• Journal of Sensor Science and Technology
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• v.12 no.3
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• pp.121-127
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• 2003

In this study, we carried out a study for implementation of the pre-amplifier and the digital signal processing part for the potable EEG biofeedback system. As we consider characteristics of the EEG signal, we designed the pre-amplifier to obtain the EEG signal to be reduced noise signal. Because the EEG signal include EOG, EMG, ECG signals etc, it is difficult to analyze of the EEG signal. Therefore, we developed DSP board and operation program which was embed the LMS adaptive filter algorithm and operate with the pre-amplifier in the real time. The simulation signal and pure EEG signal is used in the experiment. As the result, we confirmed good efficiency of developed system and possibility of application to the portable EEG biofeedback system.

• Journal of the Ergonomics Society of Korea
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• v.27 no.2
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• pp.9-14
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• 2008

Coherence can be used to evaluate the functional cortical connections between the motor cortex and muscle. This study is to find coherence between EEG (electroencephalogram) and EMG (electromyogram) evoked by movement of a hand. Seven healthy participants were asked to perform thirty repetitive movement of right hand for ten seconds with rest for ten seconds. Specific feature of EEG components has been extracted by ICA (independent component analysis) and coherence between EEG and EMG was analyzed from data measured EEG in five local areas around central part of head and EMG in flexer carpri radialis muscle during grabbing movement. Coherence between EEG and EMG was successfully obtained at 0.025 confidence limit during hand movement and showed significant difference between rest and movement at 13-18Hz.

• Journal of Digital Convergence
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• v.19 no.10
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• pp.295-301
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• 2021

Individual mental fatigue not only reduces cognitive ability and work performance, but also becomes a major factor in large and small accidents occurring in daily life. In this paper, a CNN model for EEG-based mental fatigue discrimination was proposed. To this end, EEG in the resting state and task state were collected and applied to the proposed CNN model, and then the model performance was analyzed. All subjects who participated in the experiment were right-handed male students attending university, with and average age of 25.5 years. Spectral analysis was performed on the measured EEG in each state, and the performance of the CNN model was compared and analyzed using the raw EEG, absolute power, and relative power as input data of the CNN model. As a result, the relative power of the occipital lobe position in the alpha band showed the best performance. The model accuracy is 85.6% for training data, 78.5% for validation, and 95.7% for test data. The proposed model can be applied to the development of an automated system for mental fatigue detection.

• Journal of the Ergonomics Society of Korea
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• v.26 no.2
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• pp.167-171
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• 2007

Brain computer interface is the technology of interface for next generation. Recently, user intention has been tried to be recognized for interfacing a computer. EEG plays important role in developing practical application in this area. Much research has focused on extracting EEG commander generated by human movement. ERD/ERS has generally accepted as important EEG parameters for prediction of human movement. However, There has been difference between initial movement indicated by ERD/ERS and real movement. Therefore, this study was to determine the time differences for brain interface by ERD/ERS. Five university students performed ten repetitive movements. ERD/ERS was determined according to movement execution and the significant pattern showed the difference between movement execution and movement indication of ERD/ERS.

• Proceedings of the KOSOMBE Conference
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• v.1995 no.11
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• pp.278-281
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• 1995

In this paper, we have developed EEG (electroencephalography) analyzer for monitoring the condition of brain in neurological surgery. This system is composed of EEG amplifier. personal-computer and BSP (Digital Signal Processor). By parallel processing of DSP, this system can analysis the power spectral density change of EEG in real-time and display the CSA(Compressed Spectral Array) and CDSA(Color Density Spectral array) of EEG. This system was tested by real EEG and showed the change of EEG.

• Sleep Medicine and Psychophysiology
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• v.8 no.1
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• pp.67-71
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• 2001

Independent Component Analysis (ICA) is a blind source separation method using unsupervised learning and mutual information theory created in the late eighties and developed in the nineties. It has already succeeded in separating eye movement artifacts from human scalp EEG recording. Several characteristic sleep waves such as sleep spindle, K-complex, and positive occipital sharp transient of sleep (POSTS) can be recorded during sleep EEG recording. They are used as stage determining factors of sleep staging and might be reflections of unknown neural sources during sleep. We applied the ICA method to sleep EEG for sleep waves separation. Eighteen channel scalp longitudinal bipolar montage was used for the EEG recording. With the sampling rate of 256Hz, digital EEG data were converted into 18 by n matrix which was used as a original data matrix X. Independent source matrix U (18 by n) was obtained by independent component analysis method ($U=W{\timex}X$, where W is an 18 by 18 matrix obtained by ICA procedures). ICA was applied to the original EEG containing sleep spindle, K-complex, and POSTS. Among the 18 independent components, those containing characteristic shape of sleep waves could be identified. Each independent component was reconstructed into original montage by the product of inverse matrix of W (inv(W)) and U. The reconstructed EEG might be a separation of sleep waves without other components of original EEG matrix X. This result (might) demonstrates that characteristic sleep waves may be separated from original EEG of unknown mixed neural origins by the Independent Component Analysis (ICA) method.

• Journal of the Korean Society for information Management
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• v.41 no.1
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• pp.261-282
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• 2024

This study proposed and evaluated electroencephalography (EEG)-based and eye-tracking-based methods to determine relevance by utilizing users' implicit relevance feedback while navigating content in a digital library. For this, EEG/eye-tracking experiments were conducted on 32 participants using video, image, and text data. To assess the usefulness of the proposed methods, deep learning-based artificial intelligence (AI) techniques were used as a competitive benchmark. The evaluation results showed that EEG component-based methods (av_P600 and f_P3b components) demonstrated high classification accuracy in selecting relevant videos and images (faces/emotions). In contrast, AI-based methods, specifically object recognition and natural language processing, showed high classification accuracy for selecting images (objects) and texts (newspaper articles). Finally, guidelines for implementing a digital library interface based on EEG, eye-tracking, and artificial intelligence technologies have been proposed. Specifically, a system model based on implicit relevance feedback has been presented. Moreover, to enhance classification accuracy, methods suitable for each media type have been suggested, including EEG-based, eye-tracking-based, and AI-based approaches.

• Annals of Clinical Neurophysiology
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• v.4 no.1
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• pp.28-33
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• 2002

Backgrounds and objective : EEG reflect dynamic changes of continuous neuronal activities by internal and external stimuli. The aim of this study is to quantify nonlinearly the local dynamic differences among EEG data corresponding to different states of brain. Methods : EEG was recorded from twelve healthy normal subjects(mean age, 29.7 years; 8 men and 4 women) using digital EEG machine. 18-channel EEG data were selected during eyes closed(EC), eyes open(EO), and mental arithmetic(MA) in each subject. Correlation dimension(D2) and largest Lyapunov exponent(LLE) were calculated from three states and average value was mapped 2 dimensionally and compared with each other. Results : The distribution of D2 was relatively symmetric and its value was higher in frontal than in parieto-occipital region during EC. These findings were reversed during EO. Bilateral centro-temporo-parietal region showed high D2 value in MA compared with those in EC, which was more prominent in left side. LLE was larger than zero in all state and showed significant differences among EC, EO and MA(p=0.000). Conclusion : These results suggest that nonlinear analysis of EEG can quantify dynamic state of brain.

• Annals of Clinical Neurophysiology
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• v.4 no.1
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• pp.21-27
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• 2002

Background : Spatial analysis of EEG is a phenomenal assessment and not so informative for phase space and dynamic aspect of EEG data. In contrast, nonlinear EEG analysis attempts to characterize the dynamics of neural networks in the brain. We have analyzed the features of EEG nonlinearly in subjects with white matter change on brain MRI and compared the results with cognitive function in each. Methods : Digital EEG data were taken for 30 seconds in 9 subjects with white matter degeneration and in 5 healthy normal controls without white matter change on MRI. Then we analyzed them nonlinearly to calculate the correlation dimension(D2) using the MATLAB software. The cognitive function was assessed by 3MS(modified mini-mental state examination). The severity of white matter change was assessed by Scheltens scale. Results : The mean D2 value of normal control was greater than that of white matter degeneration group. The D2s of some channels were correlative with 3MS and degree of white matter degeneration significantly. Conclusions : nonlinear analysis of EEG can be used as one of adjuvant functional studies for prediction of cognitive impairment in subjects with white matter degeneration and subcortical white matter change can be influential on cognitive function and correlation dimension of EEG.

• Journal of Korea Multimedia Society
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• v.21 no.2
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• pp.166-172
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• 2018

The distraction of the driver's attention causes as much traffic accidents as drowsiness driving. Yet though there have been many studies on drowsiness driving, research on distraction driving is insufficient. In this paper, we divide distraction of attention into visual distraction and cognitive distraction and analyze the EEG of subjects while viewing images of distracting situations. The results show that more information is received and processed when distractions occur. It is confirmed that the probability of accident increases when the driver receives overwhelming amount of information that he or she cannot concentrate on driving.