• Journal of Biomedical Engineering Research
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• v.17 no.2
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• pp.179-188
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• 1996

A clinically oriented 32-channel electroencephalogram (EEG) and evoked potential (EP) mapping system has been developed EEG and EP signals acquired from 32-channel electrodes attached on the heroid surface are amplified by a pre-amplifier which is separated from main amplifier and is located near the patient to reduce signal attenuation and noise contamination between electrodes and the amplifier. The amplified signals are further amplified by a main amplifier where various filtering and gain contr61 are achieved An automatic artifact rejection scheme is employed using neural network-based EEG and artifact classifier, by which examination time is substantially reduce4 The continuously measured EEG sigrlals are used for spectral mapping, and auditory and visual evoked potentials measured in synchronous to the auditory and visual stimuli are used for temporal evoked potential mapping. A user-friendly graphical interface based on the Microsoft Window 3.1 is developed for the operation of the system. Statistical databases for comparisons of group and individual are included to support a statistically-based diagnosis.

• Fisheries and Aquatic Sciences
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• v.17 no.1
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• pp.19-25
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• 2014

We investigated the effects of Ecklonia cava ethanol extract (ECE) on sleep architecture and sleep profiles. ECE was orally administered at a dose of 100, 250, or 500 mg/kg to C57BL/6N mice and its effects were measured by recording electroencephalogram (EEG) and electromyogram. Administration of ECE (250 and 500 mg/kg) significantly induced non-rapid eye movement sleep (NREMS) without affecting rapid eye movement sleep. The increase in NREMS by ECE (500 mg/kg) was significant (P < 0.05) during the first 2 h after administration. In addition, ECE had no effect on EEG power density (an indicator of sleep quality) in NREMS. These results suggest that ECE induces NREMS in a manner similar to physiological sleep.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.12
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• pp.702-707
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• 2003

This study describes a new technique for human sensibility evaluation using electroencephalogram(EEG). Production of EEG is assumed to be linear. The linear predictor coefficients and the linear cepstral coefficients of EEG are used as the feature parameters of sensibility and pattern classification performances of them are compared. Using the better parameter, a human sensibility evaluation algorithm is designed. The obtained results are as follows. The linear predictor coefficients showed the better performance in pattern classification than the linear cepstral coefficients. Then, using the linear predictor coefficients as the feature parameter, a human sensibility evaluation algorithm is developed at the base of a multi-layer neural network. This algorithm showed 90% of accuracy in comfortableness evaluation in spite of fluctuations in statistics of EEG signal.

• Journal of Magnetics
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• v.16 no.3
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• pp.259-262
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• 2011

Compared to acupuncture, the pulsed magnetic field (PMF) stimulus is a useful tool for treatment of many physical conditions and health maintenance due to its advantages as a noninvasive and nontoxic medical treatment. The purpose of this study was to investigate the effect of PMF stimulus direction at PC9 on the alpha activity of electroencephalogram (EEG) and vascular aging calculated from photoplethysmograph (PPG). It can be concluded that the direction of PMF stimulus affects the increase of alpha activity of EEG and PPG, indicating the vascular stiffness and the sclerosis level of blood vessels weakly relevant to the direction of PMF stimulus.

• Journal of the Korean Institute of Educational Facilities
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• v.21 no.6
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• pp.33-42
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• 2014

The objective of this study is to investigate learning factors (stability, attention and activation) in school by electroencephalogram (theta, alpha and beta waves) analysis during relaxed or concentrated. In order to measure electroencephalograms, MP 150 by Biopac and ECI Electro-Cap are employed. Three types of color temperatures (3000K, 5000K, 7000K) are used and 13 undergraduate and 12 graduate students are selected as experimental subjects. When subjects are relaxed during contemplation or concentrated during mental arithmetic, we compare with stability, attention and activation indices. The test results show that subjects were stable when color temperature is 5000K. Subjects gave best attention when color temperature is 7000K. Subjects activated well when color temperature is 3000K during relaxed attention. However, subjects activated rigorous when color temperature is 7000K during constrained attention.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.179-182
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• 2015

Detecting and assessing human-related risks is critical to improve the on-site safety condition and reduce the loss in lives, time and budget for construction industry. Recent research in neural science and psychology suggest inattentional blindness that caused by overload in working memory is the major cause of unexpected human related accidents. Due to the limitation of human mental workload, laborers are vulnerable to unexpected hazards while focusing on complicated and dangerous construction tasks. Therefore, detecting the risk perception abilities of workers could help to identify vulnerable individuals and reduce unexpected injuries. However, there are no available measurement approaches or devices capable of monitoring construction workers' mental conditions. The research proposed in this paper aims to develop such a measurement framework to evaluate hazards through monitoring electroencephalogram of labors. The research team developed a wearable safety monitoring helmet, which can collect the brain waves of users for analysis. A bispectrum approach has been developed in this paper to enrich the data source and improve accuracy.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2004.11a
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• pp.26-26
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• 2004

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2011.11a
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• pp.65-67
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• 2011

• Journal of the Korean Society for Precision Engineering
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• v.18 no.2
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• pp.14-25
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• 2001

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.10
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• pp.887-895
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• 2014

Drowsy driving is a large proportion of the total car accidents. For this reason, drowsiness detection and warning system for drivers has recently become a very important issue. Monitoring physiological signals provides the possibility of detecting features of drowsiness and fatigue of drivers. Many researches have been published that to measure electroencephalogram(EEG) signals is the effective way in order to be aware of fatigue and drowsiness of drivers. The aim of this study is to extract drowsiness-related features from a set of EEG signals and to classify the features into three states: alertness, transition, and drowsiness. This paper proposes a drowsiness detection system using errors-in-variables(EIV) for extraction of feature vectors and multilayer perceptron (MLP) for classification. The proposed method evaluates robustness for noise and compares to the previous one using linear predictive coding (LPC) combined with MLP. From evaluation results, we conclude that the proposed scheme outperforms the previous one in the low signal-to-noise ratio regime.