• Annals of Clinical Neurophysiology
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• v.1 no.2
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• pp.154-159
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• 1999

Magnetoencephalography (MEG), the measurement of magnetic fields produced by neuronal current associated with normal and pathologic brain activities, is a totally noninvasive method for localizing functional regions of the brain. During the past several years, many clinical research centers are working to expand various fundamental functional brain regions, which can be easily localized, as well as to characterize magnetic abnormalities which accompany a wide variety of cerebral disease. At present, MEG is used in a number of clinical centers throughout the world for the presurgical functional localization of eloquent cortex, and for the non-invasive localization of epileptiform activity. And also, non-invasiveness means that it can be used for screening and repetitive follow-up measurement without concern for adverse effects. As procedures for activating various functional brain regions are standardized, and as the effects of specific cerebral diseases on the MEG are carefully documented in controlled studies, the number of routine neurological applications for MEG will increase significantly. In this paper, the basic principles of MEG are reviewed briefly with its clinical application to neurologic disease.

• International Journal of Control, Automation, and Systems
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• v.1 no.3
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• pp.385-389
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• 2003

In this paper, an improved technique for multiresolutive reconstruction of magnetoencephalography (MEG) source distribution is proposed. Using the proposed technique, focal solution with higher energy density can be reconstructed. Moreover, the proposed approach is very easy to implement compared to conventional ones. The usefulness of the proposed technique is verified by the application to a real brain model.

• KIEE International Transaction on Systems and Control
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• v.2D no.2
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• pp.53-58
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• 2002

In this paper, various aspects in magnetoencephalography (MEG) source localization are studied. To minimize the errors in experimental data, an approximation technique using a polynomial function is proposed. The simulation shows that the proposed technique yields more accurate results. To improve the convergence characteristics in the optimization algorithm, a hybrid algorithm of evolution strategy and sensitivity analysis is applied to the neuromagnetic inverse problem. The effectiveness of the hybrid algorithm is verified by comparison with conventional algorithms. In addition, an artificial neural network (ANN) is applied to find an initial source location quickly and accurately. The simulation indicates that the proposed technique yields more accurate results effectively.

• 한국초전도학회:학술대회논문집
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• 2008.08a
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• pp.53-53
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• 2008

• Clinical and Experimental Pediatrics
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• v.56 no.10
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• pp.431-438
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• 2013

Magnetoencephalography (MEG) records the magnetic field generated by electrical activity of cortical neurons. The signal is not distorted or attenuated, and it is contactless recording that can be performed comfortably even for longer than an hour. It has excellent and decent temporal resolution, especially when it is combined with the patient's own brain magnetic resonance imaging (magnetic source imaging). Data of MEG and electroencephalography are not mutually exclusive and it is recorded simultaneously and interpreted together. MEG has been shown to be useful in detecting the irritative zone in both lesional and nonlesional epilepsy surgery. It has provided valuable and additive information regarding the lesion that should be resected in epilepsy surgery. Better outcomes in epilepsy surgery were related to the localization of the irritative zone with MEG. The value of MEG in epilepsy surgery is recruiting more patients to epilepsy surgery and providing critical information for surgical planning. MEG cortical mapping is helpful in younger pediatric patients, especially when the epileptogenic zone is close to the eloquent cortex. MEG is also used in both basic and clinical research of epilepsy other than surgery. MEG is a valuable diagnostic modality for diagnosis and treatment, as well as research in epilepsy.

• Journal of Korean Neurosurgical Society
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• v.52 no.5
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• pp.466-471
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• 2012

Objective : The aim of this study was to devise an objective clustering method for magnetoencephalography (MEG) interictal spike sources, and to identify the prognostic value of the new clustering method in adult epilepsy patients with cortical dysplasia (CD). Methods : We retrospectively analyzed 25 adult patients with histologically proven CD, who underwent MEG examination and surgical resection for intractable epilepsy. The mean postoperative follow-up period was 3.1 years. A hierarchical clustering method was adopted for MEG interictal spike source clustering. Clustered sources were then tested for their prognostic value toward surgical outcome. Results : Postoperative seizure outcome was Engel class I in 6 (24%), class II in 3 (12%), class III in 12 (48%), and class IV in 4 (16%) patients. With respect to MEG spike clustering, 12 of 25 (48%) patients showed 1 cluster, 2 (8%) showed 2 or more clusters within the same lobe, 10 (40%) showed 2 or more clusters in a different lobe, and 1 (4%) patient had only scattered spikes with no clustering. Patients who showed focal clustering achieved better surgical outcome than distributed cases (p=0.017). Conclusion : This is the first study that introduces an objective method to classify the distribution of MEG interictal spike sources. By using a hierarchical clustering method, we found that the presence of focal clustered spikes predicts a better postoperative outcome in epilepsy patients with CD.

• The Korean Journal of Applied Statistics
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• v.35 no.1
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• pp.161-175
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• 2022

Oscillatory magnetic fields produced in the brain due to neuronal activity can be measured by the sensor. Magnetoencephalography (MEG) is a non-invasive technique to record such neuronal activity due to excellent temporal and fair amount of spatial resolution, which gives information about the brain's functional activity. Potential utilization of high spatial resolution in MEG is likely to provide information related to in-depth brain functioning and underlying factors responsible for changes in neuronal waves in some diseases under resting state or task state. This review is a comprehensive report to introduce statistical models from MEG data including graphical network modelling. It is also meaningful to note that statisticians should play an important role in the brain science field.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.10
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• pp.606-610
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• 2003

Noise subtraction using reference channel data has been used to improve signal-to-noise ratio in magnetoencephalography. In this paper, an adaptive noise subtraction model is proposed and parameters for the model are optimized. A criterion to determine an optimal update period for the filter coefficients is proposed based on the ratio of peak amplitude of evoked field (N100m) divided by the output standard deviation. Experiments are carried out using a 40 channel MEG system. From the experiments, the proposed noise subtraction method shows superior performances over existing non-adaptive methods. Two-dimensional topographic map is shown for a diagnosis with a cubic spline interpolation.

• 한국초전도학회:학술대회논문집
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• v.12
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• pp.45-45
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• 2002

• Progress in Superconductivity
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• v.4 no.2
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• pp.114-120
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• 2003

We employed a method eliminating a temporally partial principal component (PC) of multichannel-recorded neuromagnetic fields for excluding spatially correlated noises from event-evoked signals. The noises in magnetoencephalography (MEG) are considered to be mainly spontaneous neuromagnetic fields which are spatially correlated. In conventional MEG experiments, the amplitude of the spontaneous neuromagnetic field is much lager than that of the evoked signal and the synchronized characteristics of the correlated rhythmic noise makes it possible for us to extract the correlation noises from the evoked signal by means of the general PC analysis. However, the whole-time PC of the fields still contains a little projection component of the evoked signal and the elimination of the PC results in the distortion of the evoked signal. Especially, the distortion will not be negligible when the amplitude of the evoked signal is relatively large or when the evoked signals have a spatially-asymmetrical distribution which does not cancel out the corresponding elements of the covariance matrix. In the period of prestimulus, there are only the spontaneous fields and we can find the pure noise PC that is not including the evoked signal. Besides that, we propose a method, called the extended temporal decorrelation method (ETDM), to suppress the distortion of the noise PC from remanent evoked signal components. In this study, we applied the Partial Principal component elimination method (PPCE) and ETDM to simulated signals and the auditory evoked signals that had been obtained with our homemade 37-channel magnetometer-based SQUID system. We demonstrate here that PPCE and ETDM reduce the number of epochs required in averaging to about half of that required in conventional averaging.