• KIPS Transactions on Computer and Communication Systems
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• v.9 no.11
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• pp.265-272
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• 2020

In the research of brain computer interface (BCI) technology, one of the big problems encountered is how to deal with some people as called the BCI-illiteracy group who could not control the BCI system. To approach this problem efficiently, we investigated a kind of spectral EEG characteristics in the prior resting state in association with BCI performance in the following BCI tasks. First, spectral powers of EEG signals in the resting state with both eyes-open and eyes-closed conditions were respectively extracted. Second, a convolution neural network (CNN) based binary classifier discriminated the binary motor imagery intention in the BCI task. Both the linear correlation and binary prediction methods confirmed that the spectral EEG characteristics in the prior resting state were highly related to the BCI performance in the following BCI task. Linear regression analysis demonstrated that the relative ratio of the 13 Hz below and above the spectral power in the resting state with only eyes-open, not eyes-closed condition, were significantly correlated with the quantified metrics of the BCI performance (r=0.544). A binary classifier based on the linear regression with L1 regularization method was able to discriminate the high-performance group and low-performance group in the following BCI task by using the spectral-based EEG features in the precedent resting state (AUC=0.817). These results strongly support that the spectral EEG characteristics in the frontal regions during the resting state with eyes-open condition should be used as a good predictor of the following BCI task performance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.6
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• pp.1338-1344
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• 2010

A person does communication between each other using language. But In the case of disabled person can not communication own idea to use writing and gesture. Therefore, In this paper, we embodied communication system using the facial muscle signals so that disabled person can do communication. Especially, After feature extraction of the EEG included facial muscle, it is converted the facial muscle into control signal, and then select character and communication using a minimum list keyboard.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.05a
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• pp.113-116
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• 2003

A person does communication between each other using language. But, In the case of disabled person, call not communicate own idea to use writing and gesture. We embodied communication system using the ERG so that disabled Person can do communication. After feature extraction of the EEG included facial muscle, it is converted the facial muscle into control signal. and then did so that can select character and communicate idea.

• Proceedings of the KOSOMBE Conference
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• v.1991 no.11
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• pp.34-36
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• 1991

본 논문에서는 저주파의 envelope와 고주파의 neural oscillation 신호의 곱으로 이루어진 EEG 신호의 envelope를 추출을 하기 위하여 multiplicative homomorphic 시스템을 사용하였다. 이 방법은 다른 방법에 비하여 처리 과정이 간단하여 계산량이 감소되어 실시간 envelope 추출의 가능성을 보였으며, 또 neural oscillation signal의 주파수가 변하여도 정확한 envelope 추출할 수 있는 우수한 적응력을 보였다.

• Journal of Korean Society of Transportation
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• v.29 no.5
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• pp.67-78
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• 2011

This paper analyzes the characteristics of drivers' workload observed from with 30 participant drivers with respect to two physio-physiological parameters. For investigating physio-physiological characteristics of road drivers, bio-signals from brain's occipital lobe between simulation experiment and real driving experiment are collected and analyzed. The major findings from the analysis are summarized as follows: First, the drivers' physio-physiological workload is a good parameter for explaining the workload characteristics of road drivers. Secondly, the two physio-physiological workload parameters selected, i.e., beta value and relative energy parameter, are revealed to be statistically significant. Thirdly, it is also revealed to be statistically significant to select 90 percentile measurements in simulator experiment to explain the road drivers' characteristics. Finally, the maximum workload of road design driver is 31.72 in beta parameter, whereas the minimum workload is 1.296 in relative energy parameter.

• The KIPS Transactions:PartB
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• v.16B no.1
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• pp.63-70
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• 2009

BCI technology is to control communication systems or machines by brain signal among biological signals followed by signal processing. For the implementation of BCI systems, it is required that the characteristics of brain signal are learned and analyzed in real-time and the learned characteristics are applied. In this paper, we detect feature vector of EEG signal on left and right hand movements based on incremental approach and dimension reduction using the detected feature vector. In addition, we show that the reduced dimension can improve the classification performance by removing unnecessary features. The processed data including sufficient features of input data can reduce the time of processing and boost performance of classification by removing unwanted features. Our experiments using K-NN classifier show the proposed approach 5% outperforms the PCA based dimension reduction.

• Science of Emotion and Sensibility
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• v.12 no.2
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• pp.205-214
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• 2009

This paper addresses analyzing affective color data for emotional interior design. Both the physical and psychological patterns for spatial colors were tested on thirty subjects, of which fifteen were male. All subjects participated in both the physiological and psychological experiments. The data on the reflecting subjects' affective moods is gathered through EEG physical experiments and SD (Semantic Differential Scale) method surveys. This research has suggested the relation of both experiments through affective color response. The methods of SPSS 10.0 and TeleScan Version 2 are used for analyzing response data to coordinate the colour palette with changeable moods. From the analysis of statistical data, all of the visual stimuli related emotional keywords and physiological responses. Finally, the initial goal of this research is to construct an affective colour database that is tested through human color perception by physical and psychological experiments.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.9
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• pp.917-922
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• 2016

There are many researches related on the brain wave signals to monitor the state of human health. Especially, some patients in the medical hospital need to be monitored in case of emergencies such as a seizure, an epilepsy and so on. To support QoS of the brain wave network in the hospital is a vital issue and the Opnet simulator is used for this experiment. So the efficient network topology is required for the stability of the brain wave network service. The brain waves of the patients are collected from the sensor devices in the network. Two different sensor network topologies are suggested and simulated for the comparison of the network performance. One topology is localized and the other is non-localized network. The simulation is operated with the Opnet simulator.

• Journal of Biomedical Engineering Research
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• v.24 no.6 s.81
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• pp.487-494
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• 2003

The sleep stages provides the most basic evidence for diagnosing a variety of sleep diseases. for staging sleep by analysis of EEG(electroencephalogram), it is especially important to detect the characteristic waveforms from EEG. In this paper, sleep EEG signals were analyzed using Fourier transform and continuous wavelet transform as well as discrete wavelet transform. Proposeed system methods. Fourier and wavelet for detecting of important characteristic waves(hump, sleep spindles. K-complex, hill wave, ripple wave) in sleep EEG. Sleep EEG data were analysed using Daubechies wavelet transform method and FFT method. As a result of simulation, we suggest that our neural network system attain high performance in classification of characteristic waves.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2009.05a
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• pp.59-62
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• 2009

본 연구에서는 통행방향의 교차 상황이 발생하였을 때와 그렇지 않았을 때의 뇌파를 정량적으로 측정하여 보행자가 인지심리적 불편함을 느끼는지 여부를 알아보았다. 총 50명의 실험참가자에게 통행방향의 교차가 발생하는 영상 4개와 그렇지 않은 영상 4개를 무작위고 시청하게 하고 좌 우 전두엽, 좌 우 후두엽, 좌 우 측두엽, 중심엽 부위에서 나타나는 뇌파 신호를 추출하였다. 추출된 뇌파는 주파수 분석을 하여 alpha파 상대 스펙트럼 값, beta파 상대 스펙트럼 값, theta파 상대 스펙트럼 값을 계산하고 동선의 교차 여부에 대한 정량적 비교를 실시하였다. 실험 결과, 통행방향이 일관된 영상을 시청하였을 때 모든 부위에서 alpha파가 높게 나오는 경향을 보였다. 본 연구 결과는 통행방향이 좌/우로 교차할 때 교차하지 않는 상황에 비해 보행자가 인지심리적 더 스트레스를 느끼는 것으로 나타났다.