stimuli/memory comparison, relevance judgement). As a result, we found that each step has a suitable ERP pattern, such as N100, P200, N400, P3b, and P600. Moreover, we also found that the peak amplitude of left parietal lobe (P7) and the latency of FP2 are important variables in distinguishing among relevant, partial, and non-relevant frames. Using these variables, we conducted a discriminant analysis to classify between relevant and non-relevant frames.