• Proceedings of the Korean Society for Emotion and Sensibility Conference
• /
• 1997.11a
• /
• pp.85-89
• /
• 1997

We present several biosignal measurement results and analysis algorithms for the visual stimulus from International Affective Picture Sytem. Sine human body is nonlinear dynamic system, we investigated both linear and nonlinear methods. We found that the alpha wave of EEG, the chaos of peripheral blood pressure, the LF/HF of HRV and thd retutn map of RR interval were good parameters for the measuremet of human sensibility. These can be used as the parameters for the measurement of human sensibility.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.52 no.3
• /
• pp.136-144
• /
• 2015

The EEG system can be classified as a wired or wireless device. Each device used for the medical or entertainment purposes. The collected EEG signals from sensor are analyzed using feature extractions. A wireless EEG system provides good portability and convenience, however, it requires a mobile system that has heavy computing power. In this paper a single board system is proposed to handle EEG signal processing for BCI applications. Unfortunately, the computing power of a single board system is limited unlike general desktop systems. Thus, parallel approach using multiple single board systems is investigated. The parallel EEG signal processing system that we built demonstrates superlinear speedup for an EEG signal processing algorithm.

• Journal of Biomedical Engineering Research
• /
• v.39 no.1
• /
• pp.55-61
• /
• 2018

The purpose of this study was to investigate the effect of a pillow simulated with cranial sacral therapy (CST) on sleep induction using electroencephalography (EEG). This study included 12 voluntary participants divided into experimental group (CST group) and control group (Non-CST group) to observe EEG changes. The position of the electrode for EEG measurement consists of 8 channels electrodes (Fp1, Fp2, F3, F4, T3, T4, P3 and P4). In this study, we measured the fall asleep time, change of brain activity and sleep wave ratio using EEG wave (${\delta}$, ${\theta}$, ${\alpha}$, ${\beta}$ and ${\gamma}$). As a result, the mean fall asleep time of the experimental group was shorter than that of the control group significantly (p < 0.001). Also in comparison with the control group, both the delta (d) and theta (q) wave corresponding to the slow waves showed a larger increase and the alpha (a) wave showed a larger decrease significantly. The slow waves of experimental group showed a higher rate of significant increase than the control group (p < 0.001). Therefore this study showed that pillow based on CST had an effective in improving sleep induction and quality.

• Journal of Information Processing Systems
• /
• v.14 no.4
• /
• pp.1049-1061
• /
• 2018

As mobile devices such as smartphones and tablet PCs become more popular, users are becoming accustomed to consuming a massive amount of multimedia content every day without time or space limitations. From the industry, the need for user satisfaction investigation has consequently emerged. Conventional methods to investigate user satisfaction usually employ user feedback surveys or interviews, which are considered manual, subjective, and inefficient. Therefore, the authors focus on a more objective method of investigating users' brainwaves to measure how much they enjoy their content. Particularly for multimedia content, it is natural that users will be immersed in the played content if they are satisfied with it. In this paper, the authors propose a method of using a portable and dry electroencephalogram (EEG) sensor device to overcome the limitations of the existing conventional methods and to further advance existing EEG-based studies. The proposed method uses a portable EEG sensor device that has a small, dry (i.e., not wet or adhesive), and simple sensor using a single channel, because the authors assume mobile device environments where users consider the features of portability and usability to be important. This paper presents how to measure attention, gauge and compute a score of user's content immersion level after addressing some technical details related to adopting the portable EEG sensor device. Lastly, via an experiment, the authors verified a meaningful correlation between the computed scores and the actual user satisfaction scores.

• Journal of the HCI Society of Korea
• /
• v.12 no.4
• /
• pp.75-80
• /
• 2017

With the technological development, benefits of Virtual Reality (VR) has become a key of medium in communication research. In addition, explaining human minds with physiological data has become more popular since more accurate and detailed data can be expressed. However, reading brain signals in a virtual environment setting with psychophysiological measures (e.g. EEG and fNIRS) has remained a difficulty for researchers due to a technical constraint. Since a combination of cables for brain measures attached to a head cap obstruct wearing a Head-Mounted Display (HMD) over the cap, measuring brain activities with multiple channels on several areas of the brain is inappropriate in the VR setting. Therefore, we have developed a new brain measurement cap that includes probe connectors and brackets enabling a direct connection to the HMD. We highly expect this method would contribute to cognitive psychology research measuring brain signals with new technology.

• Investigative Magnetic Resonance Imaging
• /
• v.8 no.1
• /
• pp.1-8
• /
• 2004

Purpose : Information about electrical activity inside the brain during fMRl scans is very useful in monitoring physiological function of the patient or locating the spatial position of the activated region in the brain. However, many additional noises appear in the EEG signal acquired during the MRI scan. Gradient induced noise is the biggest one among the noises. In this work, we propose a gradient noise reduction method using the independent component analysis (ICA) method. Materials and Methods : We used a 29-channel MR-compatible EEG measurement system and a 3.0 Tesla MRI system. We measured EEG signals on a subject lying inside the magnet during EPI scans. We selectively removed the gradient noise from the measured EEG signal using the ICA method. We compared the results with the ones obtained with conventional averaging method and PCA method. Results : All the noise reduction methods including the averaging and PCA methods were effective in removing the noise in some extent. However, the proposed ICA method was found to be superior to the other methods. Conclusion : Gradient noise in EEG signals acquired during fMRI scans can be effectively reduced by the ICA method. The noise-reduced EEG signal can be used in fMRI studies of epileptic patients or combinatory studies of fMRI and EEG.

• Investigative Magnetic Resonance Imaging
• /
• v.7 no.2
• /
• pp.108-115
• /
• 2003

Purpose : To evaluate the effect of the gradient switching noise on the ECD source localization with the EEG data recorded during the MRI scan. Materials and Methods : We have fabricated a spherical EEG phantom that emulates a human head on which multiple electrodes are attached. Inside the phantom, electric current dipole(ECD) sources are located to evaluate the source localization error. The EEG phantom was placed in the center of the whole-body 3.0 Tesla MRI magnet, and a sinusoidal current was fed to the ECD sources. With an MRI-compatible EEG measurement system, we recorded the multi channel electric potential signals during gradient echo single-shot EPI scans. To evaluate the effect of the gradient switching noise on the ECD source localization, we controlled the gradient noise level by changing the FOV of the EPI scan. With the measured potential signals, we have performed the ECD source localization. Results : The source localization error depends on the gradient switching noise level and the ECD source position. The gradient switching noise has much bigger negative effects on the source localization than the Gaussian noise. We have found that the ECD source localization works reasonably when the gradient switching noise power is smaller than $10\%$ of the EEG signal power. Conclusion : We think that the results of the present study can be used as a guideline to determine the degree of gradient switching noise suppression in EEG when the EEG data are to be used to enhance the performance of fMRI.

• Transactions on Electrical and Electronic Materials
• /
• v.14 no.6
• /
• pp.299-303
• /
• 2013

Anesthesia, which started being used to remove pain during surgery, has become itself one of the major concerns to be considered during surgery. While actual anesthesia is being performed, patients tend to have unpleasant experiences, due to wakening that accompanies pain, or wakening that does not accompany pain. Since this awakening during anesthesia is a most unpleasant experience in a patient's life, evaluating the depth of anesthesia during surgery is essential for patients to avoid this experience. Although there has been much effort on the understanding and measurement of the depth of anesthesia, while various researches were performed on the need of anesthesia, the development of an indicator that could objectively evaluate the depth of anesthesia, other than by using the patient's vital signs, is still inadequate. Therefore, this study was to develop an objective indicator by using EEG and ECG, which are essentially measured during the surgery, to evaluate the depth of anesthesia. The experiment was performed by taking patients who require a relatively short operation time, and general inhalation anesthetics among surgical patients in obstetrics and gynecology as the subjects of experiment, to measure the EEG and ECG signals of patients under anesthetics. The result showed that SEF using EEG and LF, HF using ECG signal and correlation dimension analysis parameter were valuable parameters that could measure the depth of anesthesia, by the stage of anesthesia.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2015.05a
• /
• pp.947-950
• /
• 2015

In the cause of car accidents in Korea, drowsy driving has shown that it is larger fctors than drunk driving. Therefore, in order to prevent drowsy driving accidents, drowsiness detection and warning system for drivers has recently become a very important issue. Furthermore, Many researches have been published that measuring alpha wave of EEG signals is the effective way in order to be aware of drowsiness of drivers. In this study, we have developed EEG measuring device that applies a signal processing algorithm using the LabView program for detecting drowsiness. According to results of drowsiness inducement experiments for small test subjects, it was able to detect the pattern of EEG, which means drowsy state based on the changing of power spectrum, counterpart of alpha wave. After all, Comparing to the results of drowsiness pattern between commercial equipments and developed device, we could confirm acquiring similar pattern to drowsiness pattern. With this results, the driver's drowsiness prevention system expect that it will be able to contribute to lowering the death rate caused by drowsy driving accidents.

• Journal of The Korean Society of Integrative Medicine
• /
• v.2 no.2
• /
• pp.79-88
• /
• 2014

Purpose: This study investigated the relationship between smartphone addiction propensities and compare muscle activity of the forearms and brain wave depend on the type of smartphone when inputting text messages. Method: We used an EMG to measure the change in muscle activity by attaching pads to the four muscles in both forearms of all 16 participants. We simultaneously conducted EEG measurements by observing the changes in alpha and beta waves recorded from electrode attached to both ears and the forehead of the participants. The participants had to input a given text using three different types of smartphones for ten minutes each. Result: The comparison of the EMG when inputting text involved a one way analysis of variance and the results showed that the iPad3 was highest for muscle activity followed by GALAXY Note2 and iPhone4. For EEG measurement, a one way analysis of variance was also used and the results showed iPhone4 was higest followed by GALAXY Note2 and finally iPad3 for EEG stress score. Conclusion: The results are thought to be used as reference data for smart phone users.