• Proceedings of the KOSOMBE Conference
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• v.1994 no.05
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• pp.49-52
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• 1994

We have developed a biological signal measurement, archiving, and communication system (SiMACS). The front end of the system is the intelligent data processing unit (IDPU) which includes ECG, EEG, EMG, blood pressure, respiration, temperature measurement modules, module control and data acquisition unit, real-time display and signal processing unit. IDPUS are connected to central data base unit through LAN(Ethernet). Workstations which receive signals from central DB and provide various signal analysis tools are also connected to the network. The developed PC-based SiMACS is described.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.10
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• pp.207-216
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• 2013

The aim of this study is to design a classifier for sleep stage analysis and select important feature set which shows sleep stage well based on physiological signals during sleep. Sleep has a significant effect on the quality of human life. When people undergo lack of sleep or sleep-related disease, they are likely to reduced concentration and cognitive impairment affects, etc. Therefore, there are a lot of research to analyze sleep stage. In this study, after acquisition physiological signals during sleep, we do pre-processing such as filtering for extracting features. The features are used input for the new combination algorithm using genetic algorithm(GA) and neural networks(NN). The algorithm selects features which have high weights to classify sleep stage. As the result of this study, accuracy of the algorithm is up to 90.26% with electroencephalography(EEG) signal and electrocardiography(ECG) signal, and selecting features are alpha and delta frequency band power of EEG signal and standard deviation of all normal RR intervals(SDNN) of ECG signal. We checked the selected features are well shown that they have important information to classify sleep stage as doing repeating the algorithm. This research could use for not only diagnose disease related to sleep but also make a guideline of sleep stage analysis.

• Journal of IKEEE
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• v.25 no.4
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• pp.679-688
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• 2021

Gait is an evaluation index used in various clinical area including brain nervous system diseases. Signal source localizing and time-frequency analysis are mainly used after extracting independent components for Electroencephalogram data as a method of measuring and analyzing brain activation related to gait. Existing treadmill-based walking EEG analysis performs signal preprocessing, independent component analysis(ICA), and source localizing by merging data after the multiple EEG measurements, and extracts representative component clusters through inter-subject clustering. In this study we propose an analysis method, without merging to single dataset, that performs signal preprocessing, ICA, and source localization on each measurements, and inter-subject clustering is conducted for ICs extracted from all subjects. The effect of data merging on the IC clustering and time-frequency analysis was investigated for the proposed method and two conventional methods. As a result, it was confirmed that a more subdivided gait-related brain signal component was derived from the proposed "non-merging" method (4 clusters) despite the small number of subjects, than conventional method (2 clusters).

• Journal of Biomedical Engineering Research
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• v.28 no.2
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• pp.287-293
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• 2007

The researchers have studied for a long time about the depth of anesthesia but they don't make criteria for the depth of anesthesia. Anesthetists can't make a prediction about patient's reaction. Therefore, patients have potential risk such as poisonous side effect, late-awake, early-awake and strain reaction. In this study, the distributed characteristics on the bispectrum and bicoherence, the type of nonlinear signal processing, as a result of the coupling of EEG were presented according to depth of anesthesia. These results were consistent with a trend of delta ratio that the index of evaluation for the depth of anesthesia. The higher-order spectrum (HOS), the bispectrum and bicoherence, gives the useful information about depth of anaesthesia than other indexes.

• Proceedings of the Korean Information Science Society Conference
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• 2003.04c
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• pp.193-195
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• 2003

The analysis of EEG data is an important task in the domain of Brain Computer Interface (BCI). In general, this task is extremely difficult because EEG data is very noisy and contains many artifacts and consists of mixtures of several brain waves. The P300 component of the evoked potential is a relatively evident signal which has a large positive wave that occurs around 300 msec after a task-relevant stimulus. Thus automatic detection of P300 is useful in BCI. To this end, in this paper we employ a method of reference-based independent component analysis (ICA) which overcomes the ordering ambiguity in the conventional ICA. We show here. that ICA incorporating with prior knowledge is useful in the task of automatic P300 detection.

• Journal of Korea Multimedia Society
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• v.14 no.9
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• pp.1202-1209
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• 2011

Neuro feedback training using ElectroEncephalo Grams (EEGs) is commonly utilized in the treatment of Alzheimer's disease, and Attention Deficit Hyperactivity Disorder (ADHD). Recently, BCI (Brain-computer Interface) contents have developed, not for the purpose of treatment, but for concentration improvement or brain relaxation training. However, as each user has different wave forms, it is hard to develop contents controlled by such different wave. Therefore, an EEG process that allows the ability to transform the variety of wave forms into one standard signal and use it without taking a user's characteristic of EEG into account, is required. In this paper, a framework that can reduce users' characteristics by normalizing and converting measured EEGs is proposed for contents. This framework also contains the process that controls different brainwave measuring devices. In experiment a handling process applying the proposed framework to the developed BCI contents is introduced.

• 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.1998 no.11
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• pp.211-212
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• 1998

In this experiment, we developed a mind switching device which uses $\alpha$-wave with its amplitude varying between the eye-open and closed states. If the subject closes eyes, the switch toggles with a small delay. The circuit consists of an amplifier, a filter and a switch. An instrument amplifier is used fur high CMRR and for high input impedance. An 8th order Butterworth filter was able to reduce noise satisfactorily. The signal is then converted to a DC level, and finally a Schmitt trigger was used to generate a switching pulse.

• Journal of Information Processing Systems
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• v.16 no.5
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• pp.1034-1047
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• 2020

The personal authentication technique is an essential tool in this complex and modern digital information society. Traditionally, the most general mechanism of personal authentication was using alphanumeric passwords. However, passwords that are hard to guess or to break, are often hard to remember. There are demands for a technology capable of replacing the text-based password system. Graphical passwords can be an alternative, but it is vulnerable to shoulder-surfing attacks. This paper looks through a number of recently developed graphical password systems and introduces a personal authentication system using a machine learning technique with electroencephalography (EEG) signals as a new type of personal authentication system which is easier for a person to use and more difficult for others to steal than other preexisting authentication systems.

• Proceedings of the KIEE Conference
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• 1993.07a
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• pp.281-283
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• 1993

A new method of EEG spindle waveform detection i s presented. The method combines the signal conditioning in the time-domin and the analysis of local spectrum in the frequency-domain. Fast computation methods, utilizing some effective approximations, are also suggested for the desist and implementation of the filter as well as for the computation of the local spectrum. The presented approach is especially useful for the real-time implementation of the waveform detection system under a general purpose microcomputer environment. The overall detection system is implemented and tested on-line with the total 24 hour data of selected four subjects. The result show the average agreement of 86.7% with the visually inspected result.