• Proceedings of the Korean Information Science Society Conference
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• 2011.06a
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• pp.469-472
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• 2011

신체 부위를 움직이는 상상을 할 때, 일반적으로 뇌의 감각 및 운동 피질 영역에서 특정 주파수 대역의 EEG(Electroencephalography) 신호의 세기가 감소하거나 증가하는 ERD(Event-Related Desynchronization)/ERS(Event-Related Synchronization) 현상이 발생한다. 하지만 ERD/ERS는 현상은 피험자에 의존적이고 매시도마다 큰 차이를 보인다. 이러한 문제를 해결하기 위해, 본 논문에서 각 시간-주파수 공간에 대하여 서로 다른 공간 필터를 구성하는 비 동질(non-homogeneous) 공간 필터 최적화 방법을 제안한다. EEG 신호는 시간에 대하여 비정상적(non-stationary) 특징을 가지기 때문에 제안하는 방법과 같이 시간에 따라 변화하는 ERD/ERS 특징을 반영하여 공간적 특징을 추출하는 방법은 시간에 대한 변화를 고려하지 않은 기존의 방법보다 우수한 성능을 보인다. 본 논문에서는 International BCI Competition IV에서 제공하는 4가지 동작 상상(왼손, 오른손, 발, 혀)에 대한 EEG 신호 데이터를 사용하여 동작 상상 분류 실험을 하고 이 결과를 기존의 타 방법들과 비교 분석하였다. 실험 결과, 피험자에 따라 서로 다른 시간-주파수 특징이 추출됨을 확인하였고, 최적화된 공간 필터들이 시간에 따라 변화하는 것을 확인하였다. 또한 이러한 특징을 이용하여 분류를 수행하였을 때, 더욱 우수한 분류 결과를 보임을 확인하였다.

• Proceedings of the Korea Contents Association Conference
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• 2005.05a
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• pp.422-428
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• 2005

In this paper, for maintaining the presentation quality of multimedia contents, we define the several events that can happen in showing when handle the multimedia contents. Also we classified these events in 4 types based on each characteristic. Furthermore we design the event model which can manage synchronization of presentation by control and managing the events. Also we propose an idea that maintain the quality of presentation service by interaction between each module and threads related in progress of show. Especially, in this research, we focus the active process for the user's participant and the unpredictable state variation of IMPSs(Interacitve Multimedia Presentation Systems).

• Proceedings of the Korean Society for Cognitive Science Conference
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• 2010.05a
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• pp.37-41
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• 2010

The previous investigations of electroencephalogram (EEG) activity in the memory retrieval tasks demonstrated that event-related potentials (ERP) during recollection showed different durations and the peak levels from those without recollection. However, it has been unknown that recollection in memory retrieval also modulates high-frequency brain rhythms as well as establishes large-scale synchronization across different cortical areas. In this study, we examined the spectral components of the EEG signals, especially the gamma bands (20-80Hz), measured during the memory retrieval tasks. Specifically, we focused on two major spectral components: first, we evaluated the temporal patterns of the power spectral density before and after the onset of the memory retrieval task; second, we estimated phase synchrony between all possible pairs of EEG channels to evaluate large-scale synchronization. Fourteen healthy subjects performed the memory retrieval task in the virtual reality environment where they selected whether or not t he present item was seen in the previous training period. When the subjects viewed the unseen items, the middle gamma power (40-60Hz) appeared to increase 200-500ms after stimulus onset while the low gamma power (20Hz) was suppressed all the way through the post-stimulus period 150ms after onset. The degree of phase synchronization in this low gamma level, however, increased when the subjects fetched the item from memory. This suggests that phase synchrony analysis might reveal different aspects of the memory retrieval process than the gamma power, providing additional information to the inference on the brain dynamics during memory retrieval.

• Journal of Biomedical Engineering Research
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• v.39 no.2
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• pp.94-102
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• 2018

It is known that gamma-band activity (GBA) and phase synchrony (GBPS) are induced by emotional visual stimuli. However, the characteristics of GBA and GBPS according to different emotional states have not been identified. The purpose of this study is to investigate the changes in gamma-band neuronal synchronization induced by positive and negative emotional visual stimuli using electroencephalograms (EEGs). Thirteen healthy male subjects have participated in the experiment. The induced spectral power in gamma-band was the highest for negative stimuli, and the lowest for neutral stimuli in 300-2,000 ms after the stimulus onset. The inter-regional phase synchronization in gamma-band was increased in 500-2,000 ms, mainly between the bilateral frontal regions and the parieto-occipital regions. Larger number of significant connections were found by negative stimuli compared to positive ones. Judging from temporal and spatial characteristics of the gamma-band activity and phase synchrony increases, the results may imply that affective visual stimuli cause stronger memory encoding than non-emotional stimuli, and this effect is more significant for negative emotional stimuli than positive ones.

• Korean Journal of Biological Psychiatry
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• v.8 no.2
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• pp.251-257
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• 2001

Backgrounds : Gamma band oscillatory activity is considered to be related to cognitive functions and illustrates that the concept of event-related oscillations bridges the gap between single neurons and neural assemblies. An event-related gamma oscillation is the time-locked responses of specific frequency, and can be identified by computing the amplitude frequency characteristics of the averaged event-related potentials(ERPs) after stimulation. Objectives : We purposed to present experimental paradigm to investigate ${\gamma}$-band oscillation activities from the recording of ERPs by using auditory oddball paradigm and investigate the difference of ${\gamma}$-band activity between schizophrenia and normal controls. Methods : The ERPs resulting from auditory stimuli with oddball paradigm in a group of schizophrenics(n=11), and also a group of age-, sex-, and handedness matched normal controls, were recorded by 128 channel EEG. The ${\gamma}$-band oscillatory activities were calculated by using time-frequency wavelet decomposition of the signal between 20 and 80Hz. The ${\gamma}$-band oscillatory activities of both groups were compared by t-test. Results : The ${\gamma}$-band oscillatory of the leads Fz, Cz, and Pz of both groups were represented well in the time-frequency maps. Significant increases of the ${\gamma}$-band activity in normal controls compared with schizophrenics were observed around 160 msec, 350 msec, and 800 msec after stimulation. Conclusions : Our results suggested that the increment in ${\gamma}$-band oscillatory activity during cognitive operations and decreased ${\gamma}$-band activity in schizophrenics may be associated with the cognitive dysfunctions and the pathophysiology of the schizophrenia.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.57 no.2
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• pp.109-114
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• 2008

In this paper, a dynamic learning controller for robot manipulator is implemented using real-time operating system with capabilities of multitasking, intertask communication and synchronization, event-driven, priority-driven scheduling, real-time clock control, etc. The controller hardware system with VME bus and related devices is developed and applied to implement a dynamic learning control scheme for robot manipulator. Real-time performance of the proposed dynamic learning controller is tested and evaluated for tracking of the desired trajectory and compared with the conventional servo controller.

• Journal of Biomedical Engineering Research
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• v.33 no.1
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• pp.15-24
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• 2012

Classification of human thought is an emerging research field that may allow us to understand human brain functions and further develop advanced brain-computer interface (BCI) systems. In the present study, we introduce a new approach to classify various mental states from noninvasive electrophysiological recordings of human brain activity. We utilized the full spatial and spectral information contained in the electroencephalography (EEG) signals recorded while a subject is performing a specific mental task. For this, the EEG data were converted into a 2D spatiospectral pattern map, of which each element was filled with 1, 0, and -1 reflecting the degrees of event-related synchronization (ERS) and event-related desynchronization (ERD). We evaluated the similarity between a current (input) 2D pattern map and the template pattern maps (database), by taking the inner-product of pattern matrices. Then, the current 2D pattern map was assigned to a class that demonstrated the highest similarity value. For the verification of our approach, eight participants took part in the present study; their EEG data were recorded while they performed four different cognitive imagery tasks. Consistent ERS/ERD patterns were observed more frequently between trials in the same class than those in different classes, indicating that these spatiospectral pattern maps could be used to classify different mental states. The classification accuracy was evaluated for each participant from both the proposed approach and a conventional mental state classification method based on the inter-hemispheric spectral power asymmetry, using the leave-one-out cross-validation (LOOCV). An average accuracy of 68.13% (${\pm}9.64%$) was attained for the proposed method; whereas an average accuracy of 57% (${\pm}5.68%$) was attained for the conventional method (significance was assessed by the one-tail paired $t$-test, $p$ < 0.01), showing that the proposed simple classification approach might be one of the promising methods in discriminating various mental states.

• 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.

• Korean Journal of Biological Psychiatry
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• v.20 no.4
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• pp.119-128
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• 2013

Objectives Memory impairment is a very important mental health issue for elderly and adults. Mild cognitive impairment (MCI) is a prodromal stage of Alzheimer's disease (AD). Early detection of the prodromal stage of patients with AD is an important topic of interest for both mental health clinicians and policy makers. Methods Electroencephalograpgy (EEG) has been used as a possible biological marker for patients with MCI, and AD. In this review, we will summarize the clinical implications of EEG and ERP as a biological marker for AD and MCI. Results EEG power density, functional coupling, spectral coherence, synchronization, and connectivity were analyzed and proved their clinical efficacy in patients with the prodromal stage of AD. Serial studies on late event-related potentials (ERPs) were also conducted in MCI patients as well as healthy elders. Even though these EEG and ERP studies have some limitations for their design and method, their clinical implications are increasing rapidly. Conclusion EEG and ERP can be used as biological markers of AD and MCI. Also they can be used as useful tools for early detection of AD and MCI patients. They are useful and sensitive research tools for AD and MCI patients. However, some problems remain to be solved until they can be practical measures in clinical setting.

• Journal of Biomedical Engineering Research
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• v.43 no.6
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• pp.375-381
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• 2022

Brain-computer interface (BCI) is a technology that allows users with motor disturbance to control machines by brainwaves without a physical controller. Motor imagery (MI)-BCI is one of the popular BCI techniques, but it needs a long calibration time for users to perform a mental task that causes high fatigue to the users. MI is reported as showing a similar neural mechanism as motor execution (ME) and motor observation (MO). However, integrative investigations of these three tasks are rarely conducted. In this study, we propose a new paradigm that incorporates three tasks (MI, ME, and MO) and conducted a comparative analysis. For this study, we collected Electroencephalograms (EEG) of motor imagery/execution/observation from 28 healthy subjects and investigated alpha event-related (de)synchronization (ERD/ERS) and classification accuracy (left vs. right motor tasks). As result, we observed ERD and ERS in MI, MO and ME although the timing is different across tasks. In addition, the MI showed strong ERD on the contralateral hemisphere, while the MO showed strong ERD on the ipsilateral side. In the classification analysis using a Riemannian geometry-based classifier, we obtained classification accuracies as MO (66.34%), MI (60.06%) and ME (58.57%). We conclude that there are similarities and differences in fundamental neural mechanisms across the three motor tasks and that these results could be used to advance the current MI-BCI further by incorporating data from ME and MO.