• 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.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.768-773
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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 drowsiness detection system using Linear Predictive Coding (LPC) coefficients and Support Vector Machine (SVM). Samples of EEG data from each predefined state were used to train the SVM program by using the proposed feature extraction algorithms. The trained SVM 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.

• Sleep Medicine and Psychophysiology
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• v.10 no.1
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• pp.52-60
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• 2003

Objectives: Stage 1 sleep provides important information regarding interpretation of nocturnal polysomnography, particularly sleep onset. It is a short transition period from wakeful consciousness to sleep. The lack of prominent sleep events characterizing stage 1 sleep is a major obstacle in automatic sleep stage scoring. In this study, utilization of simultaneous EEG and EOG processing and analyses to detect stage 1 sleep automatically were attempted. Methods: Relative powers of the alpha waves and the theta waves were calculated from spectral estimation. A relative power of alpha waves less than 50% or relative power of theta waves more than 23% was regarded as stage 1 sleep. SEM(slow eye movement) was defined as the duration of both-eye movement ranging from 1.5 to 4 seconds, and was also regarded as stage 1 sleep. If one of these three criteria was met, the epoch was regarded as stage 1 sleep. Results were compared to the manual rating results done by two polysomnography experts. Results: A total of 169 epochs were analyzed. The agreement rate for stage 1 sleep between automatic detection and manual scoring was 79.3% and Cohen’s Kappa was 0.586 (p<0.01). A significant portion (32%) of automatically detected stage 1 sleep included SEM. Conclusion: Generally, digitally-scored sleep staging shows accuracy up to 70%. Considering potential difficulty in stage 1 sleep scoring, accuracy of 79.3% in this study seems to be strong enough. Simultaneous analysis of EOG differentiates this study from previous ones which mainly depended on EEG analysis. The issue of close relationship between SEM and stage 1 sleep raised by Kinnari remains a valid one in this study.

• Journal of Biomedical Engineering Research
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• v.25 no.1
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• pp.11-19
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• 2004

Stage 1 sleep provides important information regarding interpretation of nocturnal polysomnography, particularly sleep onset. It is a short transition period from wakeful consciousness to sleep. Lack of prominent sleep events characterizing stage 1 sleep is a major obstacle in automatic sleep stage scoring. In this study, we attempted to utilize simultaneous EEC and EOG processing and analyses to detect stage 1 sleep automatically. Relative powers of the alpha waves and the theta waves were calculated from spectral estimation. Either the relative power of alpha waves less than 50％ or the relative power of theta waves more than 23％ was regarded as stage 1 sleep. SEM (slow eye movement) was defined as the duration of both eye movement ranging from 1.5 to 4 seconds and regarded also as stage 1 sleep. If one of these three criteria was met, the epoch was regarded as stage 1 sleep. Results f ere compared to the manual rating results done by two polysomnography experts. Total of 169 epochs was analyzed. Agreement rate for stage 1 sleep between automatic detection and manual scoring was 79.3％ and Cohen's Kappa was 0.586 (p＜0.01). A significant portion (32％) of automatically detected stage 1 sleep included SEM. Generally, digitally-scored sleep s1aging shows the accuracy up to 70％. Considering potential difficulties in stage 1 sleep scoring, the accuracy of 79.3％ in this study seems to be robust enough. Simultaneous analysis of EOG provides differential value to the present study from previous oneswhich mainly depended on EEG analysis. The issue of close relationship between SEM and stage 1 sleep raised by Kinnariet at. remains to be a valid one in this study.

• Science of Emotion and Sensibility
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• v.19 no.1
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• pp.83-94
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• 2016

The purpose of this study is to find out the relationships of brain laterality, active EEG over all brain regions and personality traits by measuring EEG signals on the basis of the counseling psychology personality theories. For this study, the EEG of ninety-six college students as measured by an eight channel EEG device and analyzed through the computer and the data of their Big Five Personality Test were analyzed by statistical analysis. The result was that when theta's laterality at the prefrontal lobe is bigger, neuroticism is higher in the personality factors. On each of the brain regions, theta's activity on the left of the prefrontal lobe makes higher neuroticism but lower conscientiousness, and beta's activity on the left of the frontal lobe makes lower extroversion and openness to experience. These results showed that there are statistically meaningful relationships between the brain region activated specific EEG and individual personality or psychological traits. This study branched out into theta band while most previous studies measured in alpha and beta band. Also from these results it suggested the counseling strategy with the brain and follow-up studies.

• Science of Emotion and Sensibility
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• v.26 no.3
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• pp.41-52
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• 2023

The purpose of this study was to determine the effects of resilience on information processing. Thirty-nine male high school students were randomly selected and assigned to one of the three experimental groups: (1) high group (n = 13), (2) middle group (n = 13) and low group (n = 13) according to their resilience scale (KRQ-53) scores. The tasks were simple reaction time, choice reaction time-1, and choice reaction time -2. Electroencephalogram (EEG) was measured at Fp1, Fp2, F3, F4, Fz, Cz and Pz. A 3 × 8 × 4 (groups × areas × times) ANOVA with repeated measures on the last factor was calculated to determine resilience effects on EEG. P300 was analyzed using a 3 × 3 × 8 (groups × tasks × areas) ANOVA. The results showed that the theta waves of the middle group were higher than those of the high and low groups. Second, as a result of analyzing alpha waves, the high group demonstrated higher alpha waves than the middle and low groups. Third, the mid-beta waves of the middle and low groups were higher than those of the high group. Lastly, the result of this study showed that the P300 amplitude of the middle group was higher than that of the high and low groups. These results indicated that the middle group processed cognitive information more efficiently than the other two groups. The findings of this study demonstrated that cognitive information processing ability varies depending on the degree of resilience.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.1
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• pp.462-470
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• 2015

This study examined for which socio-economic difference effects on brain function and Problem behavior syndrome in children. About a children with no disorders, diseases or cognitive dysfunction-30 were from LIC children and another 30, from MC ones, the study was conducted by measuring and analyzing the data using brain function analysis and K-CBCL from January to April, 2013. The results of the study are as follows. First, it was found that the ratio of LIC's theta(${\Theta}$) and SMR waves and that of delta(${\delta}$), high beta(${\beta}h$), alpha(${\alpha}$) and low beta(${\beta}l$) waves showed significantly higher values than MC children. Second, concerning the symptoms of child behavioral problems, LIC showed significantly higher values than MC children in symptoms of the body, depression and anxiety, social immaturity, thinking problems, attention problems, aggression, internalization, externalization, overall behavioral problems, and emotional instability. MC children showed significantly higher values than LIC chidren in symptoms of social, academic-performance, total social skills. In conclusion, the significant difference of the brain functions and the symptoms of child behavioral problems between LIC and MC children showed that the socio-ecnomic difference has an influence on the same functions and symptoms above.

• Journal of the Korea Convergence Society
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• v.12 no.4
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• pp.61-68
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• 2021

The purpose of this study was to derive their cognitive, psychological, and behavioral characteristics through a fusion study that analyzed the quantified results of post-homeless people participating in the housing support project after the electroencephalogram test. Participants in this study are 6 people who have lived in temporary homeless facilities or homeless living facilities and have expressed their intention to participate in the housing support project. Electroencephalogram measurements were performed using Brainmaster equipment according to the International 10-20 Law. As a result of the analysis, the study participants showed high levels of Theta wave, Beta wave, and High Beta wave, which show the peculiarity of the emotional aspect, which is a result of showing emotional characteristics such as anger, anxiety, and insomnia in the emotional aspect. Therefore, it is suggested that they provide stress management, counseling, and physical health management services in order to maintain a stable residential life in the community in the future. The results of this study presented a quantitative basis for the cognitive, psychological, and behavioral characteristics of homeless people, and suggests a support system necessary for them to maintain self-reliance in communities in the future.

• 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.

• Journal of Korean Society of Transportation
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• v.24 no.7 s.93
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• pp.7-14
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• 2006

Most researches about rumble strips have Presented only the before-and-after analysis of the accidents. So, Researchers have not dealt with the estimation of rumble strip's effectiveness on the driver's alertness. In this study. the effectiveness of the rumble strips on the driver's alertness was estimated by measuring the bio-signal transmitted from the driver. The bio-signal acquired for this experiments were theta wave in central lobe. The experimental results revealed that the theta waves as measured form the drivers's head while in the rumble strip section differed from those while in non-rumbled section; 74 percent decrease in theta wave value, respectively. This fact finding could mean that the driver's alertness increased from 74 percent while in the rumble strip section of the road. In all five trials of driving experiments on the rumble strip section, all the drivers showed the best alertness as measured by the theta waves in the first driving trial.