• The Journal of Korean Physical Therapy
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• v.32 no.6
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• pp.365-371
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• 2020

Objective: To investigate the action observation effects of functional electrical stimulation (FES) on the communication between motor cortex and muscle through corticomuscular coherence (CMC) analysis. Methods: Electroencephalogram (EEG) and electromyogram (EMG) of 27 healthy, nonathlete subjects were measured during action observation, FES, and action observation with FES, which lasted for 7sper session for 10 times. All trials were repeated for 30 times. Simultaneously measured EEG raw data and rectified EMG signals were used to calculate CMC. Only confidence limit values above 0.0306 were used for analysis. CMC was divided into three frequency domains, andthe grand average coherence and peak coherence were computed. Repeated ANOVA was performed to analyze the coherence value difference for each condition's frequency band. Results: CMC showed significant differences in peak coherence and average coherence between the conditions (p<0.05). Action observation application with FES in all frequency band showed the highest peak and average coherence value. Conclusions: The results of this study are assumed to be the combination of increased eccentric information transfer from the sensorymotor cortex by action observation and an increased in concentric sensory input from the peripheral by the FES, suggesting that these are reflecting the sensorimotor integration process.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.19-22
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• 2022

In this work, we studied consumers' attractiveness/usefulness perceptions (CAUP) of online commerce product photos when exposed to alternative dark/light user interface (UI) modes. We analyzed time-series EEG data from 31 individuals and performed neuroscience mining (NSM) to ascertain (a) how the CAUP of products differs among UI modes; and (b) which deep learning model provides the most accurate assessment of such neuroscience mining (NSM) business difficulties. The dark UI style increased the CAUP of the products displayed and was predicted with the greatest accuracy using a unique EEG power spectra separated wave brainwave 2D-ConvLSTM model. Then, using relative importance analysis, we used this model to determine the most relevant power spectra. Our findings are considered to contribute to the discovery of objective truths about online customers' reactions to various user interface modes used by various online marketplaces that cannot be uncovered through more traditional research approaches like as surveys.

• 전자공학회논문지 IE
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• v.43 no.1
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• pp.16-24
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• 2006

This paper is aimed to develope of an integrated BCI(Brain Computer Interface System) that make possible for simultaneous multichannel data process and used extra cellular neural activity from the vestibular system instead of electroencephalogram signals for more precision control. The electrical properties pre-amplifier are 47.6 dB of gain, 0.005 % of distortion at 100 Hz, 12M$\Omega$ of input impedance. Window discriminator used two CPU with difference role to increase processing speed so that sampling frequency was 87 kHz. The designed window discriminator has more not only two times in signal resolution power but also ten times in error discrimination power than commericially available discriminator. The proposed method decreases 100 times in amount of integrated data then BCI system during 100 ms.

• Journal of Power System Engineering
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• v.14 no.4
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• pp.29-36
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• 2010

This study was performed with 5 subjects and used three kinds of music and vibroacoustic stimuli wave based upon each kinds of music. Executing music stimulation, vibro tactile and acoustic wave stimulation to human body were performed. Then measured brain waves were analyzed under each condition including before stimulation, stimulation 1, and stimulation 2. Effects by stimulation results could be studied with experiments and summarized results are followings. 1. It may be concluded that effects on brain waves by music and vibroacoustic stimulation might differ under different situations such as stimulation types with vibroacoustic equipment, human body and mental conditions when measuring, etc.. 2. During stimuli by using music A, B, and C, the effect of $\alpha$ wave, $\beta$ wave, and SMR wave power values show same tendency to the subject c but music C had very different tendency during vibroacoustic stimuli. 3. During vibroacoustic stimuli by applying the signals of music C, because SMR wave power value was continually increased with consistency comparing to Bst, this can be estimated that an application of inducing mind concentration condition would be possible under relaxed body and mind conditions. 4. To secure data significance, all measured data need to be tested statistically whether data would be interrelated or not.

• Korean Journal of Biological Psychiatry
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• v.22 no.2
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• pp.87-94
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• 2015

Objectives The power spectral analysis of electroencephalogram has been widely used to reveal the pathophysiology of the alcoholic brain. However, the results were not consistent and the three dimensional study can be hardly found. The purpose of this study was to investigate characteristics of the three dimensional electroencephalographic (EEG) activity of alcohol dependent patients using standardized low resolution electromagnetic tomography (sLORETA). Methods The participants consisted of 30 alcohol dependent patients and 30 normal healthy controls. All the participants were males who had refrained from alcohol at least one month and were not taking any medications. Thirty two channel EEG data was collected in the resting state with eyes-closed condition during 30 seconds. The three dimensional data was compared between two groups using sLORETA for delta, theta, alpha, beta1, beta2, and beta3 frequency bands. Results sLORETA revealed significantly increased brain cortical activity in alpha, beta1, beta2, and beta3 bands each in alcohol dependent patients compared to normal controls. The voxels showing the maximum significance were in the left transverse temporal gyrus, left superior temporal gyrus, left anterior cingulate, and left fusiform gyrus in alpha, beta1, beta2, and beta3 bands respectively. Conclusions These results suggest that chronic alcohol intake may cause neurophysiological changes in cerebral activity. Therefore, the measuring of EEG can be helpful in understanding the pathophysiology of cognitive impairements in alcohol dependence.

• Korean Journal of Adult Nursing
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• v.25 no.6
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• pp.644-654
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• 2013

Purpose: The purpose of this study was to examine the effects of aroma foot reflexology massage on mood states specifically depression and brain waves of elderly women with osteoarthritis. Methods: The study was a nonequivalent control group non-synchronized design. The participants were 62 elderly women with osteoarthritis. The instruments were the Korean-Profile of Mood States-Brief for mood states and 8-channel EEG (Electroencephalogram) system for brain waves. Data were collected from March to May, 2012. Twenty-six participants were assigned to the treatment group and twenty-six to the comparison group. The data were analyzed using SPSS/WIN 17.0 version program, and included descriptive statistics, t-test, and ANCOVA. The intervention was conducted three times a week for two weeks. Results: There were significantly improvement in reported depression. s. Brain waves (EEG) increased significantly in F3, T3 of ${\alpha}$ wave and in F4, T3, and P4 of ${\beta}$ wave between the two groups. Conclusion: Aroma foot reflexology massage can be utilized as an effective intervention to decrease depression of mood states, increase of ${\alpha}$, and ${\beta}$ brain wave on woman elderly with osteoarthritis.

• Journal of Biomedical Engineering Research
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• v.36 no.4
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• pp.95-102
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• 2015

Epilepsy is a chronic neurological disease which produces repeated seizures. Over 30% of epileptic patients cannot be treated with anti-epileptic drugs, and surgical resection may cause loss of brain functions. Seizure suppression by electrical stimulation is currently being investigated as a new treatment method as clinical evidence has shown that electrical stimulation to brain could suppress seizure activity. In this paper, design of a real-time closed-loop neurostimulation system for epileptic seizure suppression is presented. The system records neural signals, detects seizures and delivers electrical stimulation. The system consists of a 6-channel electrode, front-end amplifiers, a data acquisition board by National Instruments, and a neurostimulator and Generic Osorio-Frei algorithm was applied for seizure detection. The algorithm was verified through simulation using electroencephalogram data, and the operation of whole system was verified through simulation and in- vivo test.

• Journal of Korean Society of Forest Science
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• v.98 no.1
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• pp.88-93
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• 2009

The overall purpose of this study was to figure out psycho-physiological variations in human bodies according to observing visual images of forests. To collect data, the authors employed 9 views each in three different environments such as cities, forests, and landscape which combines a forest with water. The experiment was conducted by showing total 27 images to 30 visitors to measure the subjects' changes of alpha waves of EEG(electroencephalogram). As measures of psychological impact of the views, PRS(Perceived Restorativeness Scale) and PANAS(Positive and Negative Affect Schedule) were used. The results of the data analyses indicated that the views of landscape with a forest and water influenced most highly on subjects' psycho-physiological responses.

• Journal of Digital Convergence
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• v.18 no.12
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• pp.279-284
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• 2020

In January 2020, COVID19 plunged the whole planet into a pandemic. This has caused great economic losses and is causing social confusion. COVID19 has a superior infection rate among people with underlying disease such as heart disease, high blood pressure, diabetes, stroke, depression, and cancer. In addition, it was studied that patients with underlying disease had a higher fatality rate than those without underlying disease. In this study, the presence or absence of underlying disease was classified using EEG data. The data used to classify the presence or absence of underlying disease was EEG data provided by Data Science lab, consisting of 33 features and 69 samples. Z-score was used for data pretreatment. Classification was performed using the neural network NEWFM and ZNN engine. As a result of the classification of the presence or absence of the underlying disease, the experimental results were 77.945 for NEWFM and 76.4% for ZNN. Through this study, it is expected that EEG data can be measured, the presence or absence of an underlying disease is classified, and those with a high infection rate can be prevented from COVID19. Based on this, there is a need for research that can subdivide underlying disease in the future and research on the effects of each underlying disease on infectious disease.

• Journal of Internet Computing and Services
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• v.20 no.5
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• pp.27-36
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• 2019

In this study, experiments on the improvement of the emotion classification, analysis and accuracy of EEG data were proceeded, which applied DEAP (a Database for Emotion Analysis using Physiological signals) dataset. In the experiment, total 32 of EEG channel data measured from 32 of subjects were applied. In pre-processing step, 256Hz sampling tasks of the EEG data were conducted, each wave range of the frequency (Hz); Theta, Slow-alpha, Alpha, Beta and Gamma were then extracted by using Finite Impulse Response Filter. After the extracted data were classified through Time-frequency transform, the data were purified through Independent Component Analysis to delete artifacts. The purified data were converted into CSV file format in order to conduct experiments of Machine learning algorithm and Arousal-Valence plane was used in the criteria of the emotion classification. The emotions were categorized into three-sections; 'Positive', 'Negative' and 'Neutral' meaning the tranquil (neutral) emotional condition. Data of 'Neutral' condition were classified by using Cz(Central zero) channel configured as Reference channel. To enhance the accuracy ratio, the experiment was performed by applying the attributes selected by ASC(Attribute Selected Classifier). In "Arousal" sector, the accuracy of this study's experiments was higher at "32.48%" than Koelstra's results. And the result of ASC showed higher accuracy at "8.13%" compare to the Liu's results in "Valence". In the experiment of Random Forest Classifier adapting ASC to improve accuracy, the higher accuracy rate at "2.68%" was confirmed than Total mean as the criterion compare to the existing researches.