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

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.749-755
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• 2009

BCI (Brain-Computer Interface) is a way to control computer by using the human brain waves. As the hardware using brain wave technologies has developed, former expensive and big sized brain wave measuring devices have recently become much smaller and cheaper in their prices, making it possible for the individuals to buy them. This predicts them to be applied in various fields of multimedia industry. This thesis is to give an insight into whether the BCI device can be used as a new gaming device approaching it in a game designing point of view. At first, we propose game play elements that can effectively utilizing the BCI devices and produce a game prototype adopting the BCI device based on such game play elements. Next, we verify it with statistical data analysis to show that the prototype adopting the BCI device gives more clear and efficient controls in its game play than a game of only adopting keyboard & mouse devices. The results will give a guideline for effective game design methodology for making BCI based games.

• Journal of Korea Game Society
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• v.9 no.6
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• pp.31-44
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• 2009

BCI (Brain-Computer Interface) is a way to control computer by using the human brain waves. As the hardware using brain wave technologies has developed, former expensive and big sized brain wave measuring devices have recently become much smaller and cheaper in their prices, making it possible for the individuals to buy them. This predicts them to be applied in various fields of multimedia industry. This thesis is to give an insight into whether the BCI device can be used as a new gaming device approaching it in a game designing point of view. At first, we proposed game play elements that can effectively utilizing the BCI devices, systematize, and produced a game prototype adopting the BCI device based on such game play elements. Next, we verified it with statistical data analysis to show that the prototype adopting the BCI device gives more clear and efficient controls in its game play than a game of only adopting keyboard & mouse devices and analysis verified that BCI-based game play elements provide users with a more intuitive and interesting experience than traditional non-BcI-based game play elements. The results will give a guideline for effective game design methodology for making BCI based games.

• 한국HCI학회:학술대회논문집
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• 2007.02a
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• pp.780-783
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• 2007

A Many studies have been made on the prediction of human voluntary movement intention in real-time based on invasive or non-invasive methods to help severely motor-disabled persons by offering some abilities of motor controls and communications. In the present study, we have developed an internet game driven by and/or linked to a brain-computer interface (BCI) system. Activities of two single neuronal units recorded from either hippocampus or prefrontal cortex of SD rats were used in real time to control two-dimensional movements of a robot, or a game object.

• Journal of National Security and Military Science
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• s.1
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• pp.249-267
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• 2003

EEG is an electrical signal, which occurs during information processing in the brain. These EEG signals have been used clinically, but nowadays we are mainly studying Brain-Computer Interface (BCI) such as interfacing with a computer through the EEG, controlling the machine through the EEG. The ultimate purpose of BCI study is specifying the EEG at various mental states so as to control the computer and machine. This research makes the controlling system of directions with the artifact that are generated from the subject's will, for the purpose of controlling the machine correctly and reliably. We made the system like this. First, we select the particular artifact among the EEG mixed with artifact, then, recognize and classify the signals' pattern, then, change the signals to general signals that can be used by the controlling system of directions.

• 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.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.9
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• pp.838-842
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• 2007

A brain-computer interface(BCI) system is a communication channel which transforms a subject's thought process into command signals to control various devices. These systems use electroencephalographic signals or the neuronal activity of many single neurons. The presented study deals with an efficient analysis method of neuronal signals from a BCI System using an independent component analysis(ICA) algorithm. The BCI system was implemented to generate event signals coding movement information of the subject. To apply the ICA algorithm, we obtained the perievent histograms of neuronal signals recorded from prefrontal cortex(PFC) region during target-to-goal(TG) task trials in the BCI system. The neuronal signals were then smoothed over 5ms intervals by low-pass filtering. The matrix of smoothed signals was then rearranged such that each signal was represented as a column and each bin as a row. Each column was also normalized to have a unit variance. As a result, we verified that different patterns of the neuronal signals are dependent on the target position and predefined event signals.

• Journal of Biomedical Engineering Research
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• v.41 no.1
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• pp.22-27
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• 2020

Auditory stimuli are commonly used in various electroencephalogram experiments, also in EEG-based brain-computer interface systems. However, using conventional earphones that blocks the ear canal attenuates or even blocks external environmental sound which might cause loss of crucial information from surroundings. Instead, bone-conductive earphones are able to deliver sound through vibration without blocking the ear canal. To investigate the feasibility of the bone-conductive earphones for auditory-stimuli based experiments, we compared N100 event-related potential features as well the event-related spectral perturbation and inter-trial coherence of auditory steady-state response between conventional and bone-conductive earphones. The results showed no significant differences between bone conduction and conventional earphones regardless of distinct sound pressures. This result shows that bone conductive earphones can be used for auditory experiments when the environmental sound is crucial to the user.

• Journal of the Korean Society of Physical Medicine
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• v.13 no.4
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• pp.59-65
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• 2018

PURPOSE: The purpose of the present study is to investigate the changes in the cognitive function of elderly dementia patients residing in a residential care facility, following six weeks of brain-computer interface (BCI)-based cognitive training and to determine whether BCI-based cognitive training effectively improves their cognitive functions. METHODS: Thirty subjects diagnosed with dementia were randomly assigned to either the experimental or control group. Pre- and post-test cognitive function assessments were conducted using the mini mental state examination-Korean (MMSE-K) and Korean-dementia rating scale (K-DRS). The experimental group received BCI-based cognitive training, which consisted of games such as flying a ball and exploding a bomb, while the control group participated in music listening activities and National Health Gymnastics. Both groups engaged in a total of 18 sessions (3 times per week for 6 weeks, for 40 minutes per session). RESULTS: After 6 weeks of intervention, the experimental group had significantly increased MMSE-K scores ($19.53{\pm}1.30$ to $22.20{\pm}1.15$; p<.0011) and total K-DRS scores ($87.20{\pm}4.16$ to $99.33{\pm}1.15$; p<.0011). In addition, the experimental group showed greater cognitive improvements than the control group. CONCLUSION: The results of this study suggest that BCI-based cognitive training is a positive intervention tool for improving the cognitive function of dementia patients.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10b
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• pp.739-741
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• 2003

Electroencephalogram (EEG)-based brain computer interface (BCI) provides a new communication channel between human brain and computer. EEG is very noisy data and contains artifacts, thus the extraction of features that are robust to noise and artifacts is important. In this paper we present a method with employ both independent component analysis (ICA) and oriented principal component analysis (OPCA) for artifact-robust feature extraction.