• Clinical and Experimental Pediatrics
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• v.54 no.1
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• pp.22-28
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• 2011

Purpose: To investigate the clinical characteristics of late-onset epilepsy combined with autism spectrum disorder (ASD), and the relationship between certain types of electroencephalography (EEG) abnormalities in ASD and associated neuropsychological problems. Methods: Thirty patients diagnosed with ASD in early childhood and later developed clinical seizures were reviewed retrospectively. First, the clinical characteristics, language and behavioral regression, and EEG findings of these late-onset epilepsy patients with ASD were investigated. The patients were then classified into 2 groups according to the severity of the EEG abnormalities in the background rhythm and paroxysmal discharges. In the severe group, EEG showed persistent asymmetry, slow and disorganized background rhythms, and continuous sharp and slow waves during slow sleep (CSWS). Results: Between the two groups, there was no statistically significant difference in mean age (P=0.259), age of epilepsy diagnosis (P=0.237), associated family history (P=0.074), and positive abnormal magnetic resonance image (MRI) findings (P=0.084). The severe EEG group tended to have more neuropsychological problems (P=0.074). The severe group statistically showed more electrographic seizures in EEG (P=0.000). Rett syndrome was correlated with more severe EEG abnormalities (P=0.002). Although formal cognitive function tests were not performed, the parents reported an improvement in neuropsychological function on the follow up checkup according to a parent's questionnaire. Conclusion: Although some ASD patients with late-onset epilepsy showed severe EEG abnormalities, including CSWS, they generally showed an improvement in EEG and clinical symptoms in the longterm follow up. In addition, severe EEG abnormalities tended to be related to the neuropsychological function.

• Physical Therapy Rehabilitation Science
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• v.6 no.4
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• pp.164-169
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• 2017

Objective: This research investigated the effects of simple visual imagery and mental rotation imagery on neural activity of adults who are at high risk of smart phone addiction by measuring their electroencephalography (EEG). Design: Cross-sectional study. Methods: Thirty people with a high risk of smart phone addiction was selected and then were evaluated for their neural activation patterns using EEG after reminding them about simple visual imagery and mental rotation imagery. A simple visual image was applied for 20 seconds using a smartphone. This was followed by a resting period of 20 seconds. Mental rotation imagery was applied for 20 seconds. During mental rotation imagery, the rotational angle was selected at random. We compared activation patterns according to the analyzed EEG with hemisphere reminding them about imagery. Results: On the EEG, theta rhythm from the left hemisphere parietal area increased when the subjects were reminded of mental rotation imagery, and sensorimotor rhythm from close to the left hemisphere area increased when the subjects were reminded of simple visual imagery. Conclusions: Neural activation from the left hemisphere occurs for motor imagery in adults who are at high risk of smart phone addiction. These results identify a neural mechanism of adults who a have high risk of smart phone addiction, which may provide contribute to the development of motor rehabilitation for smartphone users.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.1
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• pp.12-18
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• 2013

Motor imagery classification in electroencephalography (EEG)-based brain-computer interface (BCI) systems is an important research area. To simplify the complexity of the classification, selected power bands and electrode channels have been widely used to extract and select features from raw EEG signals, but there is still a loss in classification accuracy in the state-of- the-art approaches. To solve this problem, we propose a discriminative feature extraction algorithm based on power bands with principle component analysis (PCA). First, the raw EEG signals from the motor cortex area were filtered using a bandpass filter with ${\mu}$ and ${\beta}$ bands. This research considered the power bands within a 0.4 second epoch to select the optimal feature space region. Next, the total feature dimensions were reduced by PCA and transformed into a final feature vector set. The selected features were classified by applying a support vector machine (SVM). The proposed method was compared with a state-of-art power band feature and shown to improve classification accuracy.

• ETRI Journal
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• v.42 no.2
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• pp.217-229
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• 2020

Long-term electroencephalography (EEG) monitoring is time-consuming, and requires experts to interpret EEG signals to detect seizures in patients. In this paper, we propose a novel automated method called adaptive slope of wavelet coefficient counts over various thresholds (ASCOT) to classify patient episodes as seizure waveforms. ASCOT involves extracting the feature matrix by calculating the mean slope of wavelet coefficient counts over various thresholds in each frequency subband. We validated our method using our own database and a public database to avoid overtuning. The experimental results show that the proposed method achieved a reliable and promising accuracy in both our own database (98.93%) and the public database (99.78%). Finally, we evaluated the performance of the method considering various window sizes. In conclusion, the proposed method achieved a reliable seizure detection performance with a short-term window size. Therefore, our method can be utilized to interpret long-term EEG results and detect momentary seizure waveforms in diagnostic systems.

• ETRI Journal
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• v.44 no.4
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• pp.672-685
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• 2022

This article proposes a subject-independent application of brain-computer interfacing (BCI). A 32-channel Electroencephalography (EEG) device is used to measure imagined speech (SI) of four words (sos, stop, medicine, washroom) and one phrase (come-here) across 13 subjects. A deep long short-term memory (LSTM) network has been adopted to recognize the above signals in seven EEG frequency bands individually in nine major regions of the brain. The results show a maximum accuracy of 73.56% and a network prediction time (NPT) of 0.14 s which are superior to other state-of-the-art techniques in the literature. Our analysis reveals that the alpha band can recognize SI better than other EEG frequencies. To reinforce our findings, the above work has been compared by models based on the gated recurrent unit (GRU), convolutional neural network (CNN), and six conventional classifiers. The results show that the LSTM model has 46.86% more average accuracy in the alpha band and 74.54% less average NPT than CNN. The maximum accuracy of GRU was 8.34% less than the LSTM network. Deep networks performed better than traditional classifiers.

• Korean Journal of Clinical Laboratory Science
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• v.56 no.1
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• pp.85-88
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• 2024

Electroencephalography (EEG) is a test that diagnoses epilepsy and measures brain function. During EEG, the space between the electrode and the skin is filled with a conductive paste to reduce the impedance between the electrode and the scalp, which helps measure the EEG signals. This study compared the artifacts of the two representative conductive pastes (Ten20 and Elefix). The artifacts, noise, and satisfaction were surveyed after using the two conductive pastes. The two conductive pastes had similar artifacts and noise, but the survey results showed that the Elefix conductive paste had better satisfaction and adhesion. This result may be explained by the imprinting effect according to the experience of using the Elefix conductive paste first in the EEG class. Hence, further research is needed.

• Journal of Korea Society of Industrial Information Systems
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• v.22 no.1
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• pp.41-51
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• 2017

This Paper Describes a Method for the Evaluation of Sleep Apnea, Namely, the Peak Signal-to-noise ratio (PSNR) of Wavelet Transformed Electroencephalography (EEG) Data. The Purpose of this Study was to Investigate EEG Properties with Regard to Differences between Sleep Spindles and K-complexes and to Characterize Obstructive Sleep Apnea According to Sleep Stage. We Examined Non-REM and REM Sleep in 20 Patients with OSA and Established a New Approach for Detecting Sleep Apnea Base on EEG Frequency Changes According to Sleep Stage During Sleep Apnea Events. For Frequency Bands Corresponding to A3 Decomposition with a Sampling Applied to the KC and the Sleep Spindle Signal. In this Paper, the KC and Sleep Spindle are Ccalculated using MSE and PSNR for 4 Types of Mother Wavelets. Wavelet Transform Coefficients Were Obtained Around Sleep Spindles in Order to Identify the Frequency Information that Changed During Obstructive Sleep Apnea. We also Investigated Whether Quantification Analysis of EEG During Sleep Apnea is Valuable for Analyzing Sleep Spindles and The K-complexes in Patients. First, Decomposition of the EEG Signal from Feature Data was Carried out using 4 Different Types of Wavelets, Namely, Daubechies 3, Symlet 4, Biorthogonal 2.8, and Coiflet 3. We Compared the PSNR Accuracy for Each Wavelet Function and Found that Mother Wavelets Daubechies 3 and Biorthogonal 2.8 Surpassed the other Wavelet Functions in Performance. We have Attempted to Improve the Computing Efficiency as it Selects the most Suitable Wavelet Function that can be used for Sleep Spindle, K-complex Signal Processing Efficiently and Accurate Decision with Lesser Computational Time.

• Journal of Biomedical Engineering Research
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• v.26 no.4
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• pp.223-230
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• 2005

This paper presents a new approach to investigate spatial correlation between independent components of brain alpha activity in functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). To avoid potential problems of simultaneous fMRI and EEG acquisitions in imaging pure alpha activity, data from each modality were acquired separately under a 'three conditions' setup where one of the conditions involved closing eyes and relaxing, thus making it conducive to generation of alpha activity. The other two conditions -- eyes open in a lighted room or engaged in a mental arithmetic task, were designed to attenuate alpha activity. Using a Mixture Density Independent Component Analysis (MD-ICA) that incorporates flexible non-linearity functions into the conventional ICA framework, we could identify the spatiotemporal components of fMRI activations and EEG activities associated with the alpha rhythm. Then, the sources of the individual EEG alpha activity component were localized by a Maximum Entropy (ME) method that is specially designed to find the most probable dipole distribution minimizing the localization error in sense of LMSE. The resulting active dipoles were spatially transformed to 3D MRls of the subject and compared to fMRI alpha activity maps. A good spatial correlation was found in the spatial distribution of alpha sources derived independently from fMRI and EEG, suggesting the proposed method can localize the cortical areas responsible for generating alpha activity successfully in either fMRI or EEG. Finally a functional connectivity analysis was applied to show that alpha activity sources of both modalities were also functionally connected to each other, implying that they are involved in performing a common function: 'the generation of alpha rhythms'.

• Korean Journal of Clinical Laboratory Science
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• v.51 no.4
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• pp.397-405
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• 2019

Quantitative electroencephalography (QEEG) has been widely used in research and clinical fields. QEEG has been widely used to objectively document cerebral changes for the purpose of identifying the electrophysiological biomarkers across various clinical symptoms and for the stimulation of specific cortical regions associated with cognitive function. In electroencephalography (EEG), the difference in quantitative and qualitative analyses is discriminated not by its measurement methods and relevant clinical or research environments, but by its analysis methods. When performing a qualitative analysis, it is possible for a medical technologist or experienced researchers to read the EEG waveforms to exclude artifacts. However, the quantitative analysis is still based on mathematical modeling, and all EEG data are included for the analysis, leading the results to be affected by unexpected artifacts. In the hospital setting, the case that the medical technologists in charge of the EEG test perform academic research has been little reported, compared to other clinical physiological measurement-based research. This is because there are few laboratories specialized in clinical physiological research. In this respect, this study is expected to be utilized as a basic reference material for medical technologists, students, and academic researchers, all of whom would like to conduct a quantitative analysis.

• Sleep Medicine and Psychophysiology
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• v.2 no.1
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• pp.31-43
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• 1995

Recently, the methods that measure and analyze brain electrical activity quantitatively have been available with the rapid development of computer technology. The quantitative electroencephalography(QEEG) is a method of computer-assisted analyzing brain electrical activity. The QEEG allows for a more sensitive, precise and reproducible examination of EEG data than that can be accomplished by conventional EEG. It is possible to compare various EEG parameters each other by using QEEG. Neurometrics, a kind of the quantitative EEG. is to compare EEG characteristics of the patient with normative data to determine in what way the patient's EEG deviates from normality and to discriminate among psychiatric disorders. Nowadays, QEEG is far superior to conventional EEG in its detection of abnormality and in its usefulness in psychiatric differential diagnosis. The abnormal findings of QEEG in various psychiatric disorders are also discussed.