• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.2
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• pp.82-89
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• 2006

The electroencephalogram generated by light stimulation in human body of dark adaption state was measured and analyzed in the cases that electromagnetic wave was put in and not put in, respectively. Shieldroom being able to interrupt the light and the electromagnetic wave of outer was constructed, and the experimental system being able to apply any light and any electromagnetic wave was designed. When the electromagnetic wave was applied to body or not, the variation characteristics of each component in the electroencephalogram were i3s follows. The 6 wave was increased and the $\alpha$ wave and the $\beta$ wave were decreased in the case that the electromagnetic wave was applied, and the variation range of the $\Theta$ wave was small. And the influence of electromagnetic wave on human body was that the appearance time of the $\beta$ wave was late, and it moaned that the time of visual recognition was delayed.

• Journal of Biomedical Engineering Research
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• v.27 no.4
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• pp.170-179
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• 2006

The purpose of this study was to investigate the spatiotemporal cortical activation pattern and functional connectivity during visual perception of words. 61 channel recordings of electroencephalogram were obtained from 15 subjects while they were judging the meaning of Korean, English, and Chinese words with concrete meanings. We examined event-related potentials (ERP) and applied independent component analysis (ICA) to find and separate simultaneously activated neural sources. Spectral analysis was also performed to investigate the gamma-band activity (GBA, 30-50 Hz) which is known to reflect feature binding. Five significant ERP components were identified and left hemispheric dominance was observed for most sites. Meaningful differences of amplitudes and latencies among languages were observed. It seemed that familiarity with each language and orthographic characteristics affected the characteristics of ERP components. ICA helped confirm several prominent sources corresponding to some ERP components. The results of spectral and time-frequency analyses showed distinct GBAs at prefrontal, frontal, and temporal sites. The GBAs at prefrontal and temporal sites were significantly correlated with the LPC amplitude and response time. The differences in spatiotemporal patterns of GBA among languages were not prominent compared to the inter-individual differences. The gamma-band coherence revealed short-range connectivity within frontal region and long-range connectivity between frontal, posterior, and temporal sites.

• Journal of the Ergonomics Society of Korea
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• v.27 no.2
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• pp.9-14
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• 2008

Coherence can be used to evaluate the functional cortical connections between the motor cortex and muscle. This study is to find coherence between EEG (electroencephalogram) and EMG (electromyogram) evoked by movement of a hand. Seven healthy participants were asked to perform thirty repetitive movement of right hand for ten seconds with rest for ten seconds. Specific feature of EEG components has been extracted by ICA (independent component analysis) and coherence between EEG and EMG was analyzed from data measured EEG in five local areas around central part of head and EMG in flexer carpri radialis muscle during grabbing movement. Coherence between EEG and EMG was successfully obtained at 0.025 confidence limit during hand movement and showed significant difference between rest and movement at 13-18Hz.

• Journal of Biomedical Engineering Research
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• v.18 no.4
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• pp.389-396
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• 1997

Several types of electrical artifact seen on electroencephalogram( EEG) records are described. Those are the EOG and the PVC roller pump noise, and so on. An adaptive digital filtering of the electroencephalogram( EEG) is a successful way of suppressing mains interference, but it affects some of the frequency components of the signal, whore artifacts may not be acceptable in some cafes of automatic EEG processing. Thus we studied the method for cancelling these artifacts. This proposed method does not use the reference channel, and is realized by connecting the linear predictor and the fixed FIR filter for the EOG artifact, and by cascading the linear predictor and the noise canceller for the pump artifact. The simulation results illustrate the performances of the proposed method in terms of the capability of interferences suppression. In the results we obtained about 20 dB noise reduction.

• Korean Journal of Oriental Medicine
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• v.16 no.1
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• pp.157-171
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• 2010

This study aimed to investigate the effect of four common types of Qigong position (standing, sitting, supine, and horse-riding position) on the autonomic nervous system. Thirty healthy subjects participated in this study once a week for four weeks. Electroencephalogram (EEG) was measured three times (before, during, and after the position) while the subject maintained one of four positions for ten minutes. There were significant changes in HRV components compared with EEG power spectra in the standing position. Especially, the ratio of low-to-high frequency (LF/HF) which represents a state of balance of autonomic nervous system was increased. In the sitting position, $\beta$ wave which reflects a state of alert consciousness was increased and both the sympathetic and parasympathetic nerves were activated. On the other hand, in the spine position, $\theta$ wave which signifies a state of relaxation was increased and heart rate (HR) was decreased. Activation of sympathetic and parasympathetic nerves was also observed in this position. Significant increases of indices related to awakening and concentration were observed accompanied by increase of HR and a sympathetic nerve was activated in the riding-horse position. In the present study, it was shown that each Qigong position caused various and significant changes in autonomic nervous system. It would be expected that these results can be applied in the choice of appropriate Qigong position according to objective of Qigong therapy although it is remained to further evaluate the effects of long-term maintenance of Qigong positions and repeated Qigong training.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2217-2220
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• 2003

It is known that an EEG is characterized by the unique and personal characteristics of an individual. Little research has been done to take into account these personal characteristics when analyzing EEG signals. Often the EEG has frequency components which can describe most of the significant characteristics. These combinations are often unique like individual human beings and yet they have an underlying basic characteristics as well. We think that these combinations are the personal characteristics frequency components of the EEG. In this seminar, the EEG analysis method by using the Genetic Algorithms (GA), Factor Analysis (FA), and the Neural Networks (NN) is proposed. The GA is used for selecting the personal characteristic frequency components. The FA is used for extracting the characteristics data of the EEG. The NN is used for estimating the characteristics data of the EEG. Finally, in order to show the effectiveness of the proposed method, classifying the EEG pattern is carried out via computer simulations. The EEG pattern is evaluated under 4 conditions: listening to Rock music, Schmaltzy Japanese ballad music, Healing music, and Classical music. The results, when personal characteristics frequency components are NOT used, gave over 80 % accuracy versus a 95 % accuracy when personal characteristics frequency components are used. This result of our experiment shows the effectiveness of the proposed method.

• Proceedings of the IEEK Conference
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• 2000.06e
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• pp.127-130
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• 2000

Classification of Electroencephalogram(EEG) makes one of key roles in the field of clinical diagnosis, such as detection for epilepsy. Spectrum analysis using the fourier transform(FT) uses the same window to signals, so classification rate decreases for nonstationary signals such as EEG's. In this paper, wavelet power spectrum method using wavelet transform which is excellent in detection of transient components of time-varying signals is applied to the classification of three types of Event Related Potential(EP) and compared with the result by fourier transform. In the experiments, two types of photic stimulation, which are caused by eye opening/closing and artificial light, are used to collect the data to be classified. After choosing a specific range of scales, scale-averaged wavelet spectrums extracted from the wavelet power spectrum is used to find features by Back-Propagation(13P) algorithm. As a result, wavelet analysis shows superiority to fourier transform for nonstationary EEG signal classification.

• The Korean Journal of Legal Medicine
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• v.39 no.4
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• pp.115-119
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• 2015

We investigated event-related potentials (ERPs) to estimate the accuracy of eyewitness memories. Participants watched videos of vehicles being driven dangerously, from an anti-impaired driving initiative. The four-letter license plates of the vehicles were the target stimuli. Random numbers were presented while participants attempted to identify the license plate letters, and electroencephalograms were recorded. There was a significant difference in activity 300-500 milliseconds after stimulus onset, between target stimuli and random numbers. This finding contributes to establishing an eyewitness recognition model where different ERP components may reflect more explicit memory that is dissociable from recollection.

• Journal of Environmental Science International
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• v.33 no.5
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• pp.333-343
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• 2024

This study aimed to investigate whether inhaling the aroma of essential oils could alleviate physiological stress responses and mimic the effects of forest therapy in urban settings. Briefly, 31 participants underwent stress index assessments for two days and inhaled the selected plant essential oils. The effects of this treatment on physiological responses were determined through electroencephalogram (EEG) and heart rate variability (HRV) measurements taken before and after inhaling the aroma of essential oils, extracting results for low frequency (LF) and high frequency (HF) components of HRV, as well as 𝜃 and 𝛼 brainwave activities. The results indicated that lavender oil did not yield significant differences, whereas pine, chamomile, and cypress oils exhibited significant differences in effects. Overall, stress relief was associated with enhanced 𝜃 and 𝛼 brainwave activities, a decrease in the LF component and an increase in the HF component of HRV. Among the essential oils studied, pine oil was the most effective. These findings underscore the potential of plant essential oils in replicating the therapeutic benefits of forest therapy, even in urban environments. Further investigations into their utilization are warranted to better understand and harness their therapeutic potential.

• The KIPS Transactions:PartB
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• v.9B no.1
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• pp.35-46
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• 2002

The EEG is a time series of electrical potentials representing the sum of a very large number of neuronal dendrite potentials in the brain. The collective dynamic behavior of neural mass of different brain structures can be assessed from EEG with depth electrodes measurements at regular time intervals. In recent years, the theory of nonlinear dynamics has developed methods for quantitative analysis of brain function. In this paper, we considered it is reasonable or not for ICA apply to EEG analysis. Then we applied ICA to EEG for big toe movement and separated the independent components for 15 samples. The strength of each independent component can be represented on the topological map. We represented ICA can be applied for time and spatial analysis of EEG.