• Journal of the Institute of Convergence Signal Processing
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• v.13 no.3
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• pp.136-141
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• 2012

One of the main reasons for serious road accidents is driving while drowsy. 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. One of the effective signals is to measure electroencephalogram (EEG) signals and electrooculogram (EOG) signals. 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, drowsiness, sleepiness. This paper proposes a neural-network-based drowsiness detection system using Linear Predictive Coding (LPC) coefficients as feature vectors and Multi-Layer Perceptron (MLP) as a classifier. Samples of EEG data from each predefined state were used to train the MLP program by using the proposed feature extraction algorithms. The trained MLP program was tested on unclassified EEG data and subsequently reviewed according to manual classification. The classification rate of the proposed system is over 96.5% for only very small number of samples (250ms, 64 samples). Therefore, it can be applied to real driving incident situation that can occur for a split second.

• Annals of Clinical Neurophysiology
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• v.16 no.1
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• pp.15-20
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• 2014

Background: Triphasic waves are one of the electroencephalographic patterns that can be usually seen in metabolic encephalopathy. The aim of this study is to compare the clinical and electrophysiologic profiles between patients with and without triphasic waves in metabolic encephalopathy, and reassess the significance of triphasic waves in metabolic encephalopathy. Methods: We recruited 127 patients with metabolic encephalopathy, who were admitted to our hospital. We divided these admitted patients into two groups; those with and without triphasic waves. We analyzed the difference of duration of hospitalization, mortality rate during admission, Glasgow Coma Scale, severity of electroencephalographic alteration, and presence of acute symptomatic seizures between these two groups. Results: Of the 127 patients with metabolic encephalopathy, we excluded 67 patients who did not have EEG, and 60 patients finally met the inclusion criteria for this study. Patients with triphasic waves had more severe electroencephalographic alterations, lower Glasgow Coma Scale, and more acute symptomatic seizures than those without triphasic waves. After adjusting the clinical variables, Glasgow Coma Scale and acute symptomatic seizures were only significantly different between patients with and without triphasic waves. Conclusions: We demonstrated that patients with triphasic waves in metabolic encephalopathy had more significant impairment of the brain function.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.4
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• pp.2170-2178
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• 2014

In this paper, electroencephalographic (EEG) signal of one among subjects measured biosignal with visual evoked stimuli inducing the concentration was analyzed to detect the changes in the attention status during attention task fulfillment from January to February, 2011. The independent component analysis (ICA) was applied to EEG signals to isolate the attention related innate source signal within the brain and Electroculogram (EOG) artifact from measured EEG signals at the scalp. The consecutive accumulation of short time Fourier transformed (STFT) attention source signal with excluded EOG artifact can enhance the regular depiction of EPOCH graph and spectral color map representing time-varying pattern. The extracted attention indices associated with somatosensory rhythm (SMR: 12-15 Hz), and theta wave (4-7 Hz) increase marginally over time. Throughout experimental observation, the ICA with STFT can be used for the assessment of participants' status of attention.

• Weed & Turfgrass Science
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• v.7 no.2
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• pp.130-139
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• 2018

The colors of packaging of herbicides and non-selective herbicides on the market in Korea are defined as brown and red, respectively, according to the notification of RDA. The present study aimed to understand consumer's electroencephalographic (EEG) response when looking at brown and red colors of herbicide and non-selective herbicide packaging papers. The EEG cap was placed on the scalp of each participant (men and women, 10 to 20 years old) and white (control) - brown - white - red colors were sequentially displayed for 5 seconds using the computer monitor. The EEG was measured and statistical analysis was performed using SPSS. For the brown color of the herbicide, men showed a decrease in concentration and a distracting response due to a decrease in the ratio of mid beta to theta (RMT) and the spectral edge of frequency (SEF90). In women, an increase in the ratio of SMR to theta (RSMT) and the spectral edge frequency 50% of the alpha (ASEF) was observed in different brain regions and these EEG changes may enhance the relaxation, stabilization and awakening states of the brain. For the red color of the non-selective herbicide, ASEF increased psychological stability in men. In women, a decrease in absolute high beta (AHB) may associate with a decrease in attention state of the brain. Overall data of the present study clearly revealed that the colors of two herbicides showed significantly different EEG response and gender difference.

• Horticultural Science & Technology
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• v.34 no.3
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• pp.472-487
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• 2016

This study aimed to determine the effects of floral fragrances on human brain waves and moods. A total of 44 subjects participated in this experiment. Group 1 consisted of 11 male and 14 female college students with a mean age of 24.5 years (${\pm}2.23$) and Group 2 consisted of 10 males and 9 females with a mean age of 54.3 years (${\pm}2.98$). Subjects were exposed to floral fragrances of Rosa hybrida, 'Hera' (hereafter referred to as "rose"), Cymbidium faberi (hereafter referred to as "orchid"), or odorless control flowers (hereafter referred to as "control"). Experiments took place in three rooms (rose, orchid, and control). Electroencephalographs (EEGs) were recorded during exposure to the odors and the data were processed using quantitative electroencephalographic (QEEG) techniques. The changing EEG patterns were analyzed by brain mapping and compressed spectral arrays, and the subjects' preferences (hedonic evaluations) were quantified with an A1 index. Increased activation of absolute alpha waves was verified on six of the eight EEG channels, with the right frontal and left occipital lobes exhibiting no changes and the left parietal region showing the greatest activation. According to the QEEG measurements in the electrode sites over the frontal, temporal, parietal, and occipital lobes, the strongest absolute alpha waves were induced in the parietal lobes, followed by the temporal lobes, with the other lobes showing no significant changes. On brain maps, the orchid fragrance induced greater absolute alpha and absolute mid-beta activities compared with the rose and control fragrances, and the rose fragrance induced high absolute mid-beta activation. To identify emotional responses to floral fragrances, the subjects were requested to fill in a questionnaire and the resulting odor-related emotional descriptors were analyzed using semantic differential and factor analysis. Principal component analysis identified "elegant" as the first principal component describing the floral fragrance, followed by "refreshing" and "aromatic." The subjects gave orchid higher scores for "elegant" and "refreshing," while finding rose more "aromatic." Differences in hedonic evaluation revealed by the A1 index appeared in the 65-115 sec range of scent exposure time. The subjects with ages of around 50 years showed olfactory preferences throughout the entire experimental time of 160 sec, most markedly in the later time segment (115-165 sec), showing an increasing preference with increasing exposure time. We conclude that rose fragrance can improve concentration by creating an aromatic environment conducive to a concentrated and calm state of mind, and orchid fragrance can make people feel pampered and relaxed by creating an elegant and refreshing environment.

• Journal of People, Plants, and Environment
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• v.24 no.6
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• pp.639-649
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• 2021

Background and objective: Various images from visual display terminals (VDTs) as well as living lighting are important parts of our daily life; thus, properly controlling the lighting environment - that is, illuminance, color temperature and good images from VDTs - can have a substantial effect on improving the mental health and work efficiency in everyday life. We examined electroencephalography (EEG) and heart rate variability (HRV) responses to various lighting conditions in 25 university students as they viewed images of a green landscape or traffic congestion. Methods: EEG was performed in darkness and when the room was illuminated with 10 different light-emitting diode (LED) color temperatures, while the EEG and HRV responses to green landscape or traffic congestion image stimuli were measured in darkness and during room illumination with three different LED color temperatures. Results: We found a significant difference between darkness and high LED illumination (400 lx) at 7 (CZ, F4, FZ, O1, O2, OZ, and T6) of 30 channels, while the alpha wave activity increased during darkness. In the second experiment, the green landscape image stimuli in the 30 lx-2600 K lighting condition elicited theta wave activity on the EEG, whereas the traffic congestion image stimuli under high LED illumination elicited high beta and gamma wave activities. Moreover, the subjects exhibited better stress coping ability and heart rate stability in response to green landscape image stimuli under illuminated conditions, according to their HRV. Conclusion: These results suggest that lower color temperatures and illumination levels alleviate tension, and that viewing green landscape image stimuli at low illumination, or in darkness, is effective for reducing stress. Conversely, high illumination levels and color temperatures are likely to increase tension and stress in response to traffic congestion image stimuli.

• Journal of Electrical Engineering and Technology
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• v.11 no.6
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• pp.1812-1824
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• 2016

The neural dynamics underlying the causal network during motor planning or imagery in the human brain are not well understood. The lack of signal processing tools suitable for the analysis of nonlinear and nonstationary electroencephalographic (EEG) hinders such analyses. In this study, noise-assisted multivariate empirical mode decomposition (NA-MEMD) is used to estimate the causal inference in the frequency domain, i.e., partial directed coherence (PDC). Natural and intrinsic oscillations corresponding to the motor imagery tasks can be extracted due to the data-driven approach of NA-MEMD, which does not employ predefined basis functions. Simulations based on synthetic data with a time delay between two signals demonstrated that NA-MEMD was the optimal method for estimating the delay between two signals. Furthermore, classification analysis of the motor imagery responses of 29 subjects revealed that NA-MEMD is a prerequisite process for estimating the causal network across multichannel EEG data during mental tasks.

• Journal of Pharmacopuncture
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• v.22 no.3
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• pp.166-170
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• 2019

Objectives: Attentional and memory functions are important aspects of neural plasticity that, theoretically, should be amenable to pharmacopuncture treatments. A previous study from our laboratory suggested that quantitative electroencephalographic (qEEG) measurements of theta/beta ratio (TBR), an index of attentional control, may be indicative of academic performance in a first-semester medical school course. The present study expands our prior report by extracting and analyzing data on frontal theta and beta asymmetries. We test the hypothesis that the amount of frontal theta and beta asymmetries (fTA, fBA), are correlated with TBR and academic performance, thereby providing novel targets for pharmacopuncture treatments to improve cognitive performance. Methods: Ten healthy male volunteers were subjected to 5-10 min of qEEG measurements under eyes-closed conditions. The qEEG measurements were performed 3 days before each of first two block examinations in anatomy-physiology, separated by five weeks. Amplitudes of the theta and beta waveforms, expressed in ${\mu}V$, were used to compute TBR, fTA and fBA. Significance of changes in theta and beta EEG wave amplitude was assessed by ANOVA with post-hoc t-testing. Correlations between TBR, fTA, fBA and the raw examination scores were evaluated by Pearson's product-moment coefficients and linear regression analysis. Results: fTA and fBA were found to be negatively correlated with TBR (P<0.03, P<0.05, respectively) and were positively correlated with the second examination score (P<0.03, P=0.1, respectively). Conclusion: Smaller fTA and fBA were associated with lower academic performance in the second of two first-semester medical school anatomy-physiology block examination. Future studies should determine whether these qEEG metrics are useful for monitoring changes associated with the brain's cognitive adaptations to academic challenges, for predicting academic performance and for targeting phamacopuncture treatments to improve cognitive performance.

• Korean Journal of Biological Psychiatry
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• v.19 no.1
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• pp.65-69
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• 2012

Objectives : Much is still unknown about the neurophysiological mechanisms or dynamics of the sleep onset process. Detrended fluctuation analysis (DFA) is a new tool for the analysis of electroencephalography (EEG) that may give us additional information about electrophysiological changes. The purpose of this study is to analyze long-range correlations of electroencephalographic signals by DFA and their changes in the sleep onset process. Methods : Thirty channel EEG was recorded in 61 healthy subjects (male:female=34:27, age=$27.2{\pm}3.0$ years). The scaling exponents, alpha, were calculated by DFA and compared between four kinds of 30s sleep-wakefulness states such as wakefulness, transition period, early sleep, and late sleep (stage 1). These four states were selected by the distribution of alpha and theta waves in O1 and O2 electrodes. Results : The scaling exponents, alpha, were significantly different in the four states during sleep onset periods, and also varied with the thirty leads. The interaction between the sleep states and the leads was significant. The means (${\pm}$ standard deviation) of alphas for the states were 0.94 (${\pm}0.12$), 0.98 (${\pm}0.12$), 1.10 (${\pm}0.10$), 1.07 (${\pm}0.07$) in the wakefulness, transitional period, early sleep and late sleep state respectively. The mean alpha of anterior fifteen leads was greater than that of posterior fifteen leads, and the two regions showed the different pattern of changes of the alpha during the sleep onset periods. Conclusions : The characteristic findings in the sleep onset period were the increasing pattern of scaling exponent of DFA, and the pattern was slightly but significantly different between fronto-temporal and parieto-occipital regions. It suggests that the long-range correlations of EEG have a tendency of increasing from wakefulness to early sleep, but anterior and posterior brain regions have different dynamical process. DFA, one of the nonlinear analytical methods for time series, may be a useful tool for the investigation of the sleep onset period.

• Korean Journal of Biological Psychiatry
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• v.14 no.1
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• pp.42-47
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• 2007

Objectives : Studies have reported differences between depressed adults and controls in quantitative measures of EEG alpha asymmetry, but, there are few using Korean subjects. So, the present study compared EEG regional alpha asymmetries of patients having major depressive disorder(MDD) and normal controls. Methods : The subjects in this study were 11 unmedicated unipolar depressed patients and 11 non-depressed, age matched controls. Resting EEG(eyes closed and eyes open) was recorded from each participant using 8 scalp electrodes. Beck Depression Inventory(BDI), 17-item Hamilton Depression Rating Scale(HDRS), Zung's Self-Rating Depression Scale(SDS) and Spielberger's State-Trait Anxiety Inventory(STAI) were used to evaluate depression and anxiety symptoms. Results : The severities of depression measured by self-report questionnaires were positively associated with those of anxiety(state and trait) ; The subjects were both anxious and depressed. Anxious-depressed patients differed from controls in alpha asymmetry at T4 channels. They showed evidence of greater activation over right than left temporal site. Conclusion : These findings are consistent with the previousely reported alpha asymmetry of depressed patients with an anxiety disorder. The failure to find the evidence of reduced right parietal activity in depression is presumed to be due to opposing effects of comorbid anxiety on parietotemporal activity.