• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.155-156
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• 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 Korean Institute of Intelligent Systems
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• v.19 no.1
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• pp.122-127
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• 2009

In this paper, we measured EEG signals on frontal and Broca's area when subjects imagine to speak A or B or C or D. These signals were analyzed by Event-Related Spectral Perturbation (ERSP), Inter-Trial Coherence (ITC) and Event Related Potential (ERP) methods. As a result, high coherences were showed at 1$\sim$13Hz during 0$\sim$300ms after the stimuli of each character and P300 was seen clearly and there are several differences between the ERP results. However, unlike the motivation of this study to classify the characters, it is impossible that we can classify each intention or each character cause these differences. Nevertheless, this paper suggest an application system using this results so BCI can provide various services.

• Science of Emotion and Sensibility
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• v.6 no.4
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• pp.15-23
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• 2003

Recently, there have been many attempts to develop BCI (brain computer interface) based on EEG (electroencephalogram). Measurement and analysis of EEG evoked by particular stimulation is important for the design of brain wave pattern and interface of BCI. The purpose of this study is to develop a general-purpose system that measures subject's reaction time after audio-visual stimulation which can work together with any other biosignal measurement systems. The entire system is divided into four modules, which are stimulation signal generation, reaction time measurement, evoked potential measurement and synchronization. Stimulation signal generation module was implemented by means of Flash. Measurement of the reaction time (the period between the answer request and the subject reaction) was achieved by self-made microcontroller system. EEG measurement was performed using the ready-made hardware and software without any modification. Synchronization of all modules was achieved by, first, the black-and-white signals on the stimulation screen synchronized with the problem presentation and the answer request, second, the photodetectors sensing the signals. The proposed method offers easy design of purpose-specific system only by adding simple modules (reaction time measurement, synchronization) to the ready-made stimulation and EEG system, and therefore, it is expected to accelerate the researches requiring the measurement of evoked response and reaction time.

• Journal of Korea Game Society
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• v.10 no.6
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• pp.115-124
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• 2010

In the beginning brain waves were used for monkeys to control robot arm with neural activity. In recent years there are research that measured brain waves are used for the control of programs which monitor the progression of dementia or enhance of attention in children diagnosed with Attention Deficit Hyperactivity Disorder (ADHD). Moreover, low-price devices that can be used as a game control interface have become available. One of the problems associated with control using brain waves is that the mean amplitude, mean wavelength, and mean vibrational frequency of the brain waves differ from individual to individual. This paper attempts to propose a method to normalize measured brain waves using normal distribution and calculate the waveforms that can be used in controlling games. For this, a framework in which brain waves are converted in seven stages has been suggested. In addition, the estimation process in each stage has been described. In an experiment the waveforms of two subjects have been compared using the proposed method in the BCI English word learning program. The level of similarity between two subjects' waveforms has been compared with correlation coefficient. When the proposed method was applied, both meditation and concentration increased by 13% and 8%, respectively. Because the proposed regularization method is converted into a waveform fit for control functions by reducing personal characteristics reflected in the brain waves, it is fitting for application programs such as games.

• 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.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.24 no.4
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• pp.436-447
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• 2013

In this paper, Integrated circuit(IC) electromagnetic(EM) conducted immunity measurement and simulation using bulk current injection(BCI) and direct power injection(DPI) methods were conducted for 1.8 V I/O buffers. Using the equivalent circuit models developed for IC electromagnetic conducted immunity tests, we investigated the reliability of the frequency region where IC electromagnetic conducted immunity test is performed. The insertion loss for the noise injection path obtained from the simulation indicates that using only one conducted immunity test method cannot provide reliable conducted immunity test for broadband noise. Based on the forward power results, we analyzed the actual amount of EM noise injected to IC. We propose a more reliable immunity test methods for broad band noise.

• Journal of KIISE
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• v.44 no.6
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• pp.587-594
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• 2017

Recently, motor imagery electroencephalogram(EEG) based Brain-Computer Interface(BCI) systems have received a significant amount of attention in various fields, including medicine and engineering. The Common Spatial Pattern(CSP) algorithm is the most commonly-used method to extract the features from motor imagery EEG. However, the CSP algorithm has limited applicability in Small-Sample Setting(SSS) situations because these situations rely on a covariance matrix. In addition, large differences in performance depend on the frequency bands that are being used. To address these problems, 4-40Hz band EEG signals are divided using nine filter-banks and Regularized CSP(R-CSP) is applied to individual frequency bands. Then, the Mutual Information-Based Individual Feature(MIBIF) algorithm is applied to the features of R-CSP for selecting discriminative features. Thereafter, selected features are used as inputs of the classifier Least Square Support Vector Machine(LS-SVM). The proposed method yielded a classification accuracy of 87.5%, 100%, 63.78%, 82.14%, and 86.11% in five subjects("aa", "al", "av", "aw", and "ay", respectively) for BCI competition III dataset IVa by using 18 channels in the vicinity of the motor area of the cerebral cortex. The proposed method improved the mean classification accuracy by 16.21%, 10.77% and 3.32% compared to the CSP, R-CSP and FBCSP, respectively The proposed method shows a particularly excellent performance in the SSS situation.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.5
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• pp.197-206
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• 2014

Motor imagery based Brain-computer Interface(BCI), which has recently attracted attention, is the technique for decoding the user's voluntary motor intention using Electroencephalography(EEG). For classifying the motor imagery, event-related desynchronization(ERD), which is the phenomenon of EEG voltage drop at sensorimotor area in ${\mu}$-band(8-13Hz), has been generally used but this method are not free from the performance degradation of the BCI system because EEG has low spatial resolution and shows different ERD-appearing band according to users. Common spatial pattern(CSP) was proposed to solve the low spatial resolution problem but it has a disadvantage of being very sensitive to frequency-band selection. Discriminative filter bank common spatial pattern(DFBCSP) tried to solve the frequency-band selection problem by using the Fisher ratio of the averaged EEG signal power and establishing discriminative filter bank(DFB) which only includes the feature frequency-band. However, we found that DFB might not include the proper filters showing the spatial pattern of ERD. To solve this problem, we apply a band-selection process using CSP feature vectors and linear discriminant analysis to DFBCSP instead of the averaged EEG signal power. The filter selection results and the classification accuracies of the existing and the proposed methods show that the CSP feature is more effective than signal power feature.

• Journal of Korean Society of Forest Science
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• v.86 no.3
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• pp.342-351
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• 1997

The objective of this study was to compare acid-neutralizing and buffering capacity of four tree species against soil acidification by acid rain. About 30-year-old forests composed of Pinus rigida, Alnus hirsuta, Quercus mongolica, and Liriodendron tulipifera in a provincial experimental forest located 15km east from Banwol Industrial Complex in Kyonggido were used in this study. Incident precipitation, throughfall and stemflow, and soil samples were collected from May to September, 1996 to analyze their pH and canon concentrations. Internal leaf pH, external acid neutralizing capacity(ENC), and buffering capacity index(BCI) of leaves were also determined. The incident precipitation showed an average pH of 4.56, with the percentage of acid rain incidents being 74%. The average soil pH of the study area was 4.15. The pH of throughfall and stemflow in all four species was higher than that of precipitation except that of the stemflow of Pinus rigida which showed a pH of 3.73. The throughfall of Liriodendron tulipifera showed the highest pH of 5.38. The pH of throughfall and stemflow showed a positive correlation and no correlation, respectively, with precipitations. The most abundant cation in precipitation was Ca. The canon concentraions in throughfall and stemflow decreased in the following order of K, Na, Ca, and Mg. Cation concentrations in stemflow were highest in Lirioendron tulipifera and lowest in Pines rigida. Nutrient leaching from above ground increased with decreasing pH of precipitation. The pH of stemflow showed a positive correlation with ENC and BCI. The highest values in ENC, BCI, soil pH, and soil cation concentrations were observed in Liriodendron tulipifera, while the lowest values were obtained in Pinus rigida, It was concluded that Liriodendron tulipifera had highest neutralizing capacity against acid rain, while Pinus rigida had the lowest capacity and even promoted acidification of soil.

• Proceedings of the Korea Contents Association Conference
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• 2011.05a
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• pp.571-572
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• 2011

지금까지 뇌와 관련된 언어, 감정, 시각, 촉각 등 의학적 영역에서 뇌에 관한 연구들이 활발히 이루어져 왔다. 그러나 이러한 의학적, 심리학적 성과와 IT와의 융합 및 응용은 아직 초보적인 단계이다. 따라서 의학적으로 입증된 뇌 영역에서 발생하는 뇌파에 대해 보다 세밀한 분석 및 연구를 통해 실질적인 응용 가능한 BCI를 구현해보고자 한다.