• The Journal of the Korea institute of electronic communication sciences
• /
• v.9 no.11
• /
• pp.1277-1282
• /
• 2014

In this paper, we aimed to analysis EEG signals related to concentration of adolescents using letter visual stimulus to induce the concentration. The visual stimulus tasks were searching errors of propositional particle in several sentences. In the EEG signals, we specially focussed on SMR waves and mid-beta waves according to the results of a preceding research. Therefore we presented position of channel and frequency band of mid-beta significantly related to the concentration waves as the experimental results.

• Proceedings of the IEEK Conference
• /
• 2009.05a
• /
• pp.145-147
• /
• 2009

This research measures EEG signals which are generating on head skin and extracts brain concentration level related with brain activity. We have developed concentration wireless transmission system by displaying this EEG signal on PDA mobile device. The front head was used for measuring EEG signal and INA128 with TL084 and analog elements was used for measuring EEG signal, amplifying and filtering the signal. Measured analog EEG signals changed into digital signals by using ADC of PIC24FJ192 with 10bit resolution and 500Ks/s sampling rate. So The changed digital signals have transmitted to the PDA by using bluetooth. LabView 8.5 was also used for FFT transformation, frequency and spectrum analysis of the transferred EEG signal. As a result, $\alpha$ wave, $\beta$ wave, $\theta$ wave and $\delta$ wave were classified. we extracted the concentration index by adapting concentration extraction algorithm. This concentration index was transferred into PDA by wireless module and displaying.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.46 no.2
• /
• pp.41-46
• /
• 2009

This research measures EEG signals which are generating on head skin and extracts brain concentration level related with brain activity. We develop concentration wireless transmission system for controlling hardware by using this signal. Two channels are used for measuring EEG signal on front head and Biopac system with MP100 and EEG100C was used for measuring EEG signal, amplifying and filtering the signal. LabView 8.5 was also used for FFT transformation, frequency and spectrum analysis of the measured EEG signals. As a result, SMR wave, Mid-Bata wave, $\Theta$ wave classified. We extracted the concentration index by adapting concentration extraction algorithm. This concentration uldex was transferred into logo automobile device by wireless module and applied for BCI application.

• Korean Institute of Interior Design Journal
• /
• v.25 no.3
• /
• pp.60-69
• /
• 2016

This experimental study measures the emotional and physiological responses of customers as to cafe facade design. It is done through eye-tracking and EEG response experiments. Specifically, their visual concentration and EEG concentration are analyzed in line with their facade preferences. The findings are as follows. First, the correlation between their facade preferences and visual concentration on facades is as follows: Highly preferable facades have a lower visual concentration frequency than the less preferable facades. Second, an analysis of $12{\times}12$ lattice division of facades shows that all facades have a high visual concentration for signs. The exceptions are F(6), F(7), F(8), and F(10). There is no correlation between the facade preferences and visual concentration behaviors for particular facade elements. Third, an analysis of prefrontal lobe's facade concentration shows that there is no correlation between the preferences and EEG concentration. However, there are big differences in the prefrontal lobe activity of 12 subjects depending on the facade. In particular, nine of them (3, 9, 13, 14, 15, 28, 36, 38, 43) show an activated prefrontal lobe as to the highly preferable facades-F(1), F(2), F(3), and F(4). However, such activation is not detected on the less preferable facades-F(9), F(10), F(11), and F(12).

• Proceedings of the KIEE Conference
• /
• 2008.10b
• /
• pp.155-156
• /
• 2008

This research measures EEG signals which are generating on head skin and extracts brain concentration level related with brain activity. We develop concentration wireless transmission system for controlling hardware by using this signal. Two channels are used for measuring EEG signal on front head and Biopac system with MP-100 and EEG100C was used for measuring EEG signal, amplifying and filtering the signal. LabView 8.5 was also used for FFT transformation, frequency and spectrum analysis of the measure EEG signal. As a result, ${\alpha}$ wave, ${\beta}$ wave, ${\theta}$ wave and ${\delta}$ wave were classified. we extracted the concentration index by adapting concentration extraction algorithm. This concentration index was transferred into lego automobile device by wireless module and applied for BCI application.

• Journal of the Korea Society of Computer and Information
• /
• v.23 no.11
• /
• pp.129-135
• /
• 2018

In this study, we designed a model that can measure the level of user's concentration by measuring and analyzing EEG data of the subjects who are performing Continuous Performance Test based on visual stimulus. This study focused on alpha and beta waves, which are closely related to concentration in various brain waves. There are a lot of research and services to enhance not only concentration but also brain activity. However, there are formidable barriers to ordinary people for using routinely because of high cost and complex procedures. Therefore, this study designed the model using the portable EEG measurement device with reasonable cost and Visual Continuous Performance Test which we developed as a simplified version of the existing CPT. This study aims to measure the concentration level of the subject objectively through simple and affordable way, EEG analysis. Concentration is also closely related to various brain diseases such as dementia, depression, and ADHD. Therefore, we believe that our proposed model can be useful not only for improving concentration but also brain disease prediction and monitoring research. In addition, the combination of this model and the Brain Computer Interface technology can create greater synergy in various fields.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.9 no.5
• /
• pp.589-594
• /
• 2014

It has been known that the particular brain waves are induced when a human concentrates. In our study, we aimed to analysis the brain waves related to human concentration using visual stimulus to induce the concentration. The visual stimulus tasks were presented to subjects for concentration. We measured EEG signals with several channels and analyzed the signals into several frequency bands. In the measured EEG signals, we analyzed to focus on theta waves, SMR waves and mid-beta waves. Therefore we presented the results to investigate characteristics of the EEG signals related to the human concentration.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.9 no.12
• /
• pp.1421-1426
• /
• 2014

Many people complain their big or little stress due to the complicated city life in modern times, so they are exposed of the mental illness. Especially, not only students and office workers but also most people suffer from degradation of efficiency at work and keeping the high quality of life because of the insufficiency of concentration ability. To improve the concentration ability, the meditation is a substitution. The influence of meditation for the concentration ability is experimented with EEG brainwave. Some experienced meditators are participated for the experiments, and the left and right portion of prefrontal lobe, AF3 and AF4, are measured and analyzed. As a result, the changes of rhythmic activity of a unique pattern and power spectra are observed.

• Journal of Institute of Control, Robotics and Systems
• /
• v.19 no.8
• /
• pp.725-730
• /
• 2013

This paper proposed a novel method for an analysis feature of an Electroencephalogram (EEG) at all channels simultaneously. In a BCI (Brain-Computer Interface) system, EEGs are used to control a machine or computer. The EEG signals were weak to noise and had low spatial resolution because they were acquired by a non-invasive method involving, attaching electrodes along with scalp. This made it difficult to analyze the whole channel of EEG signals. And the previous method could not analyze multiple stimuli, the result being that the BCI system could not react to multiple orders. The method proposed in this paper made it possible analyze multiple-stimuli by grouping the channels. We searched the groups making the largest correlation coefficient summation of every member of the group with a BHS (Binary Harmony Search) algorithm. Then we assumed the EEG signal could be written in linear summation of groups using concentration parameters. In order to verify this assumption, we performed a simulation of three subjects, 60 times per person. From the simulation, we could obtain the groups of EEG signals. We also established the types of stimulus from the concentration coefficient. Consequently, we concluded that the signal could be divided into several groups. Furthermore, we could analyze the EEG in a new way with concentration coefficients from the EEG channel grouping.

• Journal of Sensor Science and Technology
• /
• v.13 no.3
• /
• pp.236-243
• /
• 2004

In this paper, we proposed a brain-computer interface system using EEG signals. It can generate 4 direction command signal from EEG signals captured during imagination of subjects. Bandpass filter used for preprocessing to detect the brain signal, and the power spectrum at a specific frequency domain of the EEG signals for concentration status and non-concentration one is used for feature. In order to generate an adequate signal for controlling the 4 direction movement, we propose a new interface system implemented by using a support vector machine and a time-multiplexing method. Moreover, bio-feed back process and on-line adaptive pattern recognition mechanism are also considered in the proposed system. Computer experimental results show that the proposed method is effective to recognize the non-stational brain wave signal.