• The Journal of the Korea institute of electronic communication sciences
• /
• v.15 no.3
• /
• pp.527-532
• /
• 2020

In this paper, we presented the results of analysis through EEG measurement for the purpose of checking the frequency band of EEG signals that can be used for personal authentication. The measurement status was divided into the open-eye state and the closed-eye state depending on the presence or absence of an optical task. The data measured in the EEG experiments was divided into seven frequency bands : delta waves, theta waves, alpha waves, SMR waves, mid-beta waves, beta waves and gamma waves to identify the frequency band with the smallest power fluctuation over time. In our results, there was no significant difference between the open-eye state and the closed-eye state, and the SMR waves and mid-beta waves related to human concentration had the smallest fluctuation in power over time, and were a highly reproducible frequency band.

• Proceedings of the KOSOMBE Conference
• /
• v.1996 no.11
• /
• pp.233-236
• /
• 1996

We are exposed to the various external stimuli input from the environment, which cause emotional changes based on the characteristics of the stimuli. Unfortunately, there are no quantitative results on relationship between human sensibility and the characteristics of physiological signals. The objective of this study was to quantify EEG signals evoked by auditory stimulation based on the assumption that the analysis of the variability on the characteristics of the EEG waveform may provide the significant information regarding changes in psychological states of the subject. The experiment was devised with seven experimental conditions, which are control and six different types of auditory stimulation. Twenty subjects were used to obtain EEGs while introducing auditory stimulation. Wavelet transformation was employed to analyze the EEG signals. The results showed that the reconstructed signals at the decomposition level revealed the different energy value on the EEG signals. Also, general patterns of EEG signals in rest state compare with negative and positive stimulus were found. This study could be extended to estabilish an algorithm which distinguishes psychophysiological states of the subjects exposed to the auditory stimulation.

• Information Systems Review
• /
• v.22 no.3
• /
• pp.1-14
• /
• 2020

This study aims to contribute to the existing literature in creativity from the viewpoint of neuro-physiological analysis. Further, we looked at emotional influences on creativity within a business problem-solving context that implemented the use of a cognitive map in exploring creativity. For this purpose, we measured brain cortical activity as people solved a business strategy problem to explore the neural mechanisms of "insight problems" that are influenced by distinct emotions. Through an Electroencephalography (EEG) analysis of 34 qualified participants, we investigated the relationship between emotions and business problem-solving creativity (BPSC). Insightful results were derived such that participants primed in a negative condition evoked higher temporal alpha band activity compared to those primed in the positive condition. Meanwhile, there were no significant differences between two priming conditions on the other band activities. Therefore, this study sheds a very positive light on the scholarly value of conducting rigorous studies about the relationship between emotional states and BPSC status.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• 2011.06a
• /
• pp.187-188
• /
• 2011

Independent component analysis (ICA) is a method separating the mixture of signals into statistically and mutually independent ones. It has been applied to not only the Cocktail-party problem but also EEG analysis using the EEG waveform, digital signal processing, image processing and cognitive technique field actively. This study aims to propose a procedure to estimate the modal responses and mode shapes of a floating structure by using the ICA method from measured responses of the floating structure.

• Korean Journal of Clinical Laboratory Science
• /
• v.47 no.4
• /
• pp.225-229
• /
• 2015

Epilepsy is a disorder that causes recurring seizures, and the most objective and useful test for detecting epilepsy is the electroencephalogram (EEG). The subjects of this study are 244 patients who received an EEG after being diagnosed with epilepsy at Seoul National University Hospital in 2014, and who have agreed to the purpose of the study. Based on the EEG results, subjects were divided into normal and abnormal groups with 122 subjects in each group, regardless of their gender and age, to investigate the correlation of EEG and complete blood cell count (CBC) test results. The four significant categories that displayed significant correlation between EEG results and CBC hematological measurements in this study were the white blood cell (WBC), red blood cell (RBC), neutrophil, and lymphocyte tests. The WBC (p<0.05) and neutrophil (p<0.01) showed a positive correlation with EEG results, while RBC (p<0.05) and lymphocyte (p<0.01) showed a negative correlation. One of the limitations of this study is that it is lacking the blood test result analysis according to the types of anti-epilepsy medicine. However, the analysis of EEG results by the same disease has significant meaning. Therefore, further studies are needed to statistically analyze more data in the future.

• Korean Journal of Cognitive Science
• /
• v.21 no.2
• /
• pp.309-338
• /
• 2010

In this paper, we studied the brain-computer interface (BCI). BCIs help severely disabled people to control external devices by analyzing their brain signals evoked from motor imageries. The findings in the field of neurophysiology revealed that the power of $\beta$(14-26 Hz) and $\mu$(8-12 Hz) rhythms decreases or increases in synchrony of the underlying neuronal populations in the sensorymotor cortex when people imagine the movement of their body parts. These are called Event-Related Desynchronization / Synchronization (ERD/ERS), respectively. We implemented a BCI-based mouse interface system which enabled subjects to control a computer mouse cursor into four different directions (e.g., up, down, left, and right) by analyzing brain signal patterns online. Tongue, foot, left-hand, and right-hand motor imageries were utilized to stimulate a human brain. We used a non-invasive EEG which records brain's spontaneous electrical activity over a short period of time by placing electrodes on the scalp. Because of the nature of the EEG signals, i.e., low amplitude and vulnerability to artifacts and noise, it is hard to analyze and classify brain signals measured by EEG directly. In order to overcome these obstacles, we applied statistical machine-learning techniques. We could achieve high performance in the classification of four motor imageries by employing Common Spatial Pattern (CSP) and Linear Discriminant Analysis (LDA) which transformed input EEG signals into a new coordinate system making the variances among different motor imagery signals maximized for easy classification. From the inspection of the topographies of the results, we could also confirm ERD/ERS appeared at different brain areas for different motor imageries showing the correspondence with the anatomical and neurophysiological knowledge.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.15 no.4
• /
• pp.2170-2178
• /
• 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 the Korean Institute of Intelligent Systems
• /
• v.18 no.5
• /
• pp.605-610
• /
• 2008

There are many methods for Human-Computer Interface. Recently, many researchers are studying about Brain-Signal this is because not only the disabled can use a computer by their thought without their limbs but also it is convenient to general people. But, studies about it are early stages. This paper proposes an EEG signals measurement and analysis methods for Brain-Computer Interface. Our purpose of this research is recognition of subject's intention when they imagine moving their arms. EEG signals are recorded during imaginary movement of subject's arms at electrode positions Fp1, Fp2, C3, C4. We made an analysis ERS(Event-Related Synchronization) and ERD(Event-Related Desynchronization) which are detected when people move their limbs in the ${\mu}$ waves and ${\beta}$ waves. Results of this research showed that ${\mu}$ waves are decreased and ${\beta}$ waves are increased at left brain during the imaginary movement of right hand. In contrast, ${\mu}$ waves are decreased and ${\beta}$ waves are increased at right brain during the imaginary movement of left hand.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.63 no.7
• /
• pp.968-975
• /
• 2014

This paper proposed a evaluation method for individual's subjective preferred location using EEG and emotional assessment. Visual stimulus were sequentially presented a total six points(the top and the bottom of the left, the top and the bottom of the center and the top and the bottom of the right on the screen). EEG were measured from twenty subjects according to each six points. At the same time, we were executed evaluation of subjects preferred location from emotional assessment. Alpha and beta wave were measured in Fp1, Fp2, F7 and F8 location, followed by ten to twenty electrode system. Correlations and variations of alpha and beta wave from each channel were calculated and analyzed. Because of the number of subjects population under 30, we used Speareman test for a correlation analysis between alpha and beta wave. Also, emotional assessments which compose of visual sense harmony, visual sense stability, stability of position and the visibility were performed and were analyzed by average and frequency. After visual stimulus, emotional assessments were performed. From the variance analysis of EEG, beta wave from F7 was appeared statistically significant as significance probability of 0.006. Also, between alpha wave and beta wave appeared a negative correlation(r=-0.190). From the post-hoc test of F7 beta wave, location 1, 5 and 6 appeared to difference statistically significant. Emotional assessment result according to six positions showed 0.00 significance probability. Thus, location and emotional assessment appeared to influence on each other. From the average and frequency analysis of emotional assessment, location 2 showed obtained of best emotional assessment score and appeared lower beta wave than other locations. Finally, most subjects showed a preference for location 2. Through obtained results in this paper, will be helpful to about human emotional assessment and EEG research.

• Journal of the Korea Society of Computer and Information
• /
• v.15 no.10
• /
• pp.105-112
• /
• 2010

In this paper, the bio-signals, such as EEG, ECG were measured with a sensor and their characters were drawn out and analyzed. With results from the analysis, four emotion of rest, concentration, tension and depression were inferred. In order to assess one's emotion, the characteristic vectors were drawn out by applying various ways, including the frequency analysis of the bio-signals like the measured EEG and HRV. RBFN, a neural network of the complex structure of unsupervised and supervised learning, was applied to classify and infer the deducted information. Through experiments, the system suggested in this thesis showed better capability to classify and infer than other systems using a different neural network. As follow-up research tasks, the recognizance rate of the measured bio-signals should be improved. Also, the technology which can be applied to the wired or wireless sensor measuring the bio-signals more easily and to wearable computing should be developed.