• Science of Emotion and Sensibility
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• v.14 no.2
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• pp.311-320
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• 2011

To quantitatively analyze the effects of color stimulation which is one of the major affecting factors on human emotion, we studied the relationship between color preference and the Electroencephalography (EEG) to 3 color stimuli; bright yellow red (BYR), deep green yellow (DGY), and vivid blue (VB). Physiological signal measured by EEG on the color stimulation was closely related with their well-known colorful images. The brain become more activated with decreasing the color temperature (BYR${\geq}$DGY>VB), and the right brain is more sensitive than the left. On the whole, the EEG values of the frequency bands are in order to beta ${\geq}$ theta and alpha > gamma. As decreasing the color temperature, beta wave increased (BYR${\geq}$DGY>VB), and alpha, beta and gamma waves increased with increasing the color temperature (BYR${\geq}$DGY>VB). The relationship between the color preference and EEG values showed EEG gets more activated at some frequency bands when the color preference becomes higher. In conclusion, the specific frequency band could be activating by a color stimuli which had showed higher the preference. It means that these color stimuli can apply for various industries such as beauty industry, interior design, fashion design, color therapy, and etc.

• Journal of the Institute of Convergence Signal Processing
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• v.5 no.4
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• pp.263-270
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• 2004

Recently, wavelet transform have been applied to various kinds of problems in many fields. In this paper, we propose method of Daubechies wavelet to detect several kinds of important characteristic waves in tasks EEG that are needed to diagnose EEG. We show that our system could be attained higher performance in detecting characteristic waves than the other methods. In this system, the architecture of the neural network is a three layered feed-forward networks with one hidden layer which implements the error back propagation teaming algorithm. Applying the algorithms to 4 subjects show 92% classification rates. The proposed system shows a little more accurate diagnosis for task EEG by Wavelet and neural network. From the simulation results by the implemented system, we demonstrated this research can be reduce doctor's labors and quantitative diagnosis of task EEG.

• Journal of KIISE:Software and Applications
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• v.35 no.3
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• pp.169-178
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• 2008

Cancer patients have been suffered from the instability of mind/body and unbalanced homeostasis because of cancer progression and medical treatment such as chemotherapy, It is very important that appropriated actions can be promptly taken by monitoring cancer patients' mental conditions. For this reason, it is crucial to develop a monitoring method which is convenient and not harmful to their body. Brain-computer-interface(BCI) system is introduced for the purpose in this paper. Prefrontal brain waves of cancer patients and control groups have been measured by a portable neurofeedback(NF) system based on self-regulation of the human electroencephalogram(EEG). The NF system consists of the portable EEG amplifier and a headband with dry electrodes placed on Fp1 and Fp2 sites. Patterns of the prefrontal brain waves taken by computer are correlated to brain quotients by EEG-analysis program. Basic rhythm quotient, attention quotient, emotional quotient, anti-stress quotient and correlation quotient of control group have shown high significant level compared with the cancer patients group. On the other hand, the EEG patterns analysis is shown its possibility to be an important methodology of monitoring cancer patients' condition.

• Journal of the Korean Society for Library and Information Science
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• v.56 no.1
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• pp.95-117
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• 2022

This study developed and evaluated audio-visual (AV) semantics-based video summarization methods using eye tracking and electroencephalography (EEG) data. For this study, twenty-seven university students participated in eye tracking and EEG experiments. The evaluation results showed that the average recall rate (0.73) of using both EEG and pupil diameter data for the construction of a video summary was higher than that (0.50) of using EEG data or that (0.68) of using pupil diameter data. In addition, this study reported that the reasons why the average recall (0.57) of the AV semantics-based personalized video summaries was lower than that (0.69) of the AV semantics-based generic video summaries. The differences and characteristics between the AV semantics-based video summarization methods and the text semantics-based video summarization methods were compared and analyzed.

• 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 Korean Ophthalmic Optics Society
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• v.7 no.1
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• pp.51-55
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• 2002

This study was to investigate the effect of EEG wave type of visual cortex on conjugate movement of eyeball according to movement of visual target. Visual evoked potential(VEP) system used the Bio-Pag(production in USA) and recorded to 586 computer. The illumination of test room was 50lux and the visual target was red light dot of 3cm size. The results of dextroversion and levoversion as follows : The visual stimulation waves on the visual cortex have about 71% of delta wave, about 12% of beta wave, about 9% theta wave and about 6% of alpha wave respectively. The dextroversion and levoversion state was similar results on the histogram amplitude of EEG wave, frequency of EEG wave type, EEG wave style and phase diagram of amplitude. Expecially the histogram amplitude of EEG wave appeared almost the Gaussian shape and the phase analysis of amplitude of EEG wave was nearly linear shape. On the fast fourier transform of the amplitude and Hz, the frequency was almost low frequency under 20 Hz, and the dextroversion and levoversion shape was similar results.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.2
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• pp.367-372
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• 2023

In this paper, we analyzed through the EEG signals how musical stimulus affects learning activities. Musical stimuli were divided into sedative and stimulative tendency music, preferred and non-preferred music, and the learning activity tasks were divided into mathematics tasks and memorization tasks. The signals measured in the EEG experiments were analyzed with the power spectrum of SMR waves known to be related to human concentration. Those spectra used for quantitative comparison in this paper. As a result the power of the EEG signals was observed to be greater than the case where music was given as a stimulus. Regardless of the type of task, the power of the EEG signals was observed to be greater in the case of sedative tendency than in the case of stimulative tendency, and the power of the EEG signals was observed to be greater in the case of favorite music than in the case of unfavorite music. From these results, it is estimated that if the musical stimulus exists, in the case of sedative tendency music, and in the case of favorite music, concentration can be increased than in the relative case.

• Sleep Medicine and Psychophysiology
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• v.19 no.1
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• pp.11-17
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• 2012

Objective: Among various methods developed to quantitatively explore electroencephalograms (EEG), we focused on a wavelet method that was known to yield robust results under nonstationary conditions. The aim of this study was thus to introduce the wavelet method and demonstrate its potential use in clinical sleep studies. Method: This study involved artificial EEG specifically designed to validate the wavelet method. The method was performed to obtain time-dependent spectral power and phase angles of the signal. Synchrony of multichannel EEG was analyzed by an order parameter of the instantaneous phase. The standard methods, such as Fourier transformation and coherence, were also performed and compared with the wavelet method. The method was further validated with clinical EEG and ERP samples available as pilot studies at academic sleep centers. Result: The time-frequency plot and phase synchrony level obtained by the wavelet method clearly showed dynamic changes in the EEG waveforms artificially fabricated. When applied to clinical samples, the method successfully detected changes in spectral power across the sleep onset period and identified differences between the target and background ERP. Conclusion: Our results suggest that the wavelet method could be an alternative and/or complementary tool to the conventional Fourier method in quantifying and identifying EEG and ERP biomarkers robustly, especially when the signals were nonstationary in a short time scale (1-100 seconds).

• The Journal of the Korea Contents Association
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• v.7 no.8
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• pp.41-49
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• 2007

BCI system integrates control or telecommunication system with generating electric signals in scalp itself after signal acquisition. This system detect a movement of EEG at real time, can control an electron equipment using a generated signal through EEG movement or software-based processor. In this paper, we deal with removing and separating artifacts induceced from measurement when brain-computer interface system that analyzes recognizes EEG signals occurred from various mental states. In this paper, we proposed a method of EEG classification and an artifact interval detection using bisection mathematical modeling in the EEG classification process for BCI system implementation.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.7
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• pp.717-722
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• 2016

The purpose of this study is to observe the changes of EEG signals in the process for solving the problems in concentration. In the experiments, subjects were given two tasks. The first task is to memorize the words after they used their own smart phone for ordinary commercial games and the second task is to memorize the words after they read a page of a p-book. In this paper, we present SMR waves and mid-beta waves to analyze from the EEG signals of the subjects because the waves are the EEG components related to concentration of human.