• Journal of Biomedical Engineering Research
• /
• v.18 no.4
• /
• pp.421-428
• /
• 1997

Magnetic field sensors made from superconducting quantum interference device (SQUID) are the most sensitive low-frequency sensors available, enabling measurements of extremely weak magnetic fields from the brain. Neuromagnetic measurements allow superior spatial resolution, compared with the present electric measurements, and superior temporal resolution, compared with the fMRl and PET, providing useful informations for the functional diagnoses of the brain. We developed a 4-channel SQUID system for neuromagnetic applications. The main features of the system are its simple readout electronics and compact pickup coil structure. A magnetically shielded room has been constructed for the reduction of environmental magnetic noises. The developed SQUID system has noise level lower than the magnetic noise from the brain. Magnetic field signals of the spontaneous r-rhythm activity and auditory evoked magnetic fields have been measured.

• Journal of the Korean Society of Clothing and Textiles
• /
• v.45 no.2
• /
• pp.305-316
• /
• 2021

In this study, the effect of wearing functional compression pants is verified using a lower-limb wearable robot through a bio-signal analysis and subjective fit evaluation. First, the compression area to be applied to the functional compression pants is derived using the quad method for nine men in their 20s. Subsequently, functional compression pants are prepared, and changes in Electroencephalogram (EEG) and Electrocardiogram (ECG) signals when wearing the functional compression and normal regular pants inside a wearable robot are measured. The EEG and ECG signals are measured with eyes closed and open. Results indicate that the Relative alpha (RA) and Relative gamma wave (RG) of the EEG signal differ significantly, resulting in increased stability and reduced anxiety and stress when wearing the functional compression pants. Furthermore, the ECG analysis results indicate statistically significant differences in the Low frequency (LF)/High frequency (HF) index, which reflect the overall balance of the autonomic nervous system and can be interpreted as feeling comfortable and balanced when wearing the functional compression pants. Moreover, subjective sense is discovered to be effective in assessing wear fit, ease of movement, skin friction, and wear comfort when wearing the functional compression pants.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.23 no.4
• /
• pp.354-359
• /
• 2013

In this paper, we suggested the method for implementation of unsupervised nonlinear classification using Binary Harmony Search (BHS) algorithm, which is known as a optimization algorithm. Various algorithms have been suggested for classification of feature vectors from the process of machine learning for pattern recognition or EEG signal analysis processing. Supervised learning based support vector machine or fuzzy c-mean (FCM) based on unsupervised learning have been used for classification in the field. However, conventional methods were hard to apply nonlinear dataset classification or required prior information for supervised learning. We solved this problems with proposed classification method using heuristic approach which took the minimal Euclidean distance between vectors, then we assumed them as same class and the others were another class. For the comparison, we used FCM, self-organizing map (SOM) based on artificial neural network (ANN). KEEL machine learning datset was used for simulation. We concluded that proposed method was superior than other algorithms.

• Science of Emotion and Sensibility
• /
• v.24 no.1
• /
• pp.37-44
• /
• 2021

A study was conducted on the effects of improving concentration by obscuring the peripheral vision using a blindfold that only covers the left and right sides of the field of view. The blindfold was trapezoidal in shape (5 × 4.8 cm in length and width) and was fixed to the left and right sides of the glasses with fixing clips. The material was a black-colored polypropylene (PP) and weighed 2.3 g including the clip. Qualitative and quantitative evaluations were performed on 50 healthy college students during the 15 days of using a blindfold. The qualitative analysis was performed utilizing a questionnaire regarding the improvement of concentration and the structure of the blindfold. EEG was measured while watching a learning video that required attention for quantitative analysis, and signal power and ERD/S analyses were performed for the mid β band (15-20 Hz) at the F4 position, which was the frontal lobe. The results showed that 40 of the 50 people reported improved concentration when they wore a vision shield, and 80% of the total subjects found it to be effective. From the quantitative evaluation, the ERS peak (p = 0.023) and the ERD + ERS peak value showed a significant difference (p = 0.017). In conclusion, concentration still improved even if only the left and right visual fields were used. Thus, it is expected that blindfolding could be used in various environments that require concentration.

• Science of Emotion and Sensibility
• /
• v.13 no.3
• /
• pp.429-440
• /
• 2010

It is known that as many as 1 in 91 children are diagnosed with an autistic spectrum disorder, and the incidence rate of the autistic spectrum disorder is much higher than that of cancer in Korea. It is necessary to develop a novel technology to sense their emotional status and give proper psychological diagnosis and therapy, since the children with autistic spectrum disorder usually do not express their own emotional status. This article presents the state-of-the-arts on the affective computing technologies that include recognition of emotional status through bio-sensing and virtual affective agent modeling, and then proposes a novel system architecture for diagnosis and therapy of autistic spectrum disorder. The diagnosis and therapy system of autistic spectrum disorder is composed of bio-sensing module, virtual environment module with affective agents, and haptic interface module. The architecture proposed in this paper will enhance the objectivity to diagnose autism spectrum disorders, and enable continuous treatment in daily life.

• Investigative Magnetic Resonance Imaging
• /
• v.15 no.1
• /
• pp.32-40
• /
• 2011

Purpose : The purpose of this study was to investigate the microstructural changes according to aging on the thickness and signal intensity (SI) of the cortical gray matter (GM) and white matter (WM) on the T2-, fluid-attenuated inversion recovery (FLAIR) and T1-weighted MR images in normal subjects. Materials and Methods : The 10, 20, 30, 40, 50, 60, 70, 80 and 90 year age groups of men and women (each 10 individuals) who underwent routine brain MRI, including the T2-, FLAIR and T1-weighted images, were selected for this study. We measured the thickness and the SI of the cortical GM and WM at the postcentral gyrus, which has an even thickness at the level of centrum semiovale, on the axial scans and we calculated the mean values of the thickness ratio of the gray/white matter (TRGW) and the signal intensity ratio of the gray/white matter (SRGW), and we compared the ratios of each age group. Results : On the T2-weighted images, the TRGWs were 0.81 and 0.79 at the age of 10 and they were 0.73 and 0.71 at the age of 90 in the men and women, respectively. So, the GM thickness was decreased more than the WM thickness was with aging. On the FLAIR images, the TRGWs were 1.09 and 1.00 at the age of 10 and they were 1.11 and 0.95 at the age of 70 in the men and women, respectively. On the T1-weighted images, the TRGWs were 0.66 and 0.80 at the age of 10, and the ratio was changed to 0.90 and 0.78 at the age of 90 in the men and women, respectively. On the T2-weighted image, the SRGWs were 1.53 and 1.43 at the age of 10, and they were 1.23 and 1.27 at the age of 90 in the men and women, respectively. On the FLAIR images, the SRGWs were 1.23 and 1.25 at the age of 10 and they were 1.06 and 1.05 at the age of 90 in the men and women, respectively. On the T1-weighted images, the SRGWs were 0.86 and 0.85 at the age of 10, and they were 0.90 and 0.87 at the age of 90 in the men and women, respectively. Conclusion : We suggest that the age-related microstructural changes of the thickness and the SI of the cortical GM and WM on the T2-, FLAIR and T1-weighted images are unique, and so this knowledge will be helpful to differentiate neurodegenerative disease from normal aging of the brain.

• Clinical and Experimental Pediatrics
• /
• v.48 no.11
• /
• pp.1225-1231
• /
• 2005

Purpose : This study compares the first epileptic seizures between preterm and term-born children with periventricular leukomalacia and epilepsy. Methods : From 108 cases having lesions of high signal intensity around the ventricles in T2 weighted imaging of a brain magnetic resonance study, we selected 37 cases that showed epileptic seizures two times or more and divided them into the group of preterm-born(27 cases) and term-born children(10 cases). A retrospective study was made by comparing the two groups with regard to age, type of the first epileptic seizures, EEG findings and responsiveness to anticonvulsants. Results : The age of the first epileptic seizure was $22.2{\pm}18.3$ months in the preterm-born group and $26.9{\pm}21.1$ months in the term-born group(P=0.505). As for the first epileptic seizure, 11 out of the 27 cases in the preterm-born group had infantile spasms. Out of the 10 cases in the term-born group, 7 had complex partial seizures. In the preterm group, hypsarrhythmias were found in 11 cases, focal epileptiform discharges in 6 cases. In term-born group, focal epileptiform discharges were found in 5 cases but no epileptiform discharge was found in 3 cases. Intractable epilepsies were diagnosed in 6 cases and all of them belonged to the preterm-born group. Conclusion : More severe epilepsies such as infantile spasm and intractable epilepsies seem to be more common in preterm-born epileptic children with PVL as well as more severely abnormal EEG finding compared to term-born epileptic children.

• Journal of Institute of Control, Robotics and Systems
• /
• v.19 no.8
• /
• pp.725-730
• /
• 2013

This paper proposed a novel method for an analysis feature of an Electroencephalogram (EEG) at all channels simultaneously. In a BCI (Brain-Computer Interface) system, EEGs are used to control a machine or computer. The EEG signals were weak to noise and had low spatial resolution because they were acquired by a non-invasive method involving, attaching electrodes along with scalp. This made it difficult to analyze the whole channel of EEG signals. And the previous method could not analyze multiple stimuli, the result being that the BCI system could not react to multiple orders. The method proposed in this paper made it possible analyze multiple-stimuli by grouping the channels. We searched the groups making the largest correlation coefficient summation of every member of the group with a BHS (Binary Harmony Search) algorithm. Then we assumed the EEG signal could be written in linear summation of groups using concentration parameters. In order to verify this assumption, we performed a simulation of three subjects, 60 times per person. From the simulation, we could obtain the groups of EEG signals. We also established the types of stimulus from the concentration coefficient. Consequently, we concluded that the signal could be divided into several groups. Furthermore, we could analyze the EEG in a new way with concentration coefficients from the EEG channel grouping.

• The Journal of the Korea Contents Association
• /
• v.10 no.6
• /
• pp.27-34
• /
• 2010

This paper presents a methodology for classifying left and right motor imagery using a neural network with weighted fuzzy membership functions (NEWFM) and wavelet-based feature extraction. Wavelet coefficients are extracted from electroencephalogram(EEG) signal by wavelet transforms in the first step. In the second step, sixty numbers of initial features are extracted from wavelet coefficients by the frequency distribution and the amount of variability in frequency distribution. The distributed non-overlap area measurement method selects the minimized number of features by removing the worst input features one by one, and then minimized six numbers of features are selected with the highest performance result. The proposed methodology shows that accuracy rate is 86.43% with six numbers of features.

• Journal of IKEEE
• /
• v.23 no.4
• /
• pp.1273-1279
• /
• 2019

Though sleep polysomnography (PSG) is considered as a golden rule for medical diagnosis of sleep disorder, it is essential to find alternative diagnosis methods due to its cost and time constraints. Recently, as the popularity of wearable health devices, there are many research trials to replace conventional actigraphy to consumer grade devices. However, these devices are very limited in their use due to the accessibility of the data and algorithms. In this paper, we showed the predictive model for sleep stages classified by American Academy of Sleep Medicine (AASM) standard and we proposed the estimation of sleep cycle by comparing sensor data and power spectrums of δ wave and θ wave. The sleep stage prediction for 31 subjects showed an accuracy of 85.26%. Also, we showed the possibility that proposed algorithm can find the sleep cycle of REM sleep and NREM sleep.