• Journal of Biomedical Engineering Research
• /
• v.29 no.4
• /
• pp.286-294
• /
• 2008

This work investigates the characteristics of brain interactions of experienced Zen-Buddhist practitioners by obtaining multichannel EEG (electroencephalogram) data. Brain interactions were compared among three phases-40-minute meditation (M), 5-minute Chakra-focusing practice (Z) and rest with closed eyes (R). The similarity index S, developed in nonlinear dynamical system theory, was employed to measure the degree of possibly asymmetric coupling. Meditators exhibited, overall, stronger interactions among multiple cortical areas in meditation stages M and Z than in the R state. This enhancement was greater in the M stage when the meditator was accompanied by a thought-free and fully consciousness state. In the high-frequency band (>13Hz), the interdependence was also higher in both meditation stages than at baseline rest. However, the interaction strength, especially in the posterior regions, was greatest in the Z stage, which involved internal attention. Few electrode pairs were observed with significant pair-wise asymmetry in the Z state. The similarity is a possible characteristic of dense reciprocal and strong mutual interactions between multiple cortical areas during meditation - especially in the Z state in the high-frequency band. These results demonstrate that profound Zen meditation induces various dynamic states in different phases of meditation, possibly reflected by nonlinear interdependence measure.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.55 no.5
• /
• pp.240-247
• /
• 2006

Frequent arousals during sleep degrade the quality of sleep and result in sleep fragmentation. Visual inspection of physiological signals to detect the arousal events is cumbersome and time-consuming work. The purpose of this study is to develop an automatic algorithm to detect the arousal events. The proposed method is based on time-frequency analysis and the support vector machine classifier using single channel electroencephalogram (EEG). To extract features, first we computed 6 indices to find out the informations of a subject's sleep states. Next powers of each of 4 frequency bands were computed using spectrogram of arousal region. And finally we computed variations of power of EEG frequency to detect arousals. The performance has been assessed using polysomnographic (PSG) recordings of twenty patients with sleep apnea, snoring and excessive daytime sleepiness (EDS). We could obtain sensitivity of 79.65%, specificity of 89.52% for the data sets. We have shown that proposed method was effective for detecting the arousal events.

• Biomolecules & Therapeutics
• /
• v.8 no.1
• /
• pp.63-72
• /
• 2000

We intended to show some basic requirements for spectrum analysis of electroencephalogram (EEG) by visualizing the differences of the results according to different values of some parameters for analysis. Spectrum analysis is the most popular technique applied for the quantitative analysis of the electroen- cephalographic signals. Each step from signal acquisition through spectrum analysis to presentation of parameters was examined with providing some different values of parameters. The steps are:(1) signal acquisition; (2) spectrum analysis; (3) parameter extractions; and (4) presentation of results. In the step of signal acquisition, filtering and amplification of signal should be considered and sampling rate for analog-to-digital conversion is two-time faster than highest frequency component of signal. For the spectrum analysis, the length of signal or epoch size transformed to a function on frequency domain by courier transform is important. Win dowing method applied for the pre-processing before the analysis should be considered for reducing leakage problem. In the step of parameter extraction, data reduction has to be considered so that statistical comparison can be used in appropriate number of parameters. Generally, the log of power of all bands is derived from the spectrum. For good visualization and quantitative evaluation of time course of the parameters are presented in chronospectrogram.

• Journal of IKEEE
• /
• v.22 no.4
• /
• pp.1109-1114
• /
• 2018

Electroencephalogram (EEG) is a tool used to study brain activity caused by external stimuli. In this process, artifacts are mixed and it is easy to distort the signal, so post-processing is necessary to remove it. Independent Component Analysis (ICA) is a widely used method for removing artifact. This method has a disadvantage in that it has excellent performance but some loss of brain wave information. In this paper, we propose a method to reduce EEG information loss by restricting the filter coverage using eye blink information obtained from Eyetracker. We then compared the results of the proposed method with the conventional method using quantization methods such as Signal to Noise Ratio (SNR) and Spectral Coherence (SC).

• Proceedings of the ESK Conference
• /
• 1997.04a
• /
• pp.3-9
• /
• 1997

EEG(electroencephalogram) is attempted to determination of human emotion. Ten university students were participated in this study. Ten auditory stimuli were presented for a subject to evoke emotion. Data homogeneity according to brain local area and basic mechanism of relative variation for combinational delta, theta, alpha and beta waves were analyzed. As the result, the local area characterized by factor analysis and the relative variation of alpha-delta wave can be considered as the determinants of human emotion.

• 제어로봇시스템학회:학술대회논문집
• /
• 2002.10a
• /
• pp.52.5-52
• /
• 2002

In this paper, we develop analysis models for classification of temporal data from human subjects. The study focuses on the analysis of electroencephalogram (EEG) signals obtained during various emotional states. We demonstrate a generally applicable method of removing EOG and EMG artifacts from EEGs based on independent component analysis (ICA). All EEG channel maps were interpolated from 10 EEG subbands. ICA methods are based on the assumptions that the signals recorded on the scalp are mixtures of signals from independent cerebral and artifactual sources, that potentials arising from different parts of the brain, scalp and body are summed linearly at the electrodes and that prop...

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.6 no.8
• /
• pp.27-34
• /
• 1983

Bioelectrical measurement has long been an important area in medical researches and practices, and contributed valueable data to the field of human engineering. In particular, the measurement of EEG has been widely used for the study of brain function as well as for the diagnosis of various brain disorders. The present study tried to improve conventional measurements of EEG in that FFT algorithm with microcomputer machine language was applied to facilitate the computation of various aspects of the EEG.

• Proceedings of the Korean Information Science Society Conference
• /
• 2003.10b
• /
• pp.739-741
• /
• 2003

Electroencephalogram (EEG)-based brain computer interface (BCI) provides a new communication channel between human brain and computer. EEG is very noisy data and contains artifacts, thus the extraction of features that are robust to noise and artifacts is important. In this paper we present a method with employ both independent component analysis (ICA) and oriented principal component analysis (OPCA) for artifact-robust feature extraction.

• IEIE Transactions on Smart Processing and Computing
• /
• v.4 no.2
• /
• pp.97-102
• /
• 2015

In this paper, an extension of the standard common spatial pattern (CSP) algorithm using the strong uncorrelated transform (SUT) is used in order to extract the features for an accurate classification of the left- and right-hand motor imagery tasks. The algorithm is designed to analyze the complex data, which can preserve the additional information of the relationship between the two electroencephalogram (EEG) data from distant channels. This is based on the fact that distant regions of the brain are spatially distributed spatially and related, as in a network. The real-world left- and right-hand motor imagery EEG data was acquired through the Physionet database and the support vector machine (SVM) was used as a classifier to test the proposed method. The results showed that extracting the features of the pair-wise channel data using the strong uncorrelated transform complex common spatial pattern (SUTCCSP) provides a higher classification rate compared to the standard CSP algorithm.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.16 no.5
• /
• pp.1-11
• /
• 2017

Expressway network which plays an important role on land transportation system, have been developing quantitatively and qualitatively with $7{\times}9$ structure. To cope with complex geometric condition, guidance lane marking has been installed to induce safer lane-changing maneuver. However, there is no standard on guidance lane marking and its effectiveness is also verified with limited scope. The major purpose of this research is to clarify its effectiveness in terms of driving safety aspect using virtual driving simulator and to suggest standard on the proper length. To carry this out, preference data from subjects was collected and lane-changing pattern within virtual driving environment was investigated. In addition, in order to quantify the level of comfort, Electroencephalogram data was collected and validated using statistical test. Finally, it is expected that this research can be used to establish standard on guidance lane marking.