• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제12권3호
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• pp.191-199
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• 2008

This paper proposes a new method for the inverse problem of the three-dimensional reconstruction of the electrical activity of the brain from electroencephalography (EEG). Compared to conventional direct methods using additional parameters, the proposed approach solves the EEG inverse problem iteratively without any parameter. We describe the Lagrangian corresponding to the minimization problem and suggest the numerical inverse algorithm. The restriction of influence space and the lead field matrix reduce the computational cost in this approach. The reconstructed divergence of primary current converges to a reasonable distribution for three dimensional sphere head model.

• 대한의용생체공학회:의공학회지
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• 제27권2호
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• pp.64-72
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• 2006

The present study introduces a new cortical patch-based source model for EEG/MEG cortical source imaging to consider anatomical constraints more precisely. Conventional source models for EEG/MEG cortical source imaging have used coarse cortical surface mesh or sampled small number of vertices from fine surface mesh, and thus they failed to utilize full anatomical information which nowadays we can get with sub-millimeter modeling accuracy. Conventional ones placed a single dipolar source on each cortical patch and estimated its intensity by means of various inverse algorithms; whereas the suggested cortical patch-based model integrates whole cortical area to construct lead field matrix and estimates current density that is assumed to be constant in each cortical patch. We applied the proposed and conventional models to realistic EEG data and compared the results quantitatively. The quantitative comparisons showed that the proposed model can provide more precise spatial descriptions of neuronal source distribution.

• 대한의용생체공학회:의공학회지
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• 제28권2호
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• pp.258-264
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• 2007

Spatiotemporal changes of brain rhythmic activity at a certain frequency have been usually monitored in real time using scalp potential maps of multi-channel electroencephalography(EEG) or magnetic field maps of magnetoencephalography(MEG). In the present study, we investigate if it is possible to implement a real-time brain activity monitoring system which can monitor spatiotemporal changes of cortical rhythmic activity on a subject's cortical surface, neither on a sensor plane nor on a standard brain model, with a high temporal resolution. In the suggested system, a frequency domain inverse operator is preliminarily constructed, considering the individual subject's anatomical information, noise level, and sensor configurations. Spectral current power at each cortical vertex is then calculated for the Fourier transforms of successive sections of continuous data, when a single frequency or particular frequency band is given. An offline study which perfectly simulated the suggested system demonstrates that cortical rhythmic source changes can be monitored at the cortical level with a maximal delay time of about 200 ms, when 18 channel EEG data are analyzed under Pentium4 3.4GHz environment. Two sets of artifact-free, eye closed, resting EEG data acquired from a dementia patient and a normal male subject were used to show the feasibility of the suggested system. Factors influencing the computational delay are investigated and possible applications of the system are discussed as well.

• ETRI Journal
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• 제42권6호
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• pp.932-942
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• 2020

To recover the neural activity from Magnetoencephalography (MEG) and Electroencephalography (EEG) measurements, we need to solve the inverse problem by utilizing the relation between dipole sources and the data generated by dipolar sources. In this study, we propose a new approach based on the implementation of a particle filter (PF) that uses minimum sampling variance resampling methodology to track the neural dipole sources of cerebral activity. We use this approach for the EEG data and demonstrate that it can naturally estimate the sources more precisely than the traditional systematic resampling scheme in PFs.

• 대한의용생체공학회:의공학회지
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• 제15권4호
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• pp.481-488
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• 1994

시각 및 손가락의 전기자극에 의해 머리표면에서 발생하는 유발전위를 검출하여 Source Tracing Method를 이용하여 뇌의 시각인지영역 및 손가락 감각인지영역을 추정하였다. 본 과정에서 유발전위 검출방식은 average method를 이용하였고, 흥분뉴런군에 대한 물리적 모델로 Single Current Dipole Model을 이용하고, 머리기하에 대한 3중구각모델을 이용하여 Forward Problem을 풀었다. Inverse Problem은 current dipole의 6개의 parameter에 대한 Least Square Error Method를 이용하여 신견흥분의 위치를 추정하였다. 이러한 결과와 생리학적으로 밝혀진 시각 및 체성감각 신경로와의 비교결과 유사성이 확인되었다.

• 대한의용생체공학회:의공학회지
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• 제29권4호
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• pp.329-336
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• 2008

Recently, causality analysis of source time series extracted from EEG or MEG signals is becoming of great importance in human brain mapping studies and noninvasive diagnosis of various brain diseases. Two approaches have been widely used for the analyses: one is independent component analysis (ICA), and the other is multiple signal classification (MUSIC). To the best of our knowledge, however, any comparison studies to reveal the difference of the two approaches have not been reported. In the present study, we compared the performance of the two different techniques, ICA and MUSIC, especially focusing on how accurately they can estimate and separate various brain electrical signals such as linear, nonlinear, and chaotic signals without a priori knowledge. Results of the realistic simulation studies, adopting directed transfer function (DTF) and Granger causality (GC) as measures of the accurate extraction of source time series, demonstrated that the MUSIC-based approach is more reliable than the ICA-based approach.